Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Bera, Kaustav; Braman, Nathaniel; Gupta, Amit; Velcheti, Vamsidhar; Madabhushi, Anant

doi:10.1038/s41571-021-00560-7

Cited by 819Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationAI in cancer detection

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

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Bera, Kaustav, Braman, Nathaniel, Gupta, Amit, Velcheti, Vamsidhar, Madabhushi, Anant·2022·Nature Reviews Clinical Oncology

DOI 10.1038/s41571-021-00560-7PMC PMC9034765

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Abstract

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

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Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Fully convolutional networks for semantic segmentationPreprint
Long, J., Shelhamer, E., & Darrell, T. (2014) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1605.06211 · Cited by 43,601 · see citing works
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947 · see citing works
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles
Santos, A. G., da Rocha, G. O., & de Andrade, J. B. (2019) · Scientific Reports · doi:10.1038/s41598-018-37186-2 · Cited by 8,495 · see citing works
Computational Radiomics System to Decode the Radiographic Phenotype
(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593 · see citing works
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177 · see citing works
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · cited 1× · see citing works
“This knowledge enables the clinician to identify patients with poor prognosis who might be candidates for escalation of many cases their diagnostic performance has been shown to match or even surpass that of human experts in multiple different cancer types 6,7 .”
Result
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works
MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA
Ding, C. H. Q., & PENG, H. (2005) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720005001004 · Cited by 2,775 · see citing works
Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer
Sparano, J. A., Gray, R. J., Makower, D. F., Pritchard, K. I., Albain, K. S., Hayes, D. F., Geyer, C. E., Dees, E. C., Goetz, M. P., Olson, J. A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1804710 · Cited by 2,397 · see citing works
Radiomics: the process and the challenges
Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258 · see citing works
Texture analysis using gray level run lengths
Galloway, M. M. (1975) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(75)80008-6 · Cited by 2,168 · see citing works
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167 · see citing works
A brief introduction to weakly supervised learning
Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899 · see citing works
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works
Liquid biopsy: monitoring cancer-genetics in the blood
Crowley, E. H., Nicolantonio, F. D., Loupakis, F., & Bardelli, A. (2013) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2013.110 · Cited by 1,821 · see citing works
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698 · see citing works
Self-citation
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449 · see citing works
THE PROBABLE ERROR OF A MEAN
Student (1908) · Biometrika · doi:10.1093/biomet/6.1.1 · Cited by 1,363 · see citing works
A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study
Sun, R., Limkin, E. J., Vakalopoulou, M., Dercle, L., Champiat, S., Han, S. R., Verlingue, L., Brandao, D., Lancia, A., Ammari, S., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30413-3 · Cited by 1,236 · see citing works
Deep Learning: A Primer for Radiologists
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017) · RadioGraphics · doi:10.1148/rg.2017170077 · Cited by 1,115 · see citing works
The seed and soil hypothesis revisited—The role of tumor‐stroma interactions in metastasis to different organs
Langley, R. R., & Fidler, I. J. (2011) · International Journal of Cancer · doi:10.1002/ijc.26031 · Cited by 958 · see citing works
Repeatability and Reproducibility of Radiomic Features: A Systematic Review
Traverso, A., Wee, L., Dekker, A. L. A. J., & Gillies, R. J. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.05.053 · Cited by 915 · see citing works
Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
Tumor-infiltrating lymphocytes and prognosis: a pooled individual patient analysis of early-stage triple-negative breast cancers
Loi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P. A., Lacroix-Triki, M., Joensuu, H., Dieci, M. V., Badve, S., Demaria, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.01010 · Cited by 893 · see citing works
Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>
London, A. J. (2019) · Hastings Center Report · doi:10.1002/hast.973 · Cited by 801 · see citing works
Gabor filters as texture discriminator
Fogel, I., & Sagi, D. (1989) · Biological Cybernetics · doi:10.1007/bf00204594 · Cited by 742 · see citing works
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., Liang, C., Tian, J., & Liang, C. (2016) · Radiology · doi:10.1148/radiol.2016152234 · Cited by 741 · see citing works
Targeted therapy for non-small cell lung cancer: current standards and the promise of the future.
(2015) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2218-6751.2014.05.01 · Cited by 716 · see citing works
