Cited by 342Cancer Immunotherapy and BiomarkersRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

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Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

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Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S.E., Mazzeo, L., Provenzano, L., Kosta, S., Favali, M., Spagnoletti, A., Castelo-Branco, L., Dolezal, J., Pearson, A.T., Lo Russo, G., Proto, C., Ganzinelli, M., Giani, C., Ambrosini, E., Turajlic, S., Au, L., Koopman, M., Delaloge, S., Kather, J.N., de Braud, F., Garassino, M.C., Pentheroudakis, G., Spencer, C., Pedrocchi, A.L.G.·2024·Annals of Oncology

DOI 10.1016/j.annonc.2023.10.125

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Abstract

Background The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.Materials and methods We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.Results A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.Conclusion AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., et al. (2013) · Science Signaling · doi:10.1126/scisignal.2004088 · Cited by 15,160
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
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089
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
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
Self-citation
Support vector machines
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade
Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., Hackl, H., & Trajanoski, Z. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.019 · Cited by 5,041
Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma
Bellmunt, J., de Wit, R., Vaughn, D. J., Fradet, Y., Lee, J.-L., Fong, L., Vogelzang, N. J., Climent, M. A., Petrylak, D. P., Choueiri, T. K., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1613683 · Cited by 3,395
Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study
Burtness, B., Harrington, K. J., Greil, R., Soulières, D., Tahara, M., de Castro, G., Psyrri, A., Basté, N., Neupane, P., Bratland, Å., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32591-7 · Cited by 3,336
Random survival forests
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas169 · Cited by 2,714
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
The false hope of current approaches to explainable artificial intelligence in health care
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458
Self-citation
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421
Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial
Hodi, F. S., Chiarion-Sileni, V., Gonzalez, R., Grob, J.-J., Rutkowski, P., Cowey, C. L., Lao, C. D., Schadendorf, D., Wagstaff, J., Dummer, R., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30700-9 · Cited by 1,399
Multimodal biomedical AI
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
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
Five-Year Outcomes With Pembrolizumab Versus Chemotherapy for Metastatic Non–Small-Cell Lung Cancer With PD-L1 Tumor Proportion Score ≥ 50%
Reck, M., Rodríguez‐Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S. D., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.21.00174 · Cited by 1,219
Saliency, Scale and Image Description
Kadir, T., & Brady, M. (2001) · International Journal of Computer Vision · doi:10.1023/a:1012460413855 · Cited by 1,195
Pembrolizumab versus chemotherapy for microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer (KEYNOTE-177): final analysis of a randomised, open-label, phase 3 study
Diaz, L. A., Shiu, K., Kim, T., Jensen, B. V., Jensen, L. H., Punt, C. J. A., Smith, D. M., García‐Carbonero, R., Benavides, M., Gibbs, P., et al. (2022) · The Lancet Oncology · doi:10.1016/s1470-2045(22)00197-8 · Cited by 993
The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors
Davis, A. A., & Patel, V. G. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0768-9 · Cited by 960
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 933
Machine Learning for Survival Analysis
Wang, P., Li, Y., & Reddy, C. K. A. (2019) · ACM Computing Surveys · doi:10.1145/3214306 · Cited by 858
Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer
Liu, Z., Liu, L., Weng, S., Guo, C., Dang, Q., Xu, H., Wang, L., Lu, T., Zhang, Y., Sun, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28421-6 · Cited by 858
Deep Exploration via Bootstrapped DQNPreprint
Osband, I., Blundell, C., Pritzel, A., & Roy, B. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04621 · Cited by 837
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777
