A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors

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

Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer

Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335

3D Slicer as an image computing platform for the Quantitative Imaging Network

Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095

Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer

Brahmer, J., Reckamp, K. L., Baas, P., Crinò, L., Eberhardt, W. E. E., Poddubskaya, E., Antonia, S., Pluzanski, A., Vokes, E. E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504627 · Cited by 8,447

Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774

Computational Radiomics System to Decode the Radiographic Phenotype

(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593

Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial

Herbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449

Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma

Motzer, R. J., Escudier, B., McDermott, D. F., George, S., Hammers, H. J., Srinivas, S., Tykodi, S. S., Sosman, J. A., Procopio, G., Plimack, E. R., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1510665 · Cited by 5,807

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

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132

Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.125 · Cited by 340

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang, X., Zhang, Y., Zhang, G., Qiu, X., Tan, W., Yin, X., & Liao, L. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.773840 · Cited by 201

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

An overview and a roadmap for artificial intelligence in hematology and oncology

Rösler, W., Altenbuchinger, M., Baeßler, B., Beißbarth, T., Beutel, G., Bock, R., von Bubnoff, N. C. C., Eckardt, J., Foersch, S., Loeffler, C. M. L., et al. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04667-5 · Cited by 95

A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC

Chen, M., Lu, H., Copley, S. J., Han, Y., Logan, A., Viola, P., Cortellini, A., Pinato, D. J., Power, D., & Aboagye, E. O. (2023) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2023.01.089 · Cited by 77

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., & Compagnon, P. (2022) · Transplant International · doi:10.3389/ti.2022.10640 · Cited by 67

Artificial intelligence-based biomarkers for treatment decisions in oncology

Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 74

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

Cousin, F., Louis, T., Dheur, S., Aboubakar, F., Ghaye, B., Occhipinti, M., Vos, W., Bottari, F., Paulus, A., Sibille, A., et al. (2023) · Cancers · doi:10.3390/cancers15071968 · Cited by 51

Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer

Laleh, N. G., Ligero, M., Pérez-López, R., & Kather, J. N. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-0390 · Cited by 59

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Xu, B., Dong, S.-Y., Bai, X., Song, T., Zhang, B., Zhou, L., Chen, Y., Zeng, Z., Wang, K., Zhao, H., et al. (2022) · Liver Cancer · doi:10.1159/000528034 · Cited by 57

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma

Brendlin, A., Peisen, F., Almansour, H., Afat, S., Eigentler, T., Amaral, T. M. S., Faby, S., Calvarons, A. F., Nikolaou, K., & Othman, A. E. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-003261 · Cited by 47

Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions

Tharmaseelan, H., Rotkopf, L. T., Ayx, I., Hertel, A., Nörenberg, D., Schoenberg, S. O., & Froelich, M. F. (2022) · Scientific Reports · doi:10.1038/s41598-022-22877-8 · Cited by 45

Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction

Bernatowicz, K., Amat, R., Prior, O., Frigola, J., Ligero, M., Grussu, F., Zatse, C., Serna, G., Nuciforo, P. G., Toledo, R. A., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009140 · Cited by 49

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Cho, H.-H., Lee, H. Y., Kim, E., Lee, G., Kim, J., Kwon, J., & Park, H. (2021) · Communications Biology · doi:10.1038/s42003-021-02814-7 · Cited by 40

Robust imaging habitat computation using voxel-wise radiomics features

Bernatowicz, K., Grussu, F., Ligero, M., García-Ruiz, A., Delgado, E., & Pérez-López, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-99701-2 · Cited by 38

Immunotherapy and Cancer: The Multi-Omics Perspective

Donisi, C., Pretta, A., Pusceddu, V., Ziranu, P., Lai, E., Puzzoni, M., Mariani, S., Massa, E., Madeddu, C., & Scartozzi, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063563 · Cited by 41

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

Peng, J., Zou, D., Zhang, X., Ma, H., Han, L.-J., & Yao, B. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04904-6 · Cited by 38

