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

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 access

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

Open this paper in CoChat — free account Open PDF

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.

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

Crossref · 10.1016/j.annonc.2023.10.125

Cites

145 of 145 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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

Show all 150 references

Sorted by citations

Items 201–300 of 342

Machine learning for immune biomarkers in severe mental illness: a systematic review
Slatni, R., Colombo, F., Enrico, P., Branchi, I., Picker, L. D., & Vai, B. (2026) · Neuroscience Applied · doi:10.1016/j.nsa.2026.107003 · Cited by 1
A review on artificial intelligence in pharmaceutical sciences: opportunities and challenges
Kumar, V., Kumar, U., Kumar, R., Tiwari, N., & Gupta, J. K. (2026) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2026.2670435 · Cited by 2
The expanding role of artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging
Ramezani, A., Bagheri, M., Mahmoudian, F., Fattahi, F., ghahroodi, F. N., Nasirmoghadas, P., Pourzardosht, N., Khalesi, B., Hashemi, Z. S., & Khalili, S. (2026) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2026.2706000 · Cited by 1
Therapeutic potential and mechanisms of rehabilitation interventions in preventing cancer metastasis
Chen, X., Zhang, J., Liu, X., & Du, H. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1740636 · Cited by 1
Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC
Prelaj, A., Miskovic, V., Sacco, M., Ferrarin, A., Licciardello, C. M., Provenzano, L., Favali, M., Lerma, L., Zec, A., Spagnoletti, A., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04488-2 · Cited by 2
Self-citation
Immunohistochemistry for prediction of response to immunotherapy
Haragan, A. (2024) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2024.08.002 · Cited by 1
Application of artificial intelligence in immuno-oncology
Monfort‐Lanzas, P., Gronauer, R., Balaz, M., Floriani, G., & Hackl, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14064-8.00017-5 · Cited by 1
Performance comparison of multiple neural networks for brain tumor classification
Wei, M. (2024) · International Conference on Biomedical Engineering and Applications (ICBEA) · doi:10.1109/icbea62825.2024.00012 · Cited by 1
Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis
Pang, Z., Ou, Y., Liang, J., Huang, S., Chen, J., Huang, S., Wei, Q., Liu, Y., Qin, H., & Chen, Y. (2024) · International Journal of General Medicine · doi:10.2147/ijgm.s487967 · Cited by 1
Ethical Norms for the Application of Artificial Intelligence in Medicine and the Role of Nurses
Kutsenko, V., Nurmyradov, Y. N., & Akhmedov, S. R. (2024) · Meditsinskaya sestra · doi:10.29296/25879979-2024-08-03 · Cited by 1
Deciphering the Role of SLFN12 : A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence
Chen, Z., Liu, C., Zheng, W., Fang, Y., Zhang, H., Luo, J., Li, J., Qiu, Y., Peng, J., Xia, Y., et al. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70317 · Cited by 1
Welcoming the evolution of healthcare: A transformative change through artificial intelligence and robotics
Angel, D., & Christy, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-32862-6.00015-8 · Cited by 1
Precision Medicine in Immunoradiotherapy
Feng, J., Peng, J., Qiu, H., Hu, K., Guo, X., Liu, B., Bu, L., & Wu, Q. (2025) · Springer eBooks · doi:10.1007/978-981-96-3659-4_11 · Cited by 1
Mapping the role of cytokine signaling at single-cell and structural resolution in uveal melanoma
Lin, H., Zhou, Z., Sun, H., Li, C., Lu, Y., Wu, Z., Zhou, L., Wang, Y., Pu, Z., Mou, L., et al. (2025) · Genes and Immunity · doi:10.1038/s41435-025-00337-3 · Cited by 1
Letter to the editor: radiomics signature for dynamic monitoring of tumor-inflamed microenvironment and immunotherapy response prediction