Pan-cancer image-based detection of clinically actionable genetic alterations
Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C. M. L., Echle, A., Muti, H. S., Krause, J., Niehues, J., Sommer, K. A. J., Bankhead, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0087-6 · Cited by 686
Self-citation
Machine learning for integrating data in biology and medicine: principles, practice, and opportunities
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Fu, Y., Jung, A. W., Viñas, R., González, S., Vöhringer, H. S., Shmatko, A., Yates, L. R., Jimenez‐Liñan, M., Moore, L., & Gerstung, M. (2020) · Nature Cancer · doi:10.1038/s43018-020-0085-8 · Cited by 655
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 570
Randomized phase III KEYNOTE-045 trial of pembrolizumab versus paclitaxel, docetaxel, or vinflunine in recurrent advanced urothelial cancer: results of >2 years of follow-up
Fradet, Y., Bellmunt, J., Vaughn, D., Lee, J.-L., Fong, L., Vogelzang, N., Climent, M., Petrylak, D., Choueiri, T., Necchi, A., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz127 · Cited by 425
Pemetrexed plus platinum with or without pembrolizumab in patients with previously untreated metastatic nonsquamous NSCLC: protocol-specified final analysis from KEYNOTE-189
Rodríguez‐Abreu, D., Powell, S. F., Hochmair, M. J., Gadgeel, S. M., Esteban, E., Felip, E., Speranza, G., Angelis, F. D., Dómine, M., Cheng, S. Y.-S., et al. (2021) · Annals of Oncology · doi:10.1016/j.annonc.2021.04.008 · Cited by 421
Self-citation
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
Vanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. D. A. B., Geneslaw, L., Rizvi, H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00416-8 · Cited by 395
The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-based study
Ko, J. J., Xie, W., Kroeger, N., Lee, J.-L., Rini, B. I., Knox, J. J., Bjarnason, G. A., Srinivas, S., Pal, S. K., Yuasa, T., et al. (2015) · The Lancet Oncology · doi:10.1016/s1470-2045(14)71222-7 · Cited by 385
AI in medicine must be explainable
Kundu, S. (2021) · Nature Medicine · doi:10.1038/s41591-021-01461-z · Cited by 318
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
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 293
Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response
Zhang, Z., Wang, Z., Chen, Y., Wu, H., Yin, L., Zhao, Q., Luo, H., Zeng, Z.-L., Qiu, M., & Xu, R. (2022) · Genome Medicine · doi:10.1186/s13073-022-01050-w · Cited by 282
Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer
Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., et al. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.21.02010 · Cited by 280
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Pölsterl, S. (2020) · Journal of Machine Learning Research · Cited by 278
Improved prediction of immune checkpoint blockade efficacy across multiple cancer types
Chowell, D., Yoo, S., Valero, C., Pastore, A., Krishna, C., Lee, M., Hoen, D. R., Shi, H., Kelly, D. W., Patel, N., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01070-8 · Cited by 275
CheckMate 025 Randomized Phase 3 Study: Outcomes by Key Baseline Factors and Prior Therapy for Nivolumab Versus Everolimus in Advanced Renal Cell Carcinoma
Escudier, B. J., Sharma, P., McDermott, D. F., George, S., Hammers, H. J., Srinivas, S., Tykodi, S. S., Sosman, J. A., Procopio, G., Plimack, E. R., et al. (2017) · European Urology · doi:10.1016/j.eururo.2017.02.010 · Cited by 254
The FDA approval of pembrolizumab for adult and pediatric patients with tumor mutational burden (TMB) ≥10: a decision centered on empowering patients and their physicians
Subbiah, V., Solit, D. B., Chan, T. A.-T., & Kurzrock, R. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.07.002 · Cited by 254
Explainability and artificial intelligence in medicine
Reddy, S. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00029-2 · Cited by 246
Network-based machine learning approach to predict immunotherapy response in cancer patients
Kong, J., Ha, D., Lee, J., Kim, I., Park, M., Im, S.-H., Shin, K., & Kim, S. (2022) · Nature Communications · doi:10.1038/s41467-022-31535-6 · Cited by 219
Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images
Mu, W., Jiang, L., Shi, Y., Tunali, I., Gray, J. E., Katsoulakis, E., Tian, J., Gillies, R. J., & Schabath, M. B. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002118 · Cited by 212
Predicting response to immunotherapy in gastric cancer via multi-dimensional analyses of the tumour immune microenvironment
