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Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

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Non-Invasive Tumor Genotyping Using Radiogenomic Biomarkers, a Systematic Review and Oncology-Wide Pathway Analysis

Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesseling, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. H. E., Moll, A. C., Dorsman, J. C., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.24893 · Cited by 63 · see citing works

Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival “early on” in neoadjuvant treatment of breast cancer

Drukker, K., Li, H., Antropova, N., Edwards, A. V., Papaioannou, J., & Giger, M. L. (2018) · Cancer Imaging · doi:10.1186/s40644-018-0145-9 · Cited by 66 · see citing works

Radiogenomics for Precision Medicine With a Big Data Analytics Perspective

Panayides, A. S., Pattichis, M. S., Leandrou, S., Pitris, C., Constantinidou, A., & Pattichis, C. S. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2879381 · Cited by 66 · see citing works

Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation?

Pesapane, F., Suter, M. B., Rotili, A., Penco, S., Nigro, O., Cremonesi, M., Bellomi, M., Jereczek‐Fossa, B. A., Pinotti, G., & Cassano, E. (2020) · Medical Oncology · doi:10.1007/s12032-020-01353-1 · Cited by 67 · see citing works

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

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Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data—a preliminary study

Xia, W., Chen, Y., Zhang, R., Yan, Z., Zhou, X., Zhang, B., & Gao, X. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaa609 · Cited by 63 · see citing works

Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features

Arefan, D., Chai, R., Sun, M., Zuley, M. L., & Wu, S. (2020) · Medical Physics · doi:10.1002/mp.14538 · Cited by 62 · see citing works

Breast Cancer Radiogenomics: Current Status and Future Directions

Grimm, L. J., & Mazurowski, M. A. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.09.012 · Cited by 62 · see citing works

The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

Castaldo, R., Pane, K., Nicolai, E., Salvatore, M., & Franzese, M. (2020) · Cancers · doi:10.3390/cancers12020518 · Cited by 63 · see citing works

Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive

Zhu, Y., Mohamed, A., Lai, S. Y., Yang, S., Kanwar, A., Wei, L., Kamal, M. J., Sengupta, S., Elhalawani, H. M., Skinner, H. D., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00073 · Cited by 58 · see citing works

Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Chen, X., Chen, X., Yang, J., Li, Y., Fan, W., & Yang, Z. (2020) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000000978 · Cited by 58 · see citing works

Artificial intelligence and imaging: Opportunities in cardio-oncology

Madan, N., Lucas, J., Akhter, N., Collier, P. H., Cheng, F., Guha, A., Zhang, L., Sharma, A., Hamid, A. B., Ndiokho, I., et al. (2022) · American Heart Journal Plus Cardiology Research and Practice · doi:10.1016/j.ahjo.2022.100126 · Cited by 57 · see citing works

A deep survival interpretable radiomics model of hepatocellular carcinoma patients

Wei, L., Owen, D., Rosen, B., Guo, X., Cuneo, K. C., Lawrence, T. S., Haken, R. K. T., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.013 · Cited by 55 · see citing works

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Jones, M. A., Islam, W., Faiz, R., Chen, X., & Zheng, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.980793 · Cited by 59 · see citing works

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis—a narrative review

Cè, M., Caloro, E., Pellegrino, M. E., Basile, M., Sorce, A., Fazzini, D., Oliva, G., & Cellina, M. I. (2022) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2022.00113 · Cited by 57 · see citing works

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Monti, S., Aiello, M., Incoronato, M., Grimaldi, A., Moscarino, M., Mirabelli, P., Ferbo, U., Cavaliere, C., & Salvatore, M. (2018) · Contrast Media & Molecular Imaging · doi:10.1155/2018/5076269 · Cited by 51 · see citing works

Cancer heterogeneity and imaging

O’Connor, J. P. B. (2016) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2016.10.001 · Cited by 49 · see citing works

Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients

Fan, M., Xia, P., Liu, B., Zhang, L., Wang, Y., Gao, X., & Li, L.-H. (2019) · Breast Cancer Research · doi:10.1186/s13058-019-1199-8 · Cited by 30 · see citing works

Artificial intelligence in breast imaging: Current situation and clinical challenges

You, C., Shen, Y., Sun, S., Zhou, J., Li, J., Su, G.-H., Michalopoulou, E., Peng, W., Gu, Y., Guo, W., et al. (2023) · Exploration · doi:10.1002/exp.20230007 · Cited by 45 · see citing works

Clinical Artificial Intelligence Applications

Hu, Q., & Giger, M. L. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.07.010 · Cited by 44 · see citing works

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Wang, Y., Ma, L., Yin, X., & Gao, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.689509 · Cited by 44 · see citing works

MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case–control study

Song, S. E., Shin, S. U., Moon, H., Ryu, H. S., Kim, K., & Moon, W. K. (2017) · Breast Cancer Research and Treatment · doi:10.1007/s10549-017-4143-6 · Cited by 42 · see citing works

MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

Leporq, B., Bouhamama, A., Pilleul, F., Lame, F., Bihane, C., Sdika, M., Blay, J., & Beuf, O. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00354-7 · Cited by 43 · see citing works

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis

Lei, M.-X., Varghese, B., Hwang, D., Cen, S., Lei, X., Desai, B., Azadikhah, A., Oberai, A., & Duddalwar, V. (2021) · Journal of digital imaging · doi:10.1007/s10278-021-00506-6 · Cited by 38 · see citing works

Distinct Radiomic Phenotypes Define Glioblastoma TP53-PTEN-EGFR Mutational Landscape

Zinn, P. O., Singh, S. K., Kotrotsou, A., Abrol, S., Thomas, G. A., Mosley, J. C., Elakkad, A., Hassan, I. S. A., Kumar, A. J., & Colen, R. R. (2017) · Neurosurgery · doi:10.1093/neuros/nyx316 · Cited by 41 · see citing works

How does DICOM support big data management? Investigating its use in medical imaging community

Aiello, M., Esposito, G., Pagliari, G., Borrelli, P., Brancato, V., & Salvatore, M. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01081-8 · Cited by 39 · see citing works

Breast Cancer Detected at Screening US: Survival Rates and Clinical-Pathologic and Imaging Factors Associated with Recurrence

Kim, S., Han, B., Kim, E., Choi, W. J., Choi, Y., Kim, H. H., & Moon, W. K. (2017) · Radiology · doi:10.1148/radiol.2017162348 · Cited by 38 · see citing works

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

on behalf of the TCGA group, Sutton, E. J., Huang, E. P., Drukker, K., Burnside, E. S., Li, H., Net, J., Rao, A. U. K., Whitman, G. J., Zuley, M. L., et al. (2017) · European Radiology Experimental · doi:10.1186/s41747-017-0025-2 · Cited by 38 · see citing works

FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer

Ulrich, E. J., Menda, Y., Ponto, L. L. B., Anderson, C. M., Smith, B. J., Sunderland, J. J., Graham, M. M., Buatti, J. M., & Beichel, R. (2019) · Tomography · doi:10.18383/j.tom.2018.00038 · Cited by 37 · see citing works

Radiomics and imaging genomics in precision medicine

Lee, G., Lee, H. Y., Ko, E. S., & Jeong, W. K. (2017) · Precision and Future Medicine · doi:10.23838/pfm.2017.00101 · Cited by 32 · see citing works

Clinical imaging for the prediction of neoadjuvant chemotherapy response in breast cancer

Hayashi, M., Yamamoto, Y., & Iwase, H. (2020) · Chinese Clinical Oncology · doi:10.21037/cco-20-15 · Cited by 32 · see citing works

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Santucci, D., Faiella, E., Cordelli, E., Calabrese, A., Landi, R., de Felice, C., Zobel, B. B., Grasso, R. F., Iannello, G., & Soda, P. (2021) · Cancers · doi:10.3390/cancers13184635 · Cited by 31 · see citing works

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning

Jaberipour, M., Soliman, H., Sahgal, A., & Naini, A. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01024-9 · Cited by 33 · see citing works

Gene expression profiling of calcifications in breast cancer

Shin, S. U., Lee, J. H., Kim, J. H., Kim, W. H., Song, S. E., Chu, A. J., Kim, H. S., Han, W., Ryu, H. S., & Moon, W. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-11331-9 · Cited by 30 · see citing works

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

Capobianco, E., & Dominietto, M. D. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10010015 · Cited by 28 · see citing works

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Naqa, I. M. E., Hu, Q., Chen, W., Li, H., Fuhrman, J. D., Gorre, N., & Giger, M. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.010902 · Cited by 29 · see citing works

MRI as a biomarker for breast cancer diagnosis and prognosis

Galati, F., Rizzo, V., Trimboli, R. M., Kripa, E., Maroncelli, R., & Pediconi, F. (2022) · BJR|Open · doi:10.1259/bjro.20220002 · Cited by 28 · see citing works

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

Kayadibi, Y., Koçak, B., Uçar, N., Akan, Y. N., Akbas, P., & Bektaş, S. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.02.001 · Cited by 27 · see citing works

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Gallivanone, F., Bertoli, G. R., & Porro, D. (2022) · Methods and Protocols · doi:10.3390/mps5050078 · Cited by 27 · see citing works

Machine learning with multimodal data for COVID-19

Chen, W., Sá, R. C., Bai, Y., Napel, S. A., Gevaert, O., Lauderdale, D. S., & Giger, M. L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17934 · Cited by 26 · see citing works

A low-cost, programmable, and multi-functional droplet printing system for low copy number SARS-CoV-2 digital PCR determination

Bu, W., Li, W., Li, J., Ao, T., Li, Z., Wu, B., Wu, S., Kong, W., Pan, T., Ding, Y., et al. (2021) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2021.130678 · Cited by 26 · see citing works

Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Tong, Y., Sun, P., Yong, J., Zhang, H., Huang, Y., Guo, Y., Yu, J., Zhou, S., Wang, Y., Wang, Y., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.682998 · Cited by 24 · see citing works

Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning

Sheng, W., Xia, S., Wang, Y., Yan, L., Ke, S., Mellisa, E., Gong, F., Zheng, Y., & Tang, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.964605 · Cited by 24 · see citing works

Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer

Liu, Q., & Hu, P. (2023) · Biomarker Research · doi:10.1186/s40364-023-00455-y · Cited by 24 · see citing works

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

Ming, W., Zhu, Y., Bai, Y., Gu, W., Li, F., Hu, Z., Xia, T., Dai, Z., Yu, X., Li, H., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.943326 · Cited by 21 · see citing works

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique

Ming, W., Li, F., Zhu, Y., Bai, Y., Gu, W., Liu, Y., Sun, X. S., Liu, X., & Liu, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106147 · Cited by 21 · see citing works

Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

Mohamed, R. M. M., Panthi, B., Adrada, B. E., Böge, M., Candelaria, R. P., Chen, H., Guirguis, M. S., Hunt, K. K., Huo, L., Hwang, K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-66220-9 · Cited by 19 · see citing works

Imaging and the completion of the omics paradigm in breast cancer

Leithner, D., Horvat, J. V., Ochoa‐Albiztegui, R. E., Thakur, S. B., Wengert, G., Morris, E. A., Helbich, T. H., & Pinker, K. (2018) · Der Radiologe · doi:10.1007/s00117-018-0409-1 · Cited by 19 · see citing works

Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma

Miller, T. A., Robinson, K., Li, H., Seiwert, T. Y., Haraf, D. J., Li, L., Giger, M. L., & Ginat, D. T. (2019) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2019.03.08 · Cited by 19 · see citing works

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression

Kong, D., Kim, J., Ryu, G.-H., You, H.-J., Sung, J. K., Han, Y. H., Shin, H.-M., Lee, I., Kim, S.-T., Park, C., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.23975 · Cited by 17 · see citing works

Using deep neural networks for radiogenomic analysis

Smedley, N. F., & Hsu, W. W. Y. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363864 · Cited by 17 · see citing works

MRI-based radiogenomics analysis for predicting genetic alterations in oncogenic signalling pathways in invasive breast carcinoma

Lin, P., LIU, W.-H., Li, X., Wan, D., Qin, H., Li, Q., Chen, G., He, Y., & Yang, H. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.02.011 · Cited by 17 · see citing works

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina, M. I., Cè, M., Khenkina, N., Sinichich, P., Cervelli, M., Poggi, V., Boemi, S., Ierardi, A. M., & Carrafiello, G. (2022) · Technology in Cancer Research & Treatment · doi:10.1177/15330338221141793 · Cited by 16 · see citing works

Integrating genomic and resting State fMRI for efficient autism spectrum disorder classification

Lu, P., Li, X., Hu, L., & Lu, L. J. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10473-9 · Cited by 17 · see citing works

Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype

Net, J., Whitman, G. J., Morris, E. A., Brandt, K. R., Burnside, E. S., Giger, M. L., Ganott, M. A., Sutton, E. J., Zuley, M. L., & Rao, A. U. K. (2018) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2018.08.003 · Cited by 16 · see citing works

Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy

Oriuchi, N., Sugawara, S., & Shiga, T. (2020) · Biomedicines · doi:10.3390/biomedicines8090371 · Cited by 14 · see citing works

The Future of Radiation Oncology in Soft Tissue Sarcoma

Naghavi, A. O., Yang, G. Q., Latifi, K., Gillies, R. J., McLeod, H. L., & Harrison, L. B. (2018) · Cancer Control · doi:10.1177/1073274818815504 · Cited by 14 · see citing works

TU‐CD‐BRB‐06: Deciphering Genomic Underpinnings of Quantitative MRI‐Based Radiomic Phenotypes of Invasive Breast Carcinoma

Zhu, Y., Li, H., Guo, W., Drukker, K., Yang, S., Lan, L., Giger, M. L., Ji, Y., & for the TCIA Breast Cancer Group (2015) · Medical Physics · doi:10.1118/1.4925591 · Cited by 3 · see citing works

Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression

Bismeijer, T., van der Velden, B. H. M., Canisius, S., Lips, E. H., Loo, C. E., Viergever, M. A., Wesseling, J., Gilhuijs, K. G. A., & Wessels, L. F. A. (2020) · Radiology · doi:10.1148/radiol.2020191453 · Cited by 62 · see citing works

Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling

Yeh, A. C., Li, H., Zhu, Y., Zhang, J., Khramtsova, G. F., Drukker, K., Edwards, A. V., McGregor, S. M., Yoshimatsu, T. F., Zheng, Y., et al. (2019) · Cancer Imaging · doi:10.1186/s40644-019-0233-5 · Cited by 72 · see citing works

Radiogenomic Analysis of Breast Cancer Using MRI: A Preliminary Study to Define the Landscape

Yamamoto, S., Maki, D. D., Korn, R. L., & Kuo, M. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.11.7824 · Cited by 174 · see citing works

Abstract 2633: Radiogenomics of breast cancer using DCE-MRI and gene expression profiling

Yeh, A. C., McGregor, S. M., Li, H., Ji, Y., Zhu, Y., Grushko, T. A., Edwards, A. V., Lui, F., Zhang, J., Niu, Q., et al. (2016) · Cancer Research · doi:10.1158/1538-7445.am2016-2633