Dong, Y., & Zhang, S. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-011778 · Cited by 1
Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosisPreprint
Shi, Y. X., Zhao, L., Li, Y., Liu, B., Zhou, F., Bai, X., & Zhang, J. (2025) · Research Square · doi:10.21203/rs.3.rs-7509527/v1 · Cited by 1
Multi omics integration in colorectal cancer: from molecular insights to precision oncology (Preprint)Preprint
Liu, Z., Ang, M. Y., & Kue, C. S. (2026) · doi:10.2196/preprints.95204 · Cited by 1
AI Models for Uncovering Plant–Microbiome Interactions
Radi, M., Achetoui, H., Dehbi, I., Zennouhi, H., & Lahlali, R. (2026) · Wiley eBooks · doi:10.1002/9781394380312.ch11 · Cited by 1
How does the precise prediction of preeclampsia onset aid the overall management of preeclampsia?
Chigusa, Y. (2024) · Hypertension Research · doi:10.1038/s41440-024-01621-x
WITHDRAWN: The multifaceted role of macrophage migration inhibitory factor (MIF) in innate tumor immunity
Thiele, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14064-8.00004-7
StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological ImagesPreprint
Zhang, J., Luo, B., Teng, F., Tang, G., Cen, W., Qu, C., Liu, X., Liu, X., Liu, X., Huang, H., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4616611/v1
Integrating radiomics and real-world data to predict immune-checkpoint inhibitors efficacy in advanced Non-Small Cell Lung CancerPreprint
Provenzano, L., Favali, M., Mazzeo, L., Spagnoletti, A., Calareso, G., Ruggirello, M., Greco, G. F., Vigorito, R., Lello, A. D., Zec, A., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4763917/v1
Self-citation
Exploring the Use of Deep Learning Models in Predicting Genetic Mutations Associated with Non-Small Cell Lung Cancer: A Systematic Review (Preprint)Preprint
Alsaify, A. R., Shafei, L., Dali, S. A., Adel, A., Al-Saifi, A., Takhtinejad, N. J., Nagmeldin, M., Hamad, A. A., Biswas, M. R., & Alzubaidi, M. S. (2024) · doi:10.2196/preprints.66219
The Dual Impact of AI in Clinical Trials: Perspective
Yaqub, M., & He, L. (2024) · Journal of Engineering Research and Sciences · doi:10.55708/js0309002
The multifaceted role of macrophage migration inhibitory factor (MIF) in innate tumor immunity
Thiele, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14064-8.90004-3
Immunonkologie
Letsch, A. (2024) · Die Onkologie · doi:10.1007/s00761-024-01592-1
Epigenome Analysis
Mondal, D., & Ramdas, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00264-5
Clinical Considerations for the Management of Advanced PD-L1 ≥50% Non-small Cell Lung Cancer In 2025: Should All Patients Be Treated the Same?
Mija, L. A., & Elkrief, A. (2025) · Canadian oncology today. · doi:10.58931/cot.2025.2131
Biomarkers of Cancer
Tauro, S. J., Mohite, P. B., Puri, A. V., Pawar, A., & Singh, S. K. (2025) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-96-4959-4_9
An Ensemble Approach to Predict Immunotherapy Efficacy in Non-Small Cell Lung Cancer Using Digital Pathology
Zec, A., Declich, M. M., Sacco, M., Trovò, F., Lorenzini, D., Prelaj, A., & Miskovic, V. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96235-6_1
Self-citation
Multitask Radiomics-based Dynamic Nomogram for Joint Prediction of ALK Mutation and Ki-67 Expression in NSCLCPreprint
Lin, T. Y., Peng, S., Li, C., Yu, Y., Zhou, M., Quan, X., Chen, X., Zhu, J., & Zeng, D. (2025) · Research Square · doi:10.21203/rs.3.rs-6829111/v1
Transplant-Agents : A Multi-Agent Artificial Intelligence Framework for Reproducibility Assessment of Post-Transplant Risk Prediction and Rejection BiomarkersPreprint
Ding, S., Bhattacharya, S., Sarwal, M., Sirota, M., & Butte, A. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.10.664265
Abnormality-Driven Representation Learning for Radiology Imaging
Ligero, M., Lenz, T., Wölflein, G., Nahhas, O. S. M. E., Truhn, D., & Kather, J. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04965-0_2
Self-citation
Unravelling Colorectal Cancer Survival Factors through Explainable AI in Clinical Data