Chen, Y., Jia, K., Sun, Y. L., Zhang, C., Li, Y., Zhang, L., Chen, Z., Zhang, J., Hu, Y., Yuan, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32570-z · Cited by 210
Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker
He, B., Dong, D., She, Y., Zhou, C., Fang, M., Zhu, Y., Zhang, H., Huang, Z., Jiang, T., Tian, J., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000550 · Cited by 194
Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma
Johannet, P. M., Coudray, N., Donnelly, D. M., Jour, G., Illa-Bochaca, I., Xia, Y., Johnson, D. B., Wheless, L., Patrinely, J. R., Nomikou, S., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2415 · Cited by 194
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer
Shamai, G., Livne, A., Polónia, A., Sabo, E., Cretu, A., Bar-Sela, G., & Kimmel, R. (2022) · Nature Communications · doi:10.1038/s41467-022-34275-9 · Cited by 193
Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer
Zhou, M., Zhang, Z., Bao, S., Hou, P. K., Yan, C., Su, J., & Sun, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa047 · Cited by 178
Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics
Dercle, L., Fronheiser, M., Lu, L., Du, S., Hayes, W., Leung, D. K., Roy, A., Wilkerson, J., Guo, P., Fojo, A. T., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2942 · Cited by 177
PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviewsPreprint
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T., mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E. A., Brennan, S., et al. (2020) · doi:10.31222/osf.io/gwdhk_v1 · Cited by 167
Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images
Tian, P., He, B., Mu, W., Liu, K., Liu, L., Zeng, H., Liu, Y.-J., Jiang, L.-L., Zhou, P., Huang, Z., et al. (2021) · Theranostics · doi:10.7150/thno.48027 · Cited by 151
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 144
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients
Wang, Z., Wang, Y., Yang, T., Xing, H., Wang, Y., Gao, L., Guo, X., Xing, B., Wang, Y., & Ma, W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab032 · Cited by 144
Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma
Zhang, N., Zhang, H., Wu, W., Zhou, R., Li, S., Wang, Z., Dai, Z., Zhang, L., Liu, F., Liu, Z., et al. (2022) · Theranostics · doi:10.7150/thno.74281 · Cited by 142
Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy
Zhang, Z., Chen, L., Chen, H., Zhao, J., Li, K., Sun, J., & Zhou, M. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104207 · Cited by 135
Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology
Zeng, Q., Klein, C., Caruso, S., Maillé, P., Laleh, N. G., Sommacale, D., Laurent, A., Amaddeo, G., Gentien, D., Rapinat, A., et al. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.01.018 · Cited by 129
Self-citation
Welcoming new guidelines for AI clinical research
Topol, E. J. (2020) · Nature Medicine · doi:10.1038/s41591-020-1042-x · Cited by 125
Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition
Basler, L., Gabryś, H. S., Hogan, S. A., Pavic, M., Bogowicz, M., Vuong, D., Tanadini‐Lang, S., Forster, R. E., Kudura, K., Huellner, M. W., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-0020 · Cited by 119
Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis
Dercle, L., Zhao, B., Gönen, M., Moskowitz, C. S., Ahmed, F., Beylergil, V., Connors, D. E., Yang, H., Lü, L., Fojo, T., et al. (2022) · JAMA Oncology · doi:10.1001/jamaoncol.2021.6818 · Cited by 108
A CT-based Radiomics Signature is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors
Ligero, M., Garcia-Ruiz, A., Viaplana, C., Villacampa, G., Raciti, M. V., Landa, J., Matos, I., Martin-Liberal, J., Ochoa-de-Olza, M., Hierro, C., et al. (2021) · Radiology · doi:10.1148/radiol.2021200928 · Cited by 107
Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy
Althammer, S., Tan, T. H., Spitzmüller, A., Rognoni, L., Wiestler, T., Herz, T., Widmaier, M., Rebelatto, M. C., Kaplon, H., Damotte, D., et al. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0589-x · Cited by 100
LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade
Shen, R., Postow, M. A., Adamow, M. J., Arora, A., Hannum, M., Maher, C. A., Wong, P., Curran, M. A., Hollmann, T. J., Jia, L., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abf5107 · Cited by 100
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
Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association
Wu, C.-C., Wang, Y., Livingston, J. A., Zhang, J., & Futreal, P. A. (2022) · Nature Communications · doi:10.1038/s41467-021-27651-4 · Cited by 80
A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer.
