Artificial intelligence and radiomics: Clinical applications for patients with advanced melanoma treated with immunotherapy Review
| Authors: | McGale, J.; Hama, J.; Yeh, R.; Vercellino, L.; Sun, R.; Lopci, E.; Ammari, S.; Dercle, L. |
| Review Title: | Artificial intelligence and radiomics: Clinical applications for patients with advanced melanoma treated with immunotherapy |
| Abstract: | Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts. © 2023 by the authors. |
| Keywords: | adult; cancer survival; controlled study; treatment response; major clinical study; review; hepatitis; advanced cancer; cancer combination chemotherapy; diarrhea; validation process; cancer patient; nuclear magnetic resonance imaging; prospective study; clinical practice; t lymphocyte; ipilimumab; cancer immunotherapy; melanoma; immune system; tumor volume; inflammation; cohort analysis; diagnostic imaging; oncology; retrospective study; pruritus; rash; lymphocyte activation; immunotherapy; medical imaging; artificial intelligence; patient safety; colitis; hyperthyroidism; adaptive immunity; herpes simplex virus; cell activity; immunocompetent cell; genetic polymorphism; optical coherence tomography; abscopal effect; vitiligo; immunopet; hypertransaminasemia; thyroiditis; machine learning; immune checkpoint inhibitor; response evaluation criteria in solid tumors; nivolumab; human; male; female; evaluation study; deep learning; radiomics; convolutional neural network; tumor mutational burden; people by smoking status; cross validation |
| Journal Title: | Diagnostics |
| Volume: | 13 |
| Issue: | 19 |
| ISSN: | 2075-4418 |
| Publisher: | MDPI |
| Date Published: | 2023-10-01 |
| Start Page: | 3065 |
| Language: | English |
| DOI: | 10.3390/diagnostics13193065 |
| PROVIDER: | scopus |
| PMCID: | PMC10573034 |
| PUBMED: | 37835808 |
| DOI/URL: | |
| Notes: | Source: Scopus |
