ArticleScientific reports2022
A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.Nature communications · 2025Article
- Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction.Dermatopathology (Basel, Switzerland) · 2025Review
- Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.Biomedical engineering online · 2025Article
- A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.Journal of imaging informatics in medicine · 2025Article
- Review
- Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.NPJ digital medicine · 2025Article
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.The journal of pathology. Clinical research · 2024Article
- An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.Journal of translational medicine · 2024Article
- Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.Frontiers in medicine · 2024Article
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- Benefits of spatial uncertainty aggregation for segmentation in digital pathology.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosis.Research square · 2023Article
- Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.Scientific reports · 2023Article
- Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records.Translational lung cancer research · 2023Article
- Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.Scientific reports · 2023Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.
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