Yang, Y. A., Yang, J., Shen, L., Chen, J., Xia, L., Ni, B., Ge, L., Wang, Y., & Lü, S. (2021) · PubMed · Cited by 76
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Hu, J., Cui, C., Yang, W., Huang, L., Yu, R., Liu, S., & Kong, Y. (2021) · Translational Oncology · doi:10.1016/j.tranon.2020.100921 · Cited by 75
Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis
Chen, Q., Zhang, L., Mo, X., You, J., Chen, L., Jin, F., Wang, F., Jin, Z., Zhang, B., & Zhang, S. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05509-7 · Cited by 75
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Flügge, T. V., Gaudin, R., Sabatakakis, A., Troeltzsch, D., Heiland, M., van Nistelrooij, N., & Vinayahalingam, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-29204-9 · Cited by 75
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Weber, J. S., Sznol, M., Sullivan, R. J., Blackmon, S. M., Boland, G. M., Kluger, H. M., Halaban, R., Bacchiocchi, A., Ascierto, P. A., Capone, M., et al. (2017) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-17-0412 · Cited by 74
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Park, C., Na, K. J., Choi, H., Ock, C., Ha, S., Kim, M., Park, S., Keam, B., Kim, T. M., Paeng, J. C., et al. (2020) · Theranostics · doi:10.7150/thno.50283 · Cited by 71
A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer
Gong, J., Bao, X., Wang, T., Liu, J., Peng, W., Shi, J., Wu, F., & Gu, Y. (2022) · OncoImmunology · doi:10.1080/2162402x.2022.2028962 · Cited by 71
Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade
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Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab
Baxi, V., Lee, G., Duan, C., Pandya, D., Cohen, D. N., Edwards, R., Chang, H., Li, J., Elliott, H., Pokkalla, H., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01119-2 · Cited by 66
DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
Filipski, K., Scherer, M., Zeiner, K. N., Bucher, A. M., Kleemann, J., Jurmeister, P. S., Hartung, T. I., Meißner, M., Plate, K. H., Fenton, T. R., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002226 · Cited by 64
Artificial intelligence–powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response
Choi, S., Cho, S. I., Ma, M., Park, S., Pereira, S., Aum, B. J., Shin, S., Paeng, K., Yoo, D., Jung, W., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.04.011 · Cited by 62
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Xie, F., Zhang, J. J., Wang, J., Reuben, A., Xu, W., Yi, X., Varn, F. S., Ye, Y., Cheng, J., Yu, M., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1744 · Cited by 61
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Kim, K., Kim, H. S., Kim, J. Y., Jung, H., Sun, J., Ahn, J. S., Ahn, M., Park, K., Lee, S., & Choi, J. K. (2020) · Nature Communications · doi:10.1038/s41467-020-14562-z · Cited by 61
Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma
Harder, N., Schönmeyer, R., Nekolla, K., Meier, A., Brieu, N., Vanegas, M. C., Madonna, G., Capone, M., Botti, G., Ascierto, P. A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-43525-8 · Cited by 59
Dynamic microbiome and metabolome analyses reveal the interaction between gut microbiota and anti‐PD ‐1 based immunotherapy in hepatocellular carcinoma