Kumar, A., & Kaur, S. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189664
Radiomics-based intelligent diagnosis and treatment
Fang, M., Wang, Z., Fu, J., Wang, Y., Zhao, Y., Qin, Z., Zhang, C., Xie, X., Tang, L., & Dong, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30142-1.00006-7
Current Status of Immunotherapy in Resectable Lung Cancer
Deshmukh, P., Abughanimeh, O. K. M., & Ganti, A. K. (2025) · Indian Journal of Surgical Oncology · doi:10.1007/s13193-025-02470-x
A Pathomics-Based Model for Predicting Disease-Free Survival in Gastric Cancer Patients After Curative GastrectomyPreprint
Zheng, L., Jin, Z., Wu, F., Zhu, S., Zhang, Y., Chen, L., Chen, W., Huang, C., Zhu, L., fang, S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7640558/v1
Machine learning–based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy
Lu, S., Zheng, X., Wang, Y., Li, Y., Yu, X., & Zhang, Y. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1723874
Consolidative Radical Cystectomy Following Neoadjuvant Tislelizumab Plus Gemcitabine and Cisplatin in Clinically Lymph Node–positive Bladder Cancer: A Prospective Study
Zhuang, J., Yu, H., Cao, Q., Bai, R., Feng, T., Cai, L., Yang, X. F., & Lü, Q. (2025) · European Urology Open Science · doi:10.1016/j.euros.2025.11.004
Trustworthy assessment of 2D model for lung CT scans
Favali, M., Miskovic, V., Eusebio, A., Trovò, F., Provenzano, L., Guirges, B., Ruggirello, M., Garassino, M. C., Proto, C., Russo, G. L., et al. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254399
Self-citation
A Review on Explainable Artificial Intelligence in Colorectal Cancer Biomarker Analysis using Microbiome Data
Kumar, A., & Kaur, S. (2025) · doi:10.1109/cises66934.2025.11265410 · Cited by 1
Democratizing responsible artificial intelligence for innovation and impact
Atkins, P. R., & Parashar, M. (2025) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420251405007
Novel discovery of geraniol and salvianolic acid B in regulating endometriosis recurrence targets LGALS3/VEGFA via integrating AI and biosensors
Zhang, Y., Wang, K., Shi, G., Guo, X., Liu, Y., Han, Y., Hou, R., Hu, X., Fan, W., Dai, X., et al. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.171649
Special Issue: Exploring Therapeutic Targets in the Evolving Landscape of Cancer Immunotherapy
Galati, D. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms27010243
Cancer biomarker that are currently used in cancer therapy and under evaluation in clinical trials
Tyagi, L., Mishra, S., Simran, Singh, H., Kumar, M., Rathi, G., Kaur, N., Sehgal, S. S., & Kumar, U. (2026) · Advances in cancer research · doi:10.1016/bs.acr.2025.12.010
Systematic Pathway Screening via Integrated Machine Learning Identifies FOXO‐Mediated Transcription Signature for Robust Immunotherapy Response Prediction in Non–Small Cell Lung Cancer
Wu, S., Deng, C., Fan, C., Liang, Q., Zhu, L., Mou, W., Huang, H., Wu, K., Li, Y., Deng, G., et al. (2026) · Human Mutation · doi:10.1155/humu/8690530
Multi-omics integrative analysis of stemness-associated pathological signatures to guide prognosis and therapeutic strategies in uveal melanoma
Sun, Y., Wu, J., Yuan, Y., Wu, W., Lin, T., Zhang, J., & Lin, L. (2026) · International Journal of Surgery · doi:10.1097/js9.0000000000004914
Towards the tumor microenvironment scoring methods for immune checkpoint inhibitor response
Zhou, Q., Kirshtein, A., & Shahriyari, L. (2026) · npj Precision Oncology · doi:10.1038/s41698-025-01221-z
I3LUNG: Clinical Validation of a Multimodal AI Tool to Support Immunotherapy Decisions in NSCLCPreprint
Prelaj, A., Miskovic, V., Sacco, M., Ferrarin, A., Licciardello, C. M., Provenzano, L., Favali, M., Lerma, L., Zec, A., Spagnoletti, A., et al. (2026) · medRxiv · doi:10.64898/2026.01.16.25342913
Self-citation
Les limites et le futur des outils d’intelligence artificielle en pathologie thoracique
Hofman, P. M. (2025) · Revue Francophone des Laboratoires · doi:10.1016/s1773-035x(25)76515-6
