ArticleClinical cancer research : an official journal of the American Association for Cancer Research2021
Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- Melanoma evolution in the lymph node shapes systemic outcomes.bioRxiv : the preprint server for biology · 2026Article
- Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Review
- Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.Current issues in molecular biology · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- A Targetable Developmental Program Coregulates Angiogenesis and Immune Evasion in Melanoma.Cancer discovery · 2026Article
- Challenges and potential of using digital biomarkers in healthcare and clinical trials.Communications medicine · 2026Review
- Circulating Tumor DNA in Melanoma: Advances in Detection, Clinical Applications, and Integration with Emerging Technologies.International journal of molecular sciences · 2026Review
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- A Holistic Review of Oncological Drug Targets and Trajectories of Resistance in Cancer Therapy.Oncology research · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.International journal of molecular sciences · 2025Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- The Current State and Future Prospects for Artificial Intelligence in Dermatology.Dermatologic clinics · 2025Review
- Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Gene expression cluster differences and molecular correlation with the STING pathway in orbital MALT lymphoma and orbital IgG4-related eye disease.Discover oncology · 2025Article
- Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Review
34 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
purposeSeveral biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma. EXPERIMENTAL
designWe used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400).
resultsThe multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (
conclusionsHistology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.