Wu, H., Zheng, X., Pan, T., Yang, X., Chen, X., Zhang, B., Peng, L., & Xie, C. (2022) · International Journal of Cancer · doi:10.1002/ijc.34118 · Cited by 59
Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy
Sidhom, J.-W., Oliveira, G., Ross-MacDonald, P., Wind-Rotolo, M., Wu, C. J., Pardoll, D. M., & Baras, A. S. (2022) · Science Advances · doi:10.1126/sciadv.abq5089 · Cited by 57
Machine learning reveals a PD-L1–independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context
Wiesweg, M., Mairinger, F. D., Reis, H., Goetz, M., Kollmeier, J., Misch, D., Stephan-Falkenau, S., Mairinger, T., Walter, R. F. H., Hager, T., et al. (2020) · European Journal of Cancer · doi:10.1016/j.ejca.2020.09.015 · Cited by 56
Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer
Tunali, I., Tan, Y. Q., Gray, J. E., Katsoulakis, E., Eschrich, S. A., Saller, J. J., Aerts, H. J. W. L., Boyle, T. A., Qi, J., Güveniş, A., et al. (2021) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkab048 · Cited by 54
DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy
Fang, C., Xu, D. H., Su, J., Dry, J. R., & Linghu, B. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00381-z · Cited by 53
Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options
Liu, Z., Xu, H., Weng, S., Ren, Y., & Han, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.828330 · Cited by 52
Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis
Xu, H., Liu, Z., Weng, S., Dang, Q., Ge, X., Zhang, Y., Ren, Y., Xing, Z., Chen, S., Zhou, Y., et al. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13313 · Cited by 50
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
A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study
Deng, K., Wang, L., Liu, Y., Li, X., Hou, Q., Cao, M., Ng, N. N., Wang, H., Chen, H., Yeom, K. W., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101541 · Cited by 45
Longitudinal plasma proteomic profiling of patients with non-small cell lung cancer undergoing immune checkpoint blockade
Harel, M., Lahav, C., Jacob, E., Dahan, N., Sela, I., Elon, Y., Raveh, S., Yahalom, G., Kamer, I., Zer, A., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004582 · Cited by 43
Predicting cancer immunotherapy response from gut microbiomes using machine learning models
Liang, H., Jo, J., Zhang, Z., MacGibeny, M. A., Han, J., Proctor, D. M., Taylor, M. E., Che, Y., Juneau, P. L., Apolo, A. B., et al. (2022) · Oncotarget · doi:10.18632/oncotarget.28252 · Cited by 42
Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients
Prelaj, A., Galli, E. G., Miskovic, V., Pesenti, M., Viscardi, G., Pedica, B., Mazzeo, L., Bottiglieri, A., Provenzano, L., Spagnoletti, A., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1078822 · Cited by 39
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George, E., Flagg, E., Chang, K., Bai, H. X., Aerts, H. J. W. L., Vallières, M., Reardon, D. A., & Huang, R. Y. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7488 · Cited by 36
Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data
Benzekry, S., Grangeon, M., Karlsen, M., Alexa, M., Bicalho-Frazeto, I., Chaléat, S., Tomasini, P., Barbolosi, D., Barlesi, F., & Greillier, L. (2021) · medRxiv · doi:10.3390/cancers13246210 · Cited by 36
Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes.