Advancing dermatological clinical trials through artificial intelligence
Frasier, K., Hash, M. G., Remé, B., & Pham, D. (2026) · Archives of Dermatological Research · doi:10.1007/s00403-026-04530-w
An interpretable deep learning framework based on TabNet-Cox for risk stratification and prognostic assessment in hepatocellular carcinoma immunotherapy
Dong, W., & Qian, C. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1751829
A pathomics-based pyroptosis signature predicts survival in clear cell renal cell carcinoma
Wang, K., Wei, S., Hou, D., Wang, L., Kang, L., Li, H., Li, C., & Wang, H. (2026) · Discover Oncology · doi:10.1007/s12672-026-04685-y
Care
Wenk, J., Gustav, M., Lüneburg, L.-M., Pötter, M. A., Huang, S., Bayram, İ., Krzywinski, J., Gilbert, S., Kather, J. N., & Ziemssen, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-330044-8.00014-2
Self-citation
Routine detection of reactive Cyfra21-1-specific CD8+ T cells and its predictive value for responsiveness to PD-1/PD-L1 inhibitor therapy in NSCLC patients
Song, S., Wu, Y., Zhao, Y., Zhao, G., Li, M., Wu, Y., Zhu, S., Yue, F., Chen, C., Jiang, H., et al. (2026) · Translational research · doi:10.1016/j.trsl.2026.02.010
An Adaptable Prospective Biomarker and Therapeutic Target for Diverse Cancer Types
Kumar, N., Jatav, S., & Gupta, V. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898814021126010011
A Multi-Omics and machine learning platelet-related prognostic signature in multiple myeloma
Li, X., Xiao, Q., Wang, K., Lin, X., He, Y., Peng, J., Li, N., Zhou, H., & Chen, P. (2026) · Annals of Hematology · doi:10.1007/s00277-026-06867-8
A machine learning assay to predict disease recurrence in hormone receptor-positive breast cancer
Bielo, L. B., Trapani, D., Razavi, P., Chandarlapaty, S., Zhan, Y., & Curigliano, G. (2026) · ESMO Open · doi:10.1016/j.esmoop.2026.106064
Integrative multi-omics analysis reveals metabolic dysfunction signatures as critical determinants of prostate cancer prognosis and immunosuppressive microenvironments
Nie, X., He, Z., Wang, K., Zhu, D., Li, W., Zhang, L., & Yu, Y. (2026) · European journal of medical research · doi:10.1186/s40001-026-04049-w
Identification of Biomarkers in Alzheimer Disease using Artificial Intelligence
Rasheedee, A. H. A., Singh, A. K., Kaur, J., & Garg, V. (2025) · 2025 2nd Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11410943
Machine Learning and Emerging Technologies in Cancer Care
Rafique, M. A., & Ilyas, Q. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4312-9.ch009
Integrative Mendelian Randomization and Pathomics Analysis Using Expression Quantitative Trait Loci and Genome‐Wide Association Study Data Identifies Mismatch Repair Genes as Prognostic Biomarkers in Gastric Adenocarcinoma
Zhang, Y., Li, D., Jin, Y., Zhao, W., Guo, P., Wang, Z., Zhu, X., Ma, Z., Sui, L., Liang, Y., et al. (2026) · International Journal of Genomics · doi:10.1155/ijog/2686529
Experimental multi-center validation of a radiomics-based photonic quantum precision medicine architecture for lesion-level prediction of anti-PD-1 response in non-small cell lung cancerPreprint
Olgiati, S., Santona, F., Meloni, D., Barabino, E., Rossi, G., Genova, C., Grossi, F., & Heidari, N. (2026) · medRxiv · doi:10.64898/2026.03.09.26347939
Self-citation
Habitat model based on transvaginal ultrasound images for non-invasive prediction of lymphatic vessel interstitial infiltration in endometrial cancer
Jiao, C., Yuanyuan, G., Jianghua, H., & Maochun, Z. (2026) · World Journal of Surgical Oncology · doi:10.1186/s12957-026-04294-7
PHF23-Related Prognostic Signature Modulates Immune Microenvironment and Promotes Tumor Malignancy in Glioma
Zhao, G., Wang, X., Yang, P., Feng, P., Dai, J., Niu, L., Yuan, G., & Pan, Y. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27062570
A Pathomics-Based Prognostic Model for Disease-Free Survival in Resected Gastric Cancer