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzȩbski, S., Févry, T., Katsnelson, J., Kim, E. S., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2945514 · Cited by 695 · see citing works
Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy
Lim, Z.-F., & Ma, P. C. (2019) · Journal of Hematology & Oncology · doi:10.1186/s13045-019-0818-2 · Cited by 639 · see citing works
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T. P., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2495 · Cited by 635 · see citing works
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
Braman, N. M., Etesami, M., Prasanna, P., Dubchuk, C., Gilmore, H. L., Tiwari, P., Plecha, D. M., & Madabhushi, A. (2017) · Breast Cancer Research · doi:10.1186/s13058-017-0846-1 · Cited by 634 · see citing works
Self-citation
Positron Emission Tomography-Computed Tomography Standardized Uptake Values in Clinical Practice and Assessing Response to Therapy
Kinahan, P. E., & Fletcher, J. W. (2010) · Seminars in Ultrasound, CT and MRI · doi:10.1053/j.sult.2010.10.001 · Cited by 631 · see citing works
A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme
Lao, J., Chen, Y., Li, Z.-C., Li, Q., Zhang, J., Liu, J., & Zhai, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-10649-8 · Cited by 630 · see citing works
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579 · see citing works
Ultrathin Air-Stable n-Type Organic Phototransistor Array for Conformal Optoelectronics
Liu, M., Wang, H., Tang, Q., Zhao, X., Tong, Y., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-35062-7 · Cited by 577 · see citing works
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Trebeschi, S., Drago, S. G., Birkbak, N. J., Kurilova, I., Calin, A., Pizzi, A. D., Lalezari, F., Lambregts, D. M. J., Rohaan, M. W., Parmar, C., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz108 · Cited by 567 · see citing works
Meta-analysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy
Houssami, N., Macaskill, P., von Minckwitz, G., Marinovich, M. L., & Mamounas, E. P. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2012.05.023 · Cited by 554 · see citing works
Pseudoprogression and Pseudoresponse: Imaging Challenges in the Assessment of Posttreatment Glioma
da Cruz, L. C. H., Rodríguez, I., Domingues, R. C., Gasparetto, E. L., & Sorensen, A. G. (2011) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2397 · Cited by 551 · see citing works
Rapid Texture Identification
Laws, K. (1980) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.959169 · Cited by 547 · see citing works
Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIAL
Hylton, N. M., Blume, J. D., Bernreuter, W. K., Pisano, E. D., Rosen, M. A., Morris, E. A., Weatherall, P. T., Lehman, C. D., Newstead, G. M., Polin, S. M., et al. (2012) · Radiology · doi:10.1148/radiol.12110748 · Cited by 499 · see citing works
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., Sun, K., Tang, Z., Jiang, H., Li, H., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3190 · Cited by 494 · see citing works
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
Li, H., Zhu, Y., Burnside, E. S., Drukker, K., Hoadley, K. A., Fan, C., Conzen, S. D., Whitman, G. J., Sutton, E. J., Net, J., et al. (2016) · Radiology · doi:10.1148/radiol.2016152110 · Cited by 463 · see citing works
Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study
Kickingereder, P., Isensee, F., Tursunova, I., Petersen, J., Neuberger, U., Bonekamp, D., Brugnara, G., Schell, M., Keßler, T., Foltyn‐Dumitru, M., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30098-1 · Cited by 459 · see citing works
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
Kickingereder, P., Burth, S., Wick, A., GOETZ, M. A., Eidel, O., Schlemmer, H.-P., Maier-Hein, K. H., Wick, W., Bendszus, M., Radbruch, A., et al. (2016) · Radiology · doi:10.1148/radiol.2016160845 · Cited by 449 · see citing works
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning
Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00986-2018 · Cited by 437 · see citing works
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Parmar, C., Leijenaar, R. T. H., Großmann, P., Velazquez, E. R., Bussink, J., Rietveld, D. H. F., Rietbergen, M. M., Haibe‐Kains, B., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep11044 · Cited by 433 · see citing works
Prognostic and predictive biomarkers in breast cancer: Past, present and future
Nicolini, A., Ferrari, P., & Duffy, M. J. (2017) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2017.08.010 · Cited by 430 · see citing works
Texture classification using texture spectrum
Wang, L., & He, D.-C. (1990) · Pattern Recognition · doi:10.1016/0031-3203(90)90135-8 · Cited by 422 · see citing works
Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI
Nie, K., Shi, L., Chen, Q., Hu, X., Jabbour, S. K., Yue, N. J., Niu, T., & Sun, X. (2016) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-15-2997 · Cited by 414 · see citing works
Early 18F-FDG PET for Prediction of Prognosis in Patients with Diffuse Large B-Cell Lymphoma: SUV-Based Assessment Versus Visual Analysis
Lin, C., Itti, E., Haioun, C., Petegnief, Y., Luciani, A., Dupuis, J., Paone, G., Talbot, J., Rahmouni, A., & Meignan, M. (2007) · Journal of Nuclear Medicine · doi:10.2967/jnumed.107.042093 · Cited by 413 · see citing works
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
Orlhac, F., Frouin, F., Nioche, C., Ayache, N., & Buvat, I. (2019) · Radiology · doi:10.1148/radiol.2019182023 · Cited by 413 · see citing works
Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS
Causse, M., Chua, Z. K., Peysakhovich, V., del Campo, N., & Matton, N. (2017) · Scientific Reports · doi:10.1038/s41598-017-05378-x · Cited by 402 · see citing works
Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2) –Positive Breast Cancer
Braman, N. M., Prasanna, P., Whitney, J. R., Singh, S., Beig, N. G., Etesami, M., Bates, ‬. D. B., Gallagher, K. M., Bloch, B., Vulchi, M., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.2561 · Cited by 327 · see citing works
Self-citation
Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
Peng, H., Dong, D., Fang, M.-J., Li, L., Tang, L., Chen, L., Li, W., Mao, Y., Fan, W., Liu, L., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3065 · Cited by 327 · see citing works
Anniversary Paper: History and status of CAD and quantitative image analysis: The role of <i>Medical Physics</i> and AAPM
Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics · doi:10.1118/1.3013555 · Cited by 311 · see citing works
Radiogenomics: bridging imaging and genomics
Bodalal, Z., Trebeschi, S., Nguyen-Kim, T. D. L., Schats, W., & Beets-Tan, R. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02028-w · Cited by 310 · see citing works
Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics–Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer
Yu, Y., Tan, Y., Xie, C., Hu, Q., Ouyang, J., Chen, Y., Gu, Y., Li, A., Lu, N., He, Z., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.28086 · Cited by 300 · see citing works
Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer
Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P. D., Thawani, R., Corredor, G., Alilou, M., Bera, K., Fu, P., et al. (2020) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-19-0476 · Cited by 297 · see citing works
Self-citation
Radiogenomics: What It Is and Why It Is Important
Mazurowski, M. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.04.019 · Cited by 289 · see citing works
Non-invasive decision support for NSCLC treatment using PET/CT radiomics
Mu, W., Jiang, L., Zhang, J., Shi, Y., Gray, J. E., Tunali, I., Gao, C., Sun, Y., Tian, J., Zhao, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19116-x · Cited by 289 · see citing works
Genomic Markers in Prostate Cancer Decision Making
Cucchiara, V., Cooperberg, M. R., Dall’Era, M., Lin, D. W., Montorsi, F., Schalken, J. A., & Evans, C. P. (2017) · European Urology · doi:10.1016/j.eururo.2017.10.036 · Cited by 270 · see citing works
Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118261 · Cited by 263 · see citing works
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Park, J. E., Kim, D., Kim, H., Park, S., Kim, J. Y., Cho, S. J., Shin, J. H., & Kim, J. H. (2019) · European Radiology · doi:10.1007/s00330-019-06360-z · Cited by 261 · see citing works
Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P. (2016) · European Radiology · doi:10.1007/s00330-016-4637-3 · Cited by 261 · see citing works
Self-citation
From tumor biology to clinical PET: A review of positron emission tomography (PET) in oncology
Kubota, K. (2001) · Annals of Nuclear Medicine · doi:10.1007/bf02988499 · Cited by 258 · see citing works
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Park, H., Lim, Y., Ko, E. S., Cho, H.-H., Lee, J. E., Han, B., Ko, E. Y., Choi, J. S., & Park, K. W. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3783 · Cited by 243 · see citing works
Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?
Yang, L., Dong, D., Fang, M., Zhu, Y., Zang, Y., Liu, Z., Zhang, H., Ying, J., Zhao, X., & Tian, J. (2018) · European Radiology · doi:10.1007/s00330-017-5146-8 · Cited by 239 · see citing works
Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC
Coroller, T. P., Agrawal, V., Huynh, E., Narayan, V., Lee, S. W., Mak, R. H., & Aerts, H. J. W. L. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.11.2226 · Cited by 228 · see citing works
The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology
Cagney, D. N., Sul, J., Huang, R. Y., Ligon, K. L., Wen, P. Y., & Alexander, B. M. (2017) · Neuro-Oncology · doi:10.1093/neuonc/nox242 · Cited by 221 · see citing works
Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy
Wu, J., Cao, G., Sun, X., Lee, J., Rubin, D. L., Napel, S. A., Kurian, A. W., Daniel, B. L., & Li, R. (2018) · Radiology · doi:10.1148/radiol.2018172462 · Cited by 216 · see citing works
Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
Aerts, H. J. W. L., Großmann, P., Tan, Y., Oxnard, G. R., Rizvi, N. A., Schwartz, L. H., & Zhao, B. (2016) · Scientific Reports · doi:10.1038/srep33860 · Cited by 213 · see citing works
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning
Kenny, H., Hadjiiski, L. M., Chan, H., Weizer, A. Z., Alva, A. S., Cohan, R. H., Caoili, E. M., Paramagul, C., & Samala, R. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-09315-w · Cited by 208 · see citing works
Erratum to: Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P. (2017) · European Radiology · doi:10.1007/s00330-017-4815-y · Cited by 202 · see citing works
Self-citation
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set
Cain, E. H., Saha, A., Harowicz, M. R., Marks, J. R., Marcom, P. K., & Mazurowski, M. A. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4990-9 · Cited by 202 · see citing works
CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation
Shan, Q., Hu, H.-T., Feng, S., Peng, Z., Chen, S., Zhou, Q., Li, X., Xie, X., Lu, M., Wang, W., et al. (2019) · Cancer Imaging · doi:10.1186/s40644-019-0197-5 · Cited by 175 · see citing works
Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma
Hu, Y., Xie, C., Yang, H., Ho, J. W. K., Wen, J., Han, L., Chiu, K. W.-H., Fu, J., & Vardhanabhuti, V. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.15927 · Cited by 174 · see citing works
Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer
Wang, S., Liu, Z., Rong, Y., Zhou, B., Bai, Y., Wei, W., Wei, W., Wang, M., Guo, Y., & Tian, J. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.10.019 · Cited by 173 · see citing works
Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study
Jiang, Y., Jin, C., Yu, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Hu, Y., Xu, Y., Zhou, Z., et al. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000003778 · Cited by 169 · see citing works
Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer
Jiang, Y., Wang, H., Wu, J., Chen, C., Yuan, Q., Huang, W., Li, T., Xi, S., Hu, Y., Zhou, Z., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.03.295 · Cited by 169 · see citing works
On the calculation of fractal features from images
Chen, S., Keller, J. M., & Crownover, R. M. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.254066 · Cited by 168 · see citing works
Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
Chen, S., Feng, S., Wei, J., Liu, F., Li, B., Li, X., Hou, Y., Gu, D., Tang, M., Xiao, H., et al. (2019) · European Radiology · doi:10.1007/s00330-018-5986-x · Cited by 157 · see citing works
Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC
Dou, T. H., Coroller, T. P., van Griethuysen, J. J. M., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206108 · Cited by 153 · see citing works
Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients
Zheng, B.-H., Liu, L., Zhang, Z.-Z., Shi, J.-Y., Dong, L., Tian, L., Ding, Z., Ji, Y., Rao, S., Zhou, J., et al. (2018) · BMC Cancer · doi:10.1186/s12885-018-5024-z · Cited by 148 · see citing works
Tumour heterogeneity poses a significant challenge to cancer biomarker research
Cyll, K., Ersvær, E., Vlatkovic, L., Pradhan, M., Kildal, W., Kjær, M. A., Kleppe, A., Hveem, T. S., Carlsen, B., Gill, S., et al. (2017) · British Journal of Cancer · doi:10.1038/bjc.2017.171 · Cited by 147 · see citing works
Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings
Shiradkar, R., Ghose, S., Jambor, I., et al. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26178 · Cited by 145 · see citing works
Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma
Beig, N. G., Bera, K., Prasanna, P., Antunes, J. T., Correa, R., Singh, S., Bamashmos, A. S., Ismail, M., Braman, N. M., Verma, R., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2556 · Cited by 145 · see citing works
Self-citation
Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report
Tunali, I., Gray, J. E., Qi, J., Abdalah, M. A., Jeong, D. K., Güveniş, A., Gillies, R. J., & Schabath, M. B. (2019) · Lung Cancer · doi:10.1016/j.lungcan.2019.01.010 · Cited by 143 · see citing works
Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor
Prasanna, P., Tiwari, P., & Madabhushi, A. (2016) · Scientific Reports · doi:10.1038/srep37241 · Cited by 138 · see citing works
Self-citation
The Rise of Radiomics and Implications for Oncologic Management
Verma, V., Simone, C. B., Krishnan, S., Lin, S. H., Yang, J., & Hahn, S. M. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx055 · Cited by 135 · see citing works
CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction
Vaidya, P., Bera, K., Gupta, A., Wang, X., Corredor, G., Fu, P., Beig, N., Prasanna, P., Patil, P. D., Velu, P. D., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30002-9 · Cited by 134 · see citing works
Self-citation
Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset
Ha, R. S., Chin, C., Karcich, J., Liu, M. Z., Chang, P., Mutasa, S., Sant, E. P. V., Wynn, R. T., Connolly, E. P., & Jambawalikar, S. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0144-1 · Cited by 132 · see citing works
The Value of FDG PET/CT in Treatment Response Assessment, Follow-Up, and Surveillance of Lung Cancer
Sheikhbahaei, S., Mena, E., Yanamadala, A., Redy, S., Sólnes, L. B., Wachsmann, J., & Subramaniam, R. M. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.16.16532 · Cited by 131 · see citing works
Hyperprogression under Immunotherapy
Frélaut, M., Tourneau, C. L., & Borcoman, É. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112674 · Cited by 128 · see citing works
Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways
Wu, J., Cui, Y., Sun, X., Cao, G., Li, B., Ikeda, D. M., Kurian, A. W., & Li, R. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-2415 · Cited by 114 · see citing works
Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma
Khorrami, M., Khunger, M., Zagouras, A. A., Patil, P. D., Thawani, R., Bera, K., Rajiah, P. S., Fu, P., Velcheti, V., & Madabhushi, A. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180012 · Cited by 114 · see citing works
Self-citation
Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation
Pernicka, J. S. G., Gagnière, J., Chakraborty, J., Yamashita, R., Nardo, L., Creasy, J. M., Petkovska, I., Do, R. R. K., Bates, ‬. D. B., Paroder, V., et al. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02117-w · Cited by 110 · see citing works
How Widely Is Computer-Aided Detection Used in Screening and Diagnostic Mammography?
Rao, V. M., Levin, D. C., Parker, L., Cavanaugh, B. C., Frangos, A. J., & Sunshine, J. H. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2010.05.019 · Cited by 103 · see citing works
CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk
D’Antonoli, T. A., Farchione, A., Lenkowicz, J., Chiappetta, M., Cicchetti, G., Martino, A., Ottavianelli, A., Manfredi, R. M., Margaritora, S., Bonomo, L., et al. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.05.019 · Cited by 102 · see citing works
Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study
Ismail, M., Hill, V. B., Statsevych, V., Huang, R. Y., Prasanna, P., Correa, R., Singh, G., Bera, K., Beig, N. G., Thawani, R., et al. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5858 · Cited by 101 · see citing works
Self-citation
Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence
Chitalia, R., Rowland, J., McDonald, E. S., Pantalone, L., Cohen, E. A., Gastounioti, A., Feldman, M. D., Schnall, M. D., Conant, E. F., & Despina Kontos, B. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-4067 · Cited by 101 · see citing works
Pseudoprogression and Hyperprogression as New Forms of Response to Immunotherapy
Frélaut, M., du Rusquec, P., Moura, A. D., Tourneau, C. L., & Borcoman, É. (2020) · BioDrugs · doi:10.1007/s40259-020-00425-y · Cited by 99 · see citing works
Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review
Granzier, R. W. Y., van Nijnatten, T. J. A., Woodruff, H. C., Smidt, M. L., & Lobbes, M. B. I. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108736 · Cited by 98 · see citing works
Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade
Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., Khorrami, M., Velcheti, V., & Madabhushi, A. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001343 · Cited by 98 · see citing works
Self-citation
Tumor Response Assessment for Precision Cancer Therapy: Response Evaluation Criteria in Solid Tumors and Beyond
Nishino, M. (2018) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_201441 · Cited by 94 · see citing works
Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI
Zhong, X., Cao, R., Shakeri, S., Scalzo, F., Lee, Y., Enzmann, D. R., Wu, H. H., Raman, S. S., & Sung, K. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1824-5 · Cited by 93 · see citing works
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.009 · Cited by 93 · see citing works
Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment
Kontos, D., Winham, S. J., Oustimov, A., et al. (2018) · Radiology · doi:10.1148/radiol.2018180179 · Cited by 88 · see citing works
Validity of RECIST Version 1.1 for Response Assessment in Metastatic Cancer: A Prospective, Multireader Study
Kuhl, C. K., Alparslan, Y., Schmoee, J., Sequeira, B., Keulers, A., Brümmendorf, T. H., & Keil, S. (2018) · Radiology · doi:10.1148/radiol.2018180648 · Cited by 88 · see citing works
Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
Algohary, A., Shiradkar, R., Pahwa, S. S., et al. (2020) · Cancers · doi:10.3390/cancers12082200 · Cited by 88 · see citing works
An Analysis of Perceptual and Cognitive Factors in Radiographic Interpretation
Carmody, D. P., Nodine, C. F., & Kundel, H. L. (1980) · Perception · doi:10.1068/p090339 · Cited by 87 · see citing works
Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer
Ding, J., Chen, S., Serrano‐Sosa, M., Cattell, R. F., Lei, L., Sun, J., Prasanna, P., Liu, C., & Huang, C. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.10.015 · Cited by 87 · see citing works
RaCaT: An open source and easy to use radiomics calculator tool
Pfaehler, E. A. G., Zwanenburg, A., de Jong, J. R., & Boellaard, R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212223 · Cited by 82 · see citing works
Cognitive asymmetry in dyslexic families
Gordon, H. W. (1980) · Neuropsychologia · doi:10.1016/0028-3932(80)90104-9 · Cited by 80 · see citing works
Radiomic Features of Primary Rectal Cancers on Baseline T2‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study
Antunes, J. T., Ofshteyn, A., Bera, K., Wang, E. Y., Brady, J. T., Willis, J. E., Friedman, K. A., Marderstein, E. L., Kalady, M. F., Stein, S. L., et al. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27140 · Cited by 73 · see citing works
Self-citation
Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer
Fang, M., Kan, Y., Dong, D., Yu, T., Zhao, N., Jiang, W., Zhong, L., Hu, C., Luo, Y., & Tian, J. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00563 · Cited by 72 · see citing works
Targeted therapy in HER2-positive breast cancer
LI, S. G., & Li, L. (2013) · Biomedical Reports · doi:10.3892/br.2013.95 · Cited by 71 · see citing works
Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features
Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P. D., Ahmad, U., Murthy, S. C., Stephans, K. L., Fu, P., et al. (2019) · Lung Cancer · doi:10.1016/j.lungcan.2019.06.020 · Cited by 71 · see citing works
Self-citation
Tumor Size and Computed Tomography Attenuation of Pulmonary Pure Ground-Glass Nodules Are Useful for Predicting Pathological Invasiveness
Eguchi, T., Yoshizawa, A., Kawakami, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097867 · Cited by 67 · see citing works
Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors
Wu, C., Pineda, F., Hormuth, D. A., et al. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27529 · Cited by 66 · see citing works
Electric Field Assisted Self-Healing of Open Circuits with Conductive Particle-Insulating Fluid Dispersions: Optimizing Dispersion Concentration
Parab, V., Prasad, O., Sreelal, S., & Sambandan, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-55801-8 · Cited by 64 · see citing works
Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images