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

Mohammadi, A., Mirza-Aghazadeh-Attari, M., Faeghi, F., Homayoun, H., Abolghasemi, J., Vogl, T., Bureau, N., Bakhshandeh, M., Acharya, R., & Ardakani, A. A. (2022) · Journal of ultrasound in medicine · doi:10.1002/jum.16086 · Cited by 26

Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer’s Disease: A Systematic Review of Studies in the Field

Bevilacqua, R., Barbarossa, F., Fantechi, L., Fornarelli, D., Paci, E., Bolognini, S., Giammarchi, C., Lattanzio, F., Paciaroni, L., Riccardi, G. R., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12165432 · Cited by 33

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

Ligero, M., Gielen, B., Navarro, V., Morgado, P. C., Prior, O., Dienstmann, R., Nuciforo, P. G., Trebeschi, S., Beets‐Tan, R. G. H., Sala, E., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00534-9 · Cited by 30

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study

Yolchuyeva, S., Giacomazzi, E., Tonneau, M., Lamaze, F. C., Orain, M., Coulombe, F., Malo, J., Belkaid, W., Routy, B., Joubert, P., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38076-y · Cited by 28

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

Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence

Kim, N., Lee, E. S., Won, S. E., Yang, M., Lee, A. J., Shin, Y., Ko, Y., Pyo, J., Park, H. J., & Kim, K. W. (2022) · Korean Journal of Radiology · doi:10.3348/kjr.2022.0225 · Cited by 25

Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study

Wang, Y., Zhang, L., Jiang, Y., Cheng, X., He, W., Yu, H., Li, X., Yang, J., Yao, G., Lu, Z., et al. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-24-7 · Cited by 24

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Hellwig, K., Ellmann, S., Eckstein, M., Wiesmueller, M., Rutzner, S., Semrau, S., Frey, B., Gaipl, U. S., Gostian, A., Hartmann, A., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.734872 · Cited by 24

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

ter Maat, L. S., van Duin, I. A. J., Elias, S. G., van Diest, P. J., Pluim, J. P. W., Verhoeff, J. J. C., de Jong, P. A., Leiner, T., Veta, M., & Suijkerbuijk, K. P. M. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.07.034 · Cited by 21

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Liu, Y., Zhang, H., Wang, L., Lin, S., Lu, H., Liang, H., Liang, P., Li, J., Lv, P., & Gao, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.740732 · Cited by 19

Radiomics Biomarkers to Predict Checkpoint Inhibitor Pneumonitis in Non-small Cell Lung Cancer.

Du, Y.-H., Zhang, S., Jia, X.-H., Zhang, X., Li, X.-Q., Pan, L.-B., Li, Z.-H., Niu, G., Liang, T., & Guo, H. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.09.053 · Cited by 22

CT-based radiomics nomogram may predict who can benefit from adaptive radiotherapy in patients with local advanced-NSCLC patients

Chao Hui, Z. H. O. U., Hou, L., Tang, X., Liu, C., Meng, Y., Jia, H., Yang, H., & Zhou, S. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109637 · Cited by 18

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review

Qian, L.-J., Wu, T., Kong, S., Lou, X., Jiang, Y., Tan, Z., Wu, L., & Gao, C. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111314 · Cited by 18

Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics

Qian, L., Shen, Y., Zhang, S., Qin, H., Wang, W., Kan, Y., Liu, L., Li, J., Zhang, H., & Yang, J. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.840777 · Cited by 17

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Qu, H., Zhai, H., Zhang, S., Chen, W., Zhong, H., & Cui, X.-Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.992096 · Cited by 18

CT-based radiomics nomogram for prediction of survival after transarterial chemoembolization with drug-eluting beads in patients with hepatocellular carcinoma and portal vein tumor thrombus

Cheng, S., Hu, G., Jin, Z., Wang, Z., & Xue, H. (2023) · European Radiology · doi:10.1007/s00330-023-09830-7 · Cited by 16