Zheng, L., Jin, Z., Wu, F., Zhu, S., Zhang, Y., Chen, L., Chen, W.-C., Huang, C., Zhu, L., Fang, S., et al. (2026) · Cancers · doi:10.3390/cancers18060993
The importance of ever-evolving phase 1 trials in oncology
Domen, A., Vanclooster, P., Teuwen, L.-A. M. N., Dekeyser, B., Vandamme, T., & Prenen, H. (2026) · Academia oncology. · doi:10.20935/acadonco8203
Cancer biology and the principles of targeted cancer drug discovery
Davies, A., Jones, G., Krishnan, S., Krishnamoorthy, M., Maruggi, M., Kaneda, M., & Marjon, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00035-2
Plying potency assays for immunotherapy of solid tumors
Burns, J. S., Gjeta, R., Lesný, P., & Vives, J. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1770222
Data to diagnosis: AI-powered insights for biomarker discovery in gastrointestinal tract cancers
Anbarasu, S., Bhattacharya, S., David, R., Dash, D., Vindhyasree, G., Vaishmitha, R., Ramaiah, S., & Anbarasu, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44121-9.00015-9
Advancing precision oncology through hNQO1-activatable NIR-II probes: Integrating molecular imaging with artificial intelligence
Egbewole, B. I., Oisakede, E. O., Egbon, E., Bello, O. J., Analikwu, C. C., & Olawade, D. B. (2026) · Intelligent Oncology · doi:10.1016/j.intonc.2026.100053
Melanoma and Artificial Intelligence
Toli, O., Navarrete‐Dechent, C., Karampinis, E., & Stratigos, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-15614-3_7
A Narrative Approach to Mismatch Repair-Deficient Endometrial Cancer
Barberis, M. C. P., & Zhan, Y. (2026) · Journal of Molecular Pathology · doi:10.3390/jmp7020017
An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human–machine comparison study
Tang, L., Xie, L., Yang, X., Liu, J., Zou, X., & Xia, W. (2026) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-026-04535-w
Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC
Song, J., Nie, Q., Wang, S., An, T., Li, X., Liu, Y., Wang, H., Shi, R., Wang, L., Lin, M., et al. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001361
Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities
Shi, Q., Lou, N., & Xue, C. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1769097 · Cited by 1
Current advanced immunological tools and databases in tumor immunity
Ahmed, T., Alam, K. T., & Tisha, Z. T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41551-7.00019-9
MicroRNA-based strategies to overcome the immunotherapy barrier in pancreatic ductal adenocarcinoma
徐慧文, Wang, Q., Cao, R., Qiu, Z., Liu, Y., Hu, J., Zheng, G., & Cheng, C. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1744566
Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers
Li, H. W., Nan, H., Sun, Y., Zhao, M., Qiu, Y., Chen, S., & Wang, Y. (2026) · World Journal of Surgical Oncology · doi:10.1186/s12957-026-04268-9
AI in designing novel cancer treatments
Kumar, P., Umar, M., Naeem, A., & Khan, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00011-6
Revolutionizing genomic medicine with artificial intelligence and data analytics
Patra, P. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27638-5.00013-4
Interpretable machine learning and deep learning model for discriminating pheochromocytoma from adrenocortical adenoma based on CT: A multicenter study
Sun, H., Yu, M., Zhou, Y., An, Z., Guo, X., Zhang, S., Lei, L., Liu, Y., Liang, F., Sun, B., et al. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112902
From algorithm to clinic: A critical review of deep learning advances and challenges in dermatological diagnosis and care delivery
Rahman, S., udddin, N., Ahmad, H. I., Ali, M., Fazal, S., Ahmad, A., Sultan, N., & Mashal (2026) · ICT Express · doi:10.1016/j.icte.2026.04.010
International metastatic renal cell carcinoma database consortium classification and regression tree analysis to characterize objective response rates to first line in metastatic renal cell carcinoma
Zarbá, M., O’Sullivan, D., Maj, D., Cheung, W. Y., Ludwig, L., Wells, C., Ferrier, E., Chehade, R. E. H., Donskov, F. N., Eid, M., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1827686