Zhang, Y., Lobo-Mueller, E. M., Karanicolas, P., Gallinger, S., Haider, M. A., & Khalvati, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-80998-y · Cited by 62 · see citing works
A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores
Wan, T., Bloch, B., Plecha, D. M., Thompson, C. L., Gilmore, H. L., Jaffe, C. C., Harris, L. N., & Madabhushi, A. (2016) · Scientific Reports · doi:10.1038/srep21394 · Cited by 58 · see citing works
Self-citation
Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis
Jang, K., Russo, C., & Ieva, A. D. (2020) · Neuroradiology · doi:10.1007/s00234-020-02403-1 · Cited by 58 · see citing works
Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer
Yang, J., Wu, Q., Xu, L., Wang, Z., Su, K., Liu, R., Yen, E. A., Liu, S., Qin, J., Rong, Y., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.06.004 · Cited by 57 · see citing works
An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification
Zhang, Y., Wang, J., Wu, C., Bao, M., Li, H., Wang, X., Tao, J., & Shi, H. (2016) · Oncotarget · doi:10.18632/oncotarget.11293 · Cited by 54 · see citing works
Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer
Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research · doi:10.1186/s13058-018-1039-2 · Cited by 53 · see citing works
Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy
Keek, S., Sanduleanu, S., Wesseling, F. W. R., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232639 · Cited by 52 · see citing works
Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study
Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P. D., Thawani, R., Velu, P., Rajiah, P. S., Alilou, M., Choi, H., et al. (2020) · Lung Cancer · doi:10.1016/j.lungcan.2020.02.018 · Cited by 51 · see citing works
Self-citation
MRI Enhancement in Stromal Tissue Surrounding Breast Tumors: Association with Recurrence Free Survival following Neoadjuvant Chemotherapy
Jones, E. F., Sinha, S. P., Newitt, D. C., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061969 · Cited by 50 · see citing works
Integrating deep and radiomics features in cancer bioimaging
Bizzego, A., Bussola, N., Salvalai, D., Chierici, M., Maggio, V., Jurman, G., & Furlanello, C. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791473 · Cited by 50 · see citing works
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma
Rao, A. U. K., Rao, G., Gutman, D. A., Flanders, A. E., Hwang, S. N., Rubin, D. L., Colen, R. R., Zinn, P. O., Jain, R., Wintermark, M., et al. (2015) · Journal of neurosurgery · doi:10.3171/2015.4.jns142732 · Cited by 49 · see citing works
Tumor volume reduction rate is superior to RECIST for predicting the pathological response of rectal cancer treated with neoadjuvant chemoradiation: Results from a prospective study
Xiao, J., Tan, Y., Li, W., et al. (2015) · Oncology Letters · doi:10.3892/ol.2015.3101 · Cited by 46 · see citing works
Regulation of lymphocyte production in the bone marrow. I. Turnover of small lymphocytes in mice depleted of B lymphocytes by treatment with anti-IgM antibodies.
Fulop, G. M., Gordon, J., & Osmond, D. G. (1983) · The Journal of Immunology · doi:10.4049/jimmunol.130.2.644 · Cited by 45 · see citing works
Blood Vessel Morphologic Changes Depicted with MR Angiography during Treatment of Brain Metastases: A Feasibility Study
Bullitt, E., Lin, N. U., Smith, J. K., et al. (2007) · Radiology · doi:10.1148/radiol.2453061889 · Cited by 45 · see citing works
Abnormal Vessel Tortuosity as a Marker of Treatment Response of Malignant Gliomas: Preliminary Report
Bullitt, E., Ewend, M. G., Aylward, S., et al. (2004) · Technology in Cancer Research & Treatment · doi:10.1177/153303460400300607 · Cited by 42 · see citing works
FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients
Pavic, M., Bogowicz, M., Kraft, J., et al. (2020) · EJNMMI Research · doi:10.1186/s13550-020-00669-3 · Cited by 42 · see citing works
Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival
Negreros-Osuna, A. A., Parakh, A., Corcoran, R. B., Pourvaziri, A., Kambadakone, A. R., Ryan, D. P., & Sahani, D. V. (2020) · Radiology Imaging Cancer · doi:10.1148/rycan.2020190084 · Cited by 40 · see citing works
Radiogenomics Monitoring in Breast Cancer Identifies Metabolism and Immune Checkpoints as Early Actionable Mechanisms of Resistance to Anti-angiogenic Treatment
Mehta, S., Hughes, N. P., Li, S., Jubb, A. M., Adams, R. F., Lord, S., Koumakis, L., van Stiphout, R. G. P. M., Padhani, A. R., Makris, A., et al. (2016) · EBioMedicine · doi:10.1016/j.ebiom.2016.07.017 · Cited by 39 · see citing works
Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma
Decazes, P., Becker, S., Toledano, M. N., Véra, P., Desbordes, P., Jardin, F., Tilly, H., & Gardin, I. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4041-0 · Cited by 39 · see citing works
Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI
Chirra, P. V., Leo, P., Yim, M. C., Bloch, B., Rastinehad, A. R., Purysko, A. S., Rosen, M. A., Madabhushi, A., & Viswanath, S. E. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024502 · Cited by 39 · see citing works
Self-citation
Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas
Alilou, M., Orooji, M., Beig, N. G., Prasanna, P., Rajiah, P. S., Donatelli, C., Velcheti, V., Rakshit, S., Yang, M., Jacono, F. J., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-33473-0 · Cited by 38 · see citing works
Self-citation
Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