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

Ramlee, S., Hulse, D., Bernatowicz, K., Pérez-López, R., Sala, E., & Aloj, L. (2022) · Cancers · doi:10.3390/cancers14153656 · Cited by 16

Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma

Cui, H., Zeng, L., Li, R., Li, Q., Hong, C., Zhu, H., Chen, L., Liu, L., Zou, X., & Xiao, L. (2022) · Clinical Radiology · doi:10.1016/j.crad.2022.09.113 · Cited by 14

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung

Chen, Z., Yi, L., Peng, Z., Zhou, J., Zhang, Z., Tao, Y., Lin, Z., He, A., Jin, M., & Zuo, M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.949111 · Cited by 13

Predictive biomarkers for immune checkpoint inhibitors therapy in lung cancer

Yao, J., Lin, X., Zhang, X., Xie, M., Ma, X., Bao, X., Song, J., Liang, Y., Wang, Q., & Xue, X. (2024) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2406063 · Cited by 12

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer

Zhu, Z., Chen, M., Hu, G., Pan, Z., Han, W., Tan, W., Zhou, Z., Wang, M., Li, M., Li, X., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09337-7 · Cited by 13

Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer

Wu, A., Wu, C., Zeng, Q.-X., Cao, Y., Shu, X.-F., Luo, L.-H., Feng, Z.-F., Tu, Y., Jie, Z.-G., Zhu, Y.-Y., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-35155-y · Cited by 13

Radiomics Models Derived From Arterial-Phase-Enhanced CT Reliably Predict Both PD-L1 Expression and Immunotherapy Prognosis in Non-small Cell Lung Cancer: A Retrospective, Multicenter Cohort Study

Liu, Z., Yao, Y., Zhao, M., Zhao, Q., Xue, J., Huang, Y., & Qin, S. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.07.028 · Cited by 11

Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model

Ma, Q., Ren, J.-L., Wang, R., Yuan, Y., & Tao, X.-F. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01444-9 · Cited by 11

An Imaging Feature Predicts Efficacy of Atezolizumab Plus Bevacizumab in Unresectable Hepatocellular Carcinoma

Kuwano, A., Yada, M., Miyazaki, Y., TANAKA, K., KOGA, Y., Ohishi, Y., Masumoto, A., & Motomura, K. (2023) · Cancer Diagnosis & Prognosis · doi:10.21873/cdp.10241 · Cited by 9

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study

Yolchuyeva, S., Giacomazzi, E., Tonneau, M., Lamaze, F. C., Orain, M., Coulombe, F., Malo, J., Belkaïd, W., Routy, B., Joubert, P., et al. (2023) · JTO Clinical and Research Reports · doi:10.1016/j.jtocrr.2023.100602 · Cited by 10

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique

Tur, C., Grussu, F., Angelis, F. D., Prados, F., Kanber, B., Calvi, A., Eshaghi, A., Charalambous, T., Cortese, R., Chard, D., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102904 · Cited by 9

Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy

Han, X.-Y., Wang, Y.-J., Jia, X., Zheng, Y.-T., Ding, C., Zhang, X., Zhang, K., Cao, Y.-K., Li, Y.-M., Xia, L., et al. (2023) · Translational Lung Cancer Research · doi:10.21037/tlcr-24-7 · Cited by 7

A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma

Wang, C., Leng, B., You, R., Yu, Z., Ya, L., Diao, L., Jiang, H., Cheng, Y., Yin, G., & Xu, Q. (2024) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s480540 · Cited by 8

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Leung, W., Ng, C. K. C., Lam, S., Wong, P.-T., Ng, K.-Y., Tam, C. H., Lee, T.-C., Chow, K. C., Chow, Y. K., Tam, V. C. W., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13121643 · Cited by 8

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model

Lin, S., Ma, Z., Yao, Y., Huang, H., Chen, W., Tang, D., & Gao, W. (2025) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242972 · Cited by 9

Artificial Intelligence for Drug Discovery: An Update and Future Prospects

Howell, H. J., McGale, J., Choucair, A., Shirini, D., Aide, N., Postow, M. A., Wang, L., Tordjman, M., Lopci, E., Lecler, A., et al. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2025.01.004 · Cited by 7

Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke

Wang, C., Li, T., Jia, Z., Qiu, K., Jiang, R., Hang, Y., Ni, H., Cao, Y., Zhao, L., Li, M., et al. (2023) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2023.107358 · Cited by 7

Radio-immunomics in hepatocellular carcinoma: Unraveling the tumor immune microenvironment

Liu, Q., Zhu, W., Song, F., Lou, T., He, L., Zhou, W., & Feng, Z. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100098 · Cited by 7

Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics

Zheng, S.-J., Zheng, C.-P., Zhai, T., Xu, X., Zheng, Y.-Q., Li, Z., Li, E., Liu, W., & Xu, L. (2022) · Annals of Surgical Oncology · doi:10.1245/s10434-022-13026-6 · Cited by 7

Body Composition and Radiomics From 18F-FDG PET/CT Together Help Predict Prognosis for Patients With Stage IV Non–Small Cell Lung Cancer

Zhang, Y., Tan, W., Zheng, Z., Wang, J., Xing, L., & Sun, X. (2023) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001496 · Cited by 7

Addressing Inter-reconstruction variability in multi-energy myocardial CT Radiomics: The Benefits of combat harmonization

Priya, S., Reutzel, A., Pria, O. A. F. D., Goetz, S., Pham, H. T., Alatoum, A., Aher, P. Y., Narayanasamy, S., Nagpal, P., & Carter, K. D. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111891 · Cited by 6

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Nakajima, E. C., Simpson, A. L., Bogaerts, J., de Vries, E. G. E., Do, R., Garralda, E., Goldmacher, G., Kinahan, P. E., Lambin, P., LeStage, B., et al. (2024) · JCO Precision Oncology · doi:10.1200/po.23.00687 · Cited by 6

CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

Hou, L., Chen, K., Chao Hui, Z. H. O. U., Tang, X., Yu, C., Jia, H., Xu, Q., Zhou, S., & Yang, H. (2024) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2024.100828 · Cited by 6

The Society for Immunotherapy of Cancer Perspective on Liquid Biopsy– and Radiomics-Based Technologies for Immuno-oncology Biomarker Discovery and Application

Monette, A., Aguilar‐Mahecha, A., Altınmakas, E., Angelos, M. G., Assad, N., Batist, G., Bommareddy, P. K., Bonilla, D. L., Borchers, C. H., Church, S., et al. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-3791 · Cited by 7

MRI-derived radiomics model for predicting intratumoral tertiary lymphoid structures in soft tissue sarcoma

Wang, T., Wang, L., Zhong, H., Zong, Y., Hou, F., Wang, H., Zhou, R., Gao, S., Shi, X., Yang, J., et al. (2025) · Insights into Imaging · doi:10.1186/s13244-025-02086-3 · Cited by 5

CT-based radiomics nomogram for differentiating gastric hepatoid adenocarcinoma from gastric adenocarcinoma: a multicentre study

Wang, J., Kang, B., Sun, C., Du, F., Lin, J., Ding, F., Dai, Z., Zhang, Y., Yang, C., Shang, L., et al. (2023) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2023.2166490 · Cited by 5

CT radiomics can predict disease progression within 6 months after chimeric antigen receptor-modified T-cell therapy in relapsed/refractory B-cell non-Hodgkin's lymphoma patients

Huang, R., Geng, H., Zhu, L., Yan, J. Y., Li, C., & Li, Y. (2023) · Clinical Radiology · doi:10.1016/j.crad.2023.05.022 · Cited by 6