Data-driven precision: artificial intelligence redefining immunoradiotherapy in advanced pancreatic cancer
Liu, Y., Men, X.-D., Di, B.-R., Lei, Y.-L., Li, X. J., & Luo, Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1804673
Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses
Ran, L., Hu, G., Fan, C., Teng, Y., Zhao, R., Li, Q., Yang, J., & Zhang, C. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1774959
Recent Advances in Nanomedicine for Targeted Phototherapy in Head and Neck Cancer
Liu, H., Chen, L., Li, W., Wu, L., Xia, Z., Wu, Y., Huang, Y., Gu, S., Zhang, J., & Shen, Z. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s596902
A temporal keyword co-occurrence network mining framework for detecting structural transitions in cancer biomarker research (2006–2023)
Hwang, J., Kim, S., Kim, H., & Seo, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-52746-7
From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI Integration
Jiang, Y., Wang, N., Zeng, Q., Liu, J., Liu, X., & Cao, G. (2026) · Human Mutation · doi:10.1155/humu/6434376
Two decades of research on drug resistance in renal cell carcinoma
Huang, Y., Tang, Z., Xu, H., Wei, R., Cao, M., Yang, T., Pan, Y., Wan, F., Zhao, J., Gu, Y., et al. (2026) · Discover Oncology · doi:10.1007/s12672-026-04975-5
Artificial intelligence and machine learning approaches in rational drug design to overcome cancer chemoresistance
Mishra, S., Garg, P., Tiwari, A., & Srivastava, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45717-3.00017-9
Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy
Yu, J., Xu, G., Li, Y. C., Huang, M., Yang, L., & Peng, X. Y. (2026) · Journal of Nanobiotechnology · doi:10.1186/s12951-026-04525-9
Multidimensional integration: A novel breakthrough in prognostic prediction for immunochemotherapy in human epidermal growth factor receptor-2-negative advanced gastric cancer
Li, M.-F., Song-nan, D., Bao, P.-T., & Li, Y.-G. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.118614
Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures
Nguyen, T. H., Akdiş, C. A., Baccarelli, A., Banerjee, S., Bhowmik, R. T., Coull, B. A., Childs, M. L., Pinna, L. C., Golden, C. D. W., Härtung, T., et al. (2026) · Allergy · doi:10.1111/all.70401
Toward personalised cancer care: AI in precision and molecular oncology
Sinha, A., Anzar, D., Owusu-Boampong, A. B., Nkrumah-Boateng, P. A., Mannan, K. M., Ali, S. H., Roy, S., & Wireko, A. A. (2026) · European journal of medical research · doi:10.1186/s40001-026-04670-9
Integrative multi-omics identifies a glycosylation-based prognostic framework and nominates ALG3 for targeted therapy in bladder cancer
Feng, W., & He, W. (2026) · Discover Oncology · doi:10.1007/s12672-026-05387-1
Machine Learning in Precision Oncology: Integrating Multimodal Clinical Data for Treatment Selection
Magwenzi, S. (2026) · Journal of Precision Medicine and Artificial Intelligence · doi:10.64949/3dfqcq13
Multidimensional mechanisms of drug resistance in non-small cell lung cancer and novel strategies for overcoming it
Zhang, X.-Y., Pan, X.-Y., Zhu, Y., Hu, M., Ma, S., Zheng, S.-Y., Wu, C.-H., Xie, Y.-L., & Xie, Y. (2026) · World Journal of Clinical Oncology · doi:10.5306/wjco.121766
Pharmacological Targeting of Angiogenesis in Head and Neck Cancer: Molecular Mechanisms and Emerging Therapeutic Strategies
Szekely, D., Armega-Anghelescu, A., Barb, A. C., Novacescu, D., Dumitru, C., Manole, A., Dan, R. G., & Baderca, F. (2026) · Pharmaceuticals · doi:10.3390/ph19060950
Unveiling the comorbidity hub: WT1 drives renal cancer progression in chronic kidney disease and confers sirolimus vulnerability
Zhang, W., Tang, P., Wu, S., & Sui, Z. (2026) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2026.13214

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

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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks