The DOI matches its Crossref record; 145 of the 150 references it cites were found in a registry or an index.
Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The most active line of work is multimodal deep learning (MDL), which integrates histopathology (“pathomics”), radiology (“radiomics”), genomics, and clinical records to create unified patient-level representations [70,71,72] (Figure 4).”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
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
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
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
Cited by
Sorted by citations
Items 301–342 of 342
Luo, W., Liu, R., Yang, X., Si, W., Qu, J., Xu, L., Chen, Y., Song, N., Wang, J., Cheng, Y., et al. (2026) · iScience · doi:10.1016/j.isci.2026.116459
Yang, S. M., Song, Z., Ji, S., Li, Q., Qin, D., Li, Y. W., Wang, X., & Yao, C. (2026) · Interdisciplinary Nursing Research · doi:10.1097/nr9.0000000000000122
Woodford, R. G., Lord, S., Lin, F. P. Y., Papenfuss, A. T., Marschner, I., Thomas, D., Friedlander, M. L., Scott, C. L., Simes, R. J., & Lee, C. K. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-26-00014
Advances in precision brain tumor care: From intelligent diagnostics to transformative therapies
Choudhary, N., Faiyazuddin, M., Singh, S., Kaur, A., Kumar, D., & Webster, T. J. (2026) · Advances in Cancer Biology - Metastasis · doi:10.1016/j.adcanc.2026.100198
Wang, Z., Zhang, Y., He, C., Wang, M., & Cai, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1863080
Epigenetic-targeted nanomedicine in cancer: from molecular mechanisms to therapeutic revolution
Wang, B., Han, Y., Zhang, X., Li, Y., Cai, Z., Duan, Z., Gong, Q. H., Jiang, Y., & Luo, K. (2026) · Progress in Materials Science · doi:10.1016/j.pmatsci.2026.101789
Gupta, M., & Sarkar, A. P. (2026) · Research Square · doi:10.21203/rs.3.rs-10465895/v1
Xv, Y., Liu, H., Wei, Z., Xiao, B., Yuan, Z., Jiang, Q., Zhang, X., Li, F., Chen, Y., Qiu, M., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10338322/v1
Shen, Q., & Gao, Y. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1900900
Ma, C., Zhuang, Y., Han, S., Li, S., & Dai, Q. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1863734
Kang, J., Tang, R., Li, D., Ma, L., & Zhang, Z. (2026) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2026.2707688
Fu, X., Guo, T., Zhang, M., Xia, J., Zhang, H., Jiang, X., Fang, J., Chen, X., Ren, W., She, Q., et al. (2026) · Digital Health · doi:10.1177/20552076261474097
Gite, V. S., Nikam, R. N., Pache, M. M., Wankhede, P. B., Pawale, S. S., & Darekar, A. B. (2026) · Asian Journal of Pharmaceutical Research · doi:10.52711/2231-5691.2026.00044
Wang, D., Zhang, C., Jiang, T., Liu, J., & Cheng, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-61417-6
Emerging Biomarkers in Cancer from Discovery to Clinical Applications
Sahani, P., Tiwari, S. K., Ojha, A., Gore, M. M., & Mishra, S. K. (2026) · Springer eBooks · doi:10.1007/978-981-92-0477-9_4
Short-term, Long-term, and Genetic Determinants of Human Plasma Proteome VariabilityPreprint
Benacom, D., Specht, A., Nicholas, J. C., Guillard, R., Gillman, M. G., Dubin, R. F., Ganz, P., Rotter, J. I., Taylor, K. D., Rich, S. S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.30.741635
Editorial: AI-powered integrative multi-omics for precision cancer therapies
Tang, Q., Xu, J., & Dao, F. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1944930
Li, X., Su, H., Li, Y., Jiang, L., Zhou, J., Zhang, L., Huang, Y., Yang, X., & Li, M. (2026) · Drug Delivery · doi:10.1080/10717544.2026.2713415