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers

Yu, Y., Bai, Y.-P., Zheng, P., Wang, N., Deng, X., Ma, H.-H., Yu, R., Ma, C., Liu, P., Xie, Y., et al. (2022) · BMC Cancer · doi:10.1186/s12885-022-10344-6 · Cited by 5

CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

O'Malley, R. B., Cox, D., Soloff, E. V., Zečević, M., GREEN, S. M., Coveler, A. L., Busey, J. M., & Wang, C. L. (2022) · Abdominal Radiology · doi:10.1007/s00261-022-03638-7 · Cited by 4

PMSG-Net: A priori-guided multilevel graph transformer fusion network for immunotherapy efficacy prediction

Yang, W., Wu, W., Wang, L., Zhang, S., Zhao, J.-J., & Qiang, Y. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107371 · Cited by 4

Pretreatment radiomic imaging features combined with immunological indicators to predict targeted combination immunotherapy response in advanced hepatocellular carcinoma

Zhang, X., Zhang, X., Luo, Q., Fu, Q., Liu, P., Pan, C., Liu, C., Zhang, H., & Qin, T. (2025) · World Journal of Clinical Oncology · doi:10.5306/wjco.v16.i4.102735 · Cited by 3

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics

Liang, M., Wu, S., Ou, B., Wu, J., Qiu, H., Zhao, X., & Luo, B. (2025) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2025.05.006 · Cited by 3

Molecular markers for the efficacy of neoadjuvant immunotherapy for head and neck squamous cell carcinoma

Zhu, J., & Ning, Y. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1586130 · Cited by 4

Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study

Yu, X., Kang, B., Nie, P., Deng, Y., Liu, Z., Mao, N., An, Y., Xu, J., Huang, C., Huang, Y., et al. (2023) · Chinese Medical Journal · doi:10.1097/cm9.0000000000002671 · Cited by 3

Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma

Lu, Z., Li, Y., Hu, W., Cao, Y., Lv, X., Jia, X., Shen, S., Zhao, J., & Xu, C. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1347650 · Cited by 3

MRI-Based Machine Learning and Radiomics Methods for Assessing Spinal Cord Function in Patients with Mild Cervical Spondylotic Myelopathy

He, W., Wang, K., Wang, Y., Liu, Z., Zhang, L., Jia, S., He, K., Zhang, X., & Wu, H. (2025) · Bioengineering · doi:10.3390/bioengineering12060666 · Cited by 3

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Franco, P. N., Vernuccio, F., Maino, C., Cannella, R., Otero-García, M., & Ippolito, D. (2023) · Applied Sciences · doi:10.3390/app132111839 · Cited by 2

Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer

Ma, W.-L., Hou, C.-L., Yang, M.-X., Wei, Y.-G., Mao, J., Guan, L., & Zhao, Z.-H. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1509207 · Cited by 2

Establishing a Survival Time Prediction Model for Patients with Hepatocellular Carcinoma After TACE Based on CT Radiomics: A Multi-Center Study

Yang, H., Zhao, J., Wang, Y., Zhu, D., Junpeng, G., & Ren, W. (2025) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s535606 · Cited by 2

Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging

Watzenboeck, M. L., Beer, L., Kifjak, D., Röhrich, S., Heidinger, B. H., Prayer, F., Milos, R., Apfaltrer, P., Langs, G., Baltzer, P., et al. (2024) · Cancers · doi:10.3390/cancers16081519 · Cited by 2

Predictions of Response in Non-small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Using Clinical Data, Deep Learning, and Radiomics.

Wang, C., Li, Y.-X., Ji, Y., Yu, K., Qin, C., Liu, L., Shuai, Y.-J., Chen, J.-H., Li, A., & Zhang, T. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.09.037 · Cited by 3

Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study

Chen, J., Meng, T., Xu, J., Ooi, J. D., Eggenhuizen, P. J., Liu, W., Li, F., Wu, X., Sun, J., Zhang, H., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1084299 · Cited by 2

Imaging Biomarkers to Assess Response to Immune Checkpoint Inhibitors in Solid Tumors to Tailor Therapy.

Summers, R. (2021) · Radiology · doi:10.1148/radiol.2021204407

Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma

Tanahashi, Y., Ikeda, T., Kubota, K., Kutsuna, M., Kobayashi, T., Funayama, S., Ozaki, K., Ichikawa, S., & Goshima, S. (2025) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2025-0055 · Cited by 1

A radiomics-driven machine learning model for predicting bladder cancer prognosis identifies genes associated with radiomic features

Zou, X. C., Wang, B., Yu, Z., Chao, H., Xu, X. D., & Zeng, T. (2025) · Clinical & Experimental Metastasis · doi:10.1007/s10585-025-10383-w · Cited by 1

Multi-modal gradual fusion transformer-based model for predicting immunotherapy response in patients with hepatocellular carcinoma

Xiao, L., Wang, J., Cui, H., Zhu, H., He, J., Deng, H., Zhang, W., Dong, H., Zhou, Y., JIANG, P. C., et al. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.02.003 · Cited by 1

Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis

Yin, M., Yu, C., Su, Q., Song, X., Zhao, Q., & Zhang, J. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02203-8 · Cited by 1

CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility

Chang, S., Swicklik, J. R., Zhou, Z., Kharat, S., Wellinghoff, J., Gong, H., Williamson, E. E., Foley, T. A., McCollough, C. H., & Leng, S. (2026) · Medical Physics · doi:10.1002/mp.70403 · Cited by 1

Independent Testing of Published CT Models for PD-L1 Status in Non–Small Cell Lung Cancer

O’Shea, R., Horst, C., Manickavasagar, T., Josephides, E., Hunter, S. J., Taniere, P., Nonaka, D., Hemelrijck, M. V., Spicer, J. F., Hughes, D. J., et al. (2026) · Radiology · doi:10.1148/radiol.241962 · Cited by 2

Magnetic resonance imaging radiomic phenotypes stratify response and define immune states in very high‐risk, nonmuscle–invasive bladder cancer treated with tislelizumab plus nanoparticle albumin‐bound paclitaxel

Huang, S. W., Jia, K., Liang, S., Ma, B., Chen, H., Zhang, Z., Kang, N., Wu, Z., Lin, Y., Li, P., et al. (2026) · Cancer · doi:10.1002/cncr.70498 · Cited by 1

Imaging the hallmarks of cancer

Grimm, J., Kiessling, F. M., Brindle, K. M., Hanahan, D., Jain, S. K., Lambin, P., Lammers, T., Li, K. C., de Vries, I. J., Weber, W., et al. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00950-y · Cited by 2

Radiomics for the Detection and Prediction of Cancer Therapy-Related Cardiotoxicity

Kandala, A., Rai, A., Penumaka, R. R., Wilcox, N. S., Lefebvre, B., Bravo, P. E., & Fradley, M. G. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.102942 · Cited by 1

Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images

Behmanesh, B., Saray, A. A., Deevband, M. R., Amoui, M., & Haghighatkhah, H. (2023) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.2112-1444 · Cited by 1

AI-Assisted Immunology for Precision Cancer Medicine

Sah, A. K., Verma, M., Mishra, P. S., Rahaman, S. A., Gupta, T., & Mishra, R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch010 · Cited by 1

Editorial Commentary: Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC

Erasmus, J. J., & Vlahos, I. (2023) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2023.02.012 · Cited by 1

The Role of CT-Based Radiomics in Precise Imaging of Renal Cancer

Ligero, M., Bernatowicz, K., & Pérez-López, R. (2021) · Journal of Nephrological Science · doi:10.29245/2767-5149/2021/2.1111

Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al

Viswanathan, V. S., Khorrami, M., Jazieh, K., Fu, P., Pennell, N. A., & Madabhushi, A. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005086

Deep Learning CT Signature Predicts Benefit from Immunotherapy in Metastatic NSCLC Independent of Standard Clinicopathological MarkersPreprint

Saad, M. B., Hong, L., Aminu, M., Vokes, N. I., Chen, P., Salehjahromi, M., Qin, K., Sujit, S. J., Wu, C. C., Carter, B. W., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4135321

The Utility of Radiomics in Predicting Response to Cancer Immunotherapy

Khalili, N., & Rezaei, N. (2023) · Springer eBooks · doi:10.1007/978-3-030-80962-1_136-1

A nomogram based on radiomics for predicting the high-grade histologic patterns in pure-solid clinical stage IA lung adenocarcinomaPreprint

Zhou, X., Yang, L., Dai, Q., Han, D., Leng, S., & Zhang, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3323194/v1

The Role of CT Radiomics Analysis in Predicting Overall Survival Following initial Chemotherapy for Diffuse Large B-cell LymphomaPreprint

Yin, M., Yu, C., Zhang, J., Su, Q., & Song, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4063556/v1

KI in der Medizin: Begrifflichkeiten, Chancen und Risiken

Widmer, C. (2024) · InFo Hämatologie + Onkologie · doi:10.1007/s15004-024-0567-4

Different MRI-based radiomics machine learning models to predict CD3+ Tumor-infiltrating Lymphocytes (TILs) in rectal cancerPreprint

Ma, W., Hou, C., Yang, M., Wei, Y., Mao, J., Guan, L., & Zhao, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-4622526/v1

Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases

Bodenmann, T. R., Gil, N., Dorfner, F. J., Cleveland, M. C., Patel, J., Brahmavar, S. B., Guelen, M. S., Pulido-Arias, D., Kalpathy–Cramer, J., Thiran, J., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73376-5_4

Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model

Xue, H., Liang, M.-L., Xu, H.-Z., Wang, C.-Y., Xu, T., & Zhao, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1476340

Multicenter Radiomic Feature-Based Prediction of PD-L1 Status in Lung Cancer Using Multimodal PET/CT

Da‐ano, R., Tankyevych, O., Lemée, N., Stamouli, I., Kagadis, G. C., Kalathas, T., Shi, K., Hu, J., Wang, H., Rest, C. C. L., et al. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981025

Integrative AI Model Combining Radiomics and Phenomics to Predict Survival in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy Containing RegimenPreprint

Song, J., Kim, J., Lee, H., Min, R., & Chae, Y. K. (2025) · medRxiv · doi:10.64898/2025.12.03.25341593

Artificial intelligence combining radiomics and clinical data for predicting response to immunotherapy

Ligero, M., García-Ruiz, A., Viaplana, C., Raciti, M. V., Matos, I. G., Liberal, J. M., Hierro, C., González, M., Morales‐Barrera, R., Suárez, C., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz253.002 · Cited by 5

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers

Yu, Y., Bai, Y.-P., Zheng, P., Wang, N., Deng, X., Ma, H.-H., Yu, R., Ma, C., Liu, P., Xie, Y., et al. (2022) · BMC Cancer · doi:10.1186/s12885-022-10344-6 · Cited by 5

Prediction of response to immune checkpoint inhibitors in solid tumours using CT-based biomarkers

Gielen, B. (. (2023) · Research Repository (Delft University of Technology)

CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study

He, S., Feng, Y., Lin, Q., Wang, L.-H. H., Wei, L.-J., Tong, J., Zhang, Y.-W., Liu, Y., Ye, Z.-X., Guo, Y., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.729371 · Cited by 11

Development of a predictive radiomics signature for response to immune checkpoint inhibitors (ICIs) in patients with recurrent or metastatic squamous cell carcinoma of the head and neck (RM-SCCHN)

Prawira, A., Dufort, P. A., Halankar, J., Paravasthu, D. M., Hansen, A. R., Spreafico, A., Razak, A. R. A., Chen, E., Jang, R., Metser, U., et al. (2016) · Annals of Oncology · doi:10.1093/annonc/mdw376.08 · Cited by 6