Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy.
Jeremy McGaleJakob HamaRandy YehLaetitia VercellinoRoger SunEgesta LopciSamy AmmariLaurent DerclePublished in: Diagnostics (Basel, Switzerland) (2023)
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.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- big data
- newly diagnosed
- end stage renal disease
- ejection fraction
- high resolution
- clinical practice
- chronic kidney disease
- computed tomography
- systematic review
- primary care
- magnetic resonance
- magnetic resonance imaging
- healthcare
- prognostic factors
- contrast enhanced
- type diabetes
- squamous cell carcinoma
- metabolic syndrome
- adipose tissue
- lymph node metastasis
- quality improvement
- pet ct