Zhang, C., Fonseca, L. D. A. F., Shi, Z.-W., Zhu, C., Dekker, A., Bermejo, I., & Wee, L. (2020) · Methods · doi:10.1016/j.ymeth.2020.11.005 · Cited by 35
Prediction of outcome in patients with non-small cell lung cancer treated with second line PD-1/PDL-1 inhibitors based on clinical parameters: Results from a prospective, single institution study
Rounis, K., Makrakis, D., Papadaki, C., Monastirioti, A. A., Vamvakas, L. N., Kalbakis, K., Gourlia, K., Xanthopoulos, I., Tsamardinos, I., Mavroudis, D., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252537 · Cited by 35
A comparative study between vision transformers and CNNs in digital pathologyPreprint
Deininger, L., Stimpel, B., Yüce, A., Abbasi-Sureshjani, S., Schönenberger, S., Ocampo, P., Korski, K., & Gaire, F. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.00389 · Cited by 35
Clinical decision support algorithm based on machine learning to assess the clinical response to anti–programmed death-1 therapy in patients with non–small-cell lung cancer
Ahn, B., So, J.-W., Synn, C., Kim, T. H., Kim, J. H., Byeon, Y., Kim, Y. S., Heo, S. G., Yang, S., Yun, M. R., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.05.019 · Cited by 32
Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma
Peng, J., Zou, D., 龚五星, Kang, S., & Han, L. (2020) · OncoImmunology · doi:10.1080/2162402x.2020.1734156 · Cited by 31
A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy
Xu, B., Lu, M., Yan, L., Ge, M., Ren, Y., Wang, R., Shu, Y., Hou, L., & Guo, H. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.796647 · Cited by 31
Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy
Prelaj, A., Boeri, M., Robuschi, A., Ferrara, R., Proto, C., Russo, G. L., Galli, G., Toma, A. D., Brambilla, M., Occhipinti, M. A., et al. (2022) · Cancers · doi:10.3390/cancers14020435 · Cited by 31
Self-citation
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He, B., Zhong, Y., Zhu, Y., Deng, J., Fang, M., She, Y., Wang, T., Yang, Y., Sun, X., Belluomini, L., et al. (2022) · Translational Lung Cancer Research · doi:10.21037/tlcr-22-244 · Cited by 29
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Peisen, F., Gerken, A., Hering, A., Brendlin, A., Afat, S., Nikolaou, K., Gatidis, S., Eigentler, T., Amaral, T. M. S., Moltz, J. H., et al. (2022) · Cancers · doi:10.3390/cancers14122992 · Cited by 29
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Abuhelwa, A. Y., Kichenadasse, G., McKinnon, R. A., Rowland, A., Hopkins, A. M., & Sorich, M. J. (2021) · Cancers · doi:10.3390/cancers13092001 · Cited by 28
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Ren, C., Li, J., Zhou, Y., Zhang, S., & Wang, Q. (2022) · Translational Oncology · doi:10.1016/j.tranon.2022.101367 · Cited by 28
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Elkin, R., Oh, J. H., Liu, Y. L., Selenica, P., Weigelt, B., Reis-Filho, J. S., Zamarin, D., Deasy, J. O., Norton, L., Levine, A. J., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00259-9 · Cited by 27
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
Li, T. Z., Xu, K., Gao, R., Tang, Y., Lasko, T. A., Maldonado, F., Sandler, K. L., & Landman, B. A. (2023) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2653911 · Cited by 27
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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
Using machine learning for mortality prediction and risk stratification in atezolizumab‐treated cancer patients: Integrative analysis of eight clinical trials
Wu, Y., Zhu, W., Wang, J., Liu, L., Zhang, W., Wang, Y., Shi, J., Xia, J., Gu, Y., Qian, Q., et al. (2022) · Cancer Medicine · doi:10.1002/cam4.5060 · Cited by 24
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Zheng, K. C., Gao, L., Hao, J., Zou, X., & Hu, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.972227 · Cited by 23
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Muller, M., Hummelink, K., Hurkmans, D., Niemeijer, A., Monkhorst, K., Roder, J., Oliveira, C., Roder, H., Aerts, J., & Smit, E. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-0538 · Cited by 21
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Sun, S., Guo, W., Lv, F., Zhang, G., Wang, J., Li, R., Tan, F., Li, N., Xue, Q., Gao, Y., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.587436 · Cited by 20
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Xu, P., Zhang, C., Wang, J., Wang, P., Gao, Y., Shang, S., Guo, S., Li, X., Zhi, H., & Ning, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab481 · Cited by 19