Li, J., Zhang, C., Wei, J., Zheng, P., Zhang, H., Xie, Y., Bai, J., Zhu, Z., Zhou, K., Liang, X., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.552270 · Cited by 38 · see citing works
Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes
Urata, M., Kijima, Y., Hirata, M., et al. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-730 · Cited by 34 · see citing works
CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers
Liu, S., Liu, S., Shi, H., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30352-6 · Cited by 27 · see citing works
Association of distant recurrence‐free survival with algorithmically extracted MRI characteristics in breast cancer
Mazurowski, M. A., Saha, A., Harowicz, M. R., et al. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26648 · Cited by 27 · see citing works
Application of computed tomography‑based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first‑line chemotherapy
Wei, H., Yang, F., Liu, Z., Sun, S., Xu, F., Liu, P., Li, H., Liu, Q., Xu, Q., & Wang, X. (2019) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2019.7357 · Cited by 27 · see citing works
Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study
Prasanna, P., Mitra, J., Beig, N. G., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37615-2 · Cited by 25 · see citing works
Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer
Creasy, J. M., Cunanan, K. M., Chakraborty, J., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-020-09134-w · Cited by 25 · see citing works
Computerized Detection Of Lung Nodules In Digital Chest Radiographs
Giger, M. L., Doi, K., MacMahon, H., Dwyer, S. J., & Schneider, R. H. (1987) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.967022 · Cited by 23 · see citing works
A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks
Tadayyon, H., Gangeh, M. J., Sannachi, L., Trudeau, M., Pritchard, K. I., Ghandi, S., Eisen, A. F., Look-Hong, N., Holloway, C. M. B., Wright, F. C., et al. (2019) · Oncotarget · doi:10.18632/oncotarget.26996 · Cited by 21 · see citing works
CT Hounsfield Unit Is a Good Predictor of Growth in Meningiomas
Nakasu, S., Onishi, T., Kitahara, S., et al. (2019) · Neurologia medico-chirurgica · doi:10.2176/nmc.oa.2018-0209 · Cited by 19 · see citing works
Prognostic Significance of CT-Attenuation of Tumor-Adjacent Breast Adipose Tissue in Breast Cancer Patients with Surgical Resection
Lee, J. W., Kim, S. Y., Lee, H. J., et al. (2019) · Cancers · doi:10.3390/cancers11081135 · Cited by 17 · see citing works
The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer
Kamran, S. C., Coroller, T. P., Milani, N., et al. (2020) · Radiation Oncology · doi:10.1186/s13014-020-1460-4 · Cited by 16 · see citing works
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Braman, N. M., Adoui, M. E., Vulchi, M., Turk, P., Etesami, M., Fu, P., Bera, K., Drisis, S., Varadan, V., Plecha, D. M., et al. (2020) · Repository of the University of Namur · doi:10.48550/arxiv.2001.08570 · Cited by 15 · see citing works
Self-citation
Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings
Ghose, S., Shiradkar, R., Rusu, M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13443-8 · Cited by 13 · see citing works
Response Estimation Through Spatially Oriented Neural Network and Texture Ensemble (RESONATE)
Eben, J. E., Braman, N. M., & Madabhushi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_66 · Cited by 10 · see citing works
Self-citation
Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images
Wang, P., Guo, P., Lu, J., Zhou, J., Jiang, S., & Patel, V. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59713-9_1 · Cited by 10 · see citing works
Long short-term memory networks predict breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy
Drukker, K., Edwards, A. V., Papaioannou, J., et al. (2020) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2549044 · Cited by 9 · see citing works
The Perception of Radiologic Image Information Report of an NCI Workshop on April 15-16, 1985
Kundel, H. L., & Hendee, W. R. (1985) · Investigative Radiology · doi:10.1097/00004424-198511000-00019 · Cited by 7 · see citing works
Primary vs nodal site PET / CT response as a prognostic marker in oropharyngeal squamous cell carcinoma treated with intensity‐modulated radiation therapy
Eckstein, J. M., Nolan, N., Healy, E. H., et al. (2020) · Head & Neck · doi:10.1002/hed.26242 · Cited by 5 · see citing works
Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation
Wang, C. J., Rost, N. S., & Golland, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59713-9_72 · Cited by 5 · see citing works
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT ImagesPreprint
Zhang, Y., Lobo-Mueller, E. M., Karanicolas, P. J., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04822 · Cited by 4 · see citing works
[Aspergillosis of the maxillary sinus].
Parabita, G. F., Viva, E., Troletti, G. D., et al. (1985) · PubMed · Cited by 2 · see citing works
Radiomics risk score (RRS) on CT to predict survival and response to CDK 4/6 inhibitors in hormone receptor (HR) positive metastatic breast cancer (MBC).
Kunte, S., Braman, N. M., Bera, K., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.2020.38.15_suppl.e13041 · Cited by 2 · see citing works
Self-citation
STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI
Antunes, J. T., Wei, Z., Alvarez‐Jimenez, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_67 · Cited by 1 · see citing works
Robust and Discriminative Brain Genome Association Study
Zhu, X., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_50 · Cited by 1 · see citing works

Flagged first, then by citations

Research works printed but not linked: 8

[11]FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (FDA, 2016). 27010052
[29]Eben J, Braman N & Madabhushi A in Medical Image Computing and Computer Assisted Intervention Vol. 11767 (eds Shen D et al.) 602– 610 (Springer, 2019).
[57]Antunes J in Medical Image Computing and Computer Assisted Intervention Vol. 11767 (eds Shen D et al.) 611– 619 (Springer, 2019).
[78]Braman N, Prasanna P, Alilou M, Beig N & Madabhushi A in Medical Image Computing and Computer Assisted Intervention Vol. 11071 (eds Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C& Fichtinger G) 803– 811 (Springer, 2018).
[87]Zhou T in Medical Image Computing and Computer Assisted Intervention Vol. 12262 (eds Martel AL et al.) 221– 231 (Springer, 2020).
[99]Drukker K, Edwards A, Papaioannou J & Giger M Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy. Proc. SPIE 11314, 1131410 (2020).
[133]Yang J in Medical Image Computing and Computer Assisted Intervention Vol. 12262 (eds Martel AL et al.) 211– 220 (2020).
[160]Institute of Medicine. Policy Issues in the Development of Personalized Medicine in Oncology: Workshop Summary (National Academies, 2010). 25032365

Sorted by citations

Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913 · see citing works
“MedSAM, as the inaugural foundation model in medical image segmentation, holds great potential to accelerate the advancement of new diagnostic and therapeutic tools, and ultimately contribute to improved patient care [11].”
Background
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446 · see citing works
Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study
Saad, M. B., Hong, L., Aminu, M., Vokes, N. I., Chen, P., Salehjahromi, M., Qin, K., Sujit, S. J., Lu, X., Young, E., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00082-1 · Cited by 121 · see citing works
Self-citation
Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine
Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., Borgheresi, R., Neri, E., Salvatore, M., & Aiello, M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04891-8 · Cited by 106 · see citing works
Artificial intelligence in medicine: A comprehensive survey of medical doctor’s perspectives in Portugal
Pedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74 · see citing works
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74 · see citing works
Segment Anything in Medical Images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2023) · arXiv · doi:10.48550/arxiv.2304.12306 · Cited by 67 · see citing works
A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging
Yao, L., Zhang, Z., Keles, E., Yazici, C., Tirkes, T., & Bagci, U. (2023) · Current Opinion in Gastroenterology · doi:10.1097/mog.0000000000000966 · Cited by 53 · see citing works
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
Reimagining cancer treatments in the era of generative AI
Derbal, Y. (2025) · DIGITAL HEALTH · doi:10.1177/20552076251394631 · Cited by 4 · see citing works

Authors: Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A.
Affiliations: University Hospitals Cleveland Medical Center (US)
Tempus Labs (United States) (US)
NYU Langone Health (US)
Louis Stokes Cleveland VA Medical Center (US)
Corresponding author: Madabhushi, Anant
ORCID: Bera, Kaustav
Braman, Nathaniel
Gupta, Amit
Velcheti, Vamsidhar
Madabhushi, Anant
Venue: Nature Reviews Clinical Oncology
Publisher: Springer Science and Business Media LLC
Published: 2021-10-18
Volume: 19
Issue: 2
Pages: 132-146
ISSN: 1759-4774 (ISSN-L)
1759-4782
DOI: 10.1038/s41571-021-00560-7
PMC: PMC9034765
OpenAlex: W3205076722
Cited by: 819
References: 179
Citations by year: 2026: 155 · 2025: 259 · 2024: 212 · 2023: 130 · 2022: 59 · 2021: 4
FWCI: 16.58
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Reviews Clinical Oncology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. Department of Veterans Affairs (IK6 BX006185, I01 BX004121)
National Institutes of Health (5u54ca254566-04, UL1TR002548)
National Heart, Lung, and Blood Institute (R01HL158071, R01HL151277)
National Cancer Institute (U01CA248226, U54CA254566, U24CA199374, F31CA221383, R01CA216579, U01CA239055, R01CA208236, R01CA202752, R01CA220581, R01CA257612, R01CA249992)
National Institute of Biomedical Imaging and Bioengineering (R43EB028736, R43EB015199, T32EB007509)
National Center for Research Resources (C06RR012463)
National Center for Advancing Translational Sciences
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: radiomics, artificial intelligence, tumor imaging, cancer outcome prediction, treatment response prediction, deep learning, clinical decision support, tumor molecular profiling, interpretability, data annotation, clinical adoption
MeSH terms: Artificial Intelligence, Humans, Neoplasms, Radiometry, Treatment Outcome

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