AI-based strategies for diagnosis and therapeutic discovery in pulmonary fibrosis
Lin, Y., Liu, Q., Lei, X., Cao, H., Feng, P., Wang, Y., Sun, D., Xin, G., Zhang, S., & Chen, W. (2026) · Chinese Herbal Medicines · doi:10.1016/j.chmed.2026.08.005
Shi, Y., Li, T., Li, X., & Wang, F. (2026) · Tissue and Cell · doi:10.1016/j.tice.2026.103896
Shaik, N. S., Ramamurthy, S., Balya, H., Pandiyan, B., Govindaraj, S., & Palani, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43824-0.00012-2
Ghais, A., Rizkallah, J., Wehbeh, B. E. D., Diab, Y., Awada, A., Atwi, M., Ayoub, R., Mina, C., Rammal, F., badr, A., et al. (2026) · Cancers · doi:10.3390/cancers18172829
Zhang, Q., Qin, Z., & Guo, C. (2026) · MedComm – Oncology · doi:10.1002/mog2.70097
Anwer, M., Bhaliya, K., & Wei, M. Q. (2026) · Translational Oncology · doi:10.1016/j.tranon.2026.103006
Cui, H., Piao, Z., & Zhang, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1954076
Litkowska, A., Wojtas, J., Nadulska, K., Kurec, G., Burdan, O., & Krawczyk, P. A. (2026) · Genes · doi:10.3390/genes17091076
Yang, D., Zheng, Z., Wang, H., Zhao, H., Li, Z., Liu, Y., Xiong, Y., & Luo, Z. (2026) · Bioactive Materials · doi:10.1016/j.bioactmat.2026.08.060
Hao, W., Liu, Y., Cheng, H., & Hou, M. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1894870
Chen, S., Zhang, E., Sun, F., Gao, F., Huang, D., Wang, D., Na, R., Jiang, L., & Zhang, N. (2026) · Cancers · doi:10.3390/cancers18182948
Therapeutic Resistance in Melanoma: Molecular Mechanisms and Emerging Pharmaceutical Strategies
Moussallem, N., Awada, A., Darzi, R. E., Tarhini, A., Khaled, A., Ashy, C., Nasr, W., Masri, A. E., Saad, G., Itani, M. H., et al. (2026) · Pharmaceuticals · doi:10.3390/ph19091455 · Cited by 1
Application of artificial intelligence in hepatology
Hu, Z., Lin, Y., Gao, H., Shuai, L., Tong, Z., Nie, C., Yang, Z., & Zhao, J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1851723
Mruk, B., Kusyk, D., Górna, I., Kowalówka, M., Blask‐Osipa, A., & Drzymała-Czyż, S. (2026) · Applied Sciences · doi:10.3390/app16189311
Wang, Z.-Y., Liu, N., & Chen, M. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1875788
Zhao, C., Zhang, Z., Zhou, K., Wang, D., Zhu, H., & Tsai, H.-I. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105629
Yu, S., Gu, X., Gao, Y., Zhao, F., & Zhang, J. (2026) · Integrated Diagnostics · doi:10.1016/j.intdia.2026.100006
Шкатула, П. Ю., & Moskalenko, Y. (2026) · Bulletin of Problems Biology and Medicine · doi:10.29254/2077-4214-2026-3-182-354-360
Yue, S., Li, S., Zhao, L., 李亚丽, Cai, F., Wang, K., Xu, N., Liu, B., Zhou, F., Cui, Y., et al. (2026) · International Journal of Genomics · doi:10.1155/ijog/4944325
CT Radiomics for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer
Liu, M., & Song, J. (2026) · International Conference on Biomedical Engineering and Bioinformatics for Health (ICBEBH) · doi:10.1109/icbebh71590.2026.11722498
Advances in Single-Cell and Spatial Omics for Glioblastoma Precision Medicine
Prakash, L., & Rahman, R. (2026) · Current Oncology Reports · doi:10.1007/s11912-026-01847-7
Xie, M., Chang, H., Zhao, X., Huang, J., Tang, H., Gopinath, D., Xiao, X., & Wang, C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1986038
Raheem, K. Y., Yakasiri, S. S., & Guda, C. (2026) · Neoplasia · doi:10.1016/j.neo.2026.101385
Ye, R., Wang, S., Li, Y., & He, Q. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45621
Metadata
- 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
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 35
- Issue
- 1
- Pages
- 29-65
- ISSN
- 0923-7534 (ISSN-L)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