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Peng, J., Xiao, L., Zou, D., & Han, L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.808378 · Cited by 19
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Lui, T. K.-L., Cheung, K. S., & Leung, W. K. (2022) · Hepatology International · doi:10.1007/s12072-022-10370-3 · Cited by 19
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Wang, Y., Chen, L., Ju, L., Xiao, Y., & Wang, X. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106818 · Cited by 17
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Wang, L., Zhang, H., Pan, C., Yi, J., Cui, X., Li, N., Wang, J., Gao, Z., Wu, D., Chen, J., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.829634 · Cited by 16
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Liu, Z., Xu, H., Weng, S., Guo, C., Dang, Q., Zhang, Y., Ren, Y., Liu, L., Wang, L., Ge, X., et al. (2022) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-022-03279-1 · Cited by 16
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Chen, W., Wang, M., Gao, Q., Yuan, S., Deng, W., Bie, L., Ma, Y., Zhang, C., Li, S., Luo, S., et al. (2022) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-022-03221-5 · Cited by 15
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Chen, R., Wu, W., Liu, T., Zhao, Y., Wang, Y., Zhang, H., Wang, Z., Dai, Z., Zhou, X., Luo, P., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.914001 · Cited by 15
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Hu, X., Wu, L., Yao, Y., Ma, J., Li, X., Shen, H., Liu, L., Dai, H., Wang, W. T., Chu, X., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105075 · Cited by 13
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Stein-O’Brien, G., Le, D. T., Jaffee, E. M., Fertig, E. J., & Zaidi, N. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0277 · Cited by 13
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Li, C., Tian, C., Zeng, Y.-L., Liang, J.-Y., Yang, Q.-F., Gu, F., Hu, Y., & Liu, L. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01184-1 · Cited by 10
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Chen, Y., Miao, S., & Zhao, W. (2021) · PubMed Central · Cited by 8
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See all 342

Authors: Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., Favali, M., Spagnoletti, A., Castelo-Branco, L., Dolezal, J., Pearson, A. T., Lo Russo, G., Proto, C., Ganzinelli, M., Giani, C., Ambrosini, E., Turajlic, S., Au, L., Koopman, M., Delaloge, S., Kather, J. N., de Braud, F., Garassino, M. C., Pentheroudakis, G., Spencer, C., & Pedrocchi, A. L. G.
Affiliations: European Society for Medical Oncology (CH)
Fondazione Politecnico di Milano (IT)
Aalborg University (DK)
Ospedale Policlinico San Martino (IT)
University of Campania "Luigi Vanvitelli" (IT)
Ospedale San Paolo (IT)
Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
Politecnico di Milano (IT)
University of Chicago (US)
The Francis Crick Institute (GB)
Royal Marsden NHS Foundation Trust (GB)
Else Kröner Fresenius Center for Digital Health (DE)
Corresponding author: Prelaj, A.
ORCID: Prelaj, A.
Miskovic, V.
Genova, C.
Viscardi, G.
Rebuzzi, S.E.
Mazzeo, L.
Provenzano, L.
Kosta, S.
Spagnoletti, A.
Dolezal, J.
Pearson, A.T.
Proto, C.
Ganzinelli, M.
Ambrosini, E.
Turajlic, S.
Au, L.
Koopman, M.
Delaloge, S.
Kather, J.N.
Garassino, M.C.
Pentheroudakis, G.
Pedrocchi, A.L.G.
Venue: Annals of Oncology
Publisher: Elsevier BV
Published: 2024-01
Volume: 35
Issue: 1
Pages: 29-65
ISSN: 0923-7534 (ISSN-L)
DOI: 10.1016/j.annonc.2023.10.125
OpenAlex: W4387881611
Cited by: 340
References: 150
Citations by year: 2026: 131 · 2025: 149 · 2024: 60
FWCI: 11.08
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 366 d)
Locations: Annals of Oncology (publishedVersion)
PubMed (publishedVersion)
VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, other-oa)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
Funders: Gilead Sciences
AbbVie
Cancer Research Foundation
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
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
Keywords: artificial intelligence, predictive biomarkers, immune checkpoint inhibitors, cancer immunotherapy, immunotherapy response prediction, radiomics, digital pathology, pathomics, genomics, transcriptomics, epigenomics, multimodal data integration, non-small cell lung cancer, melanoma, precision oncology
MeSH terms: Artificial Intelligence, Carcinoma, Non-Small-Cell Lung, Humans, Lung Neoplasms, Medical Oncology, Prospective Studies

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