Research graph
References from Artificial Intelligence applied to surgical pathology in the management of cutaneous melanoma. Local targets link to admitted publications; unresolved targets remain external evidence.
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2021 · External reference
Melanoma staging: Evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual
2017 · External reference
The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth
10.1016/j.ccell.2023.02.016 · 2023 · External reference
Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of melanoma, version 3.0
10.1136/jitc-2023-006947 · 2023 · External reference
Prognosis of patients with primary melanoma stage I and II according to American Joint Committee on Cancer version 8 validated in two independent cohorts: Implications for adjuvant treatment
10.1200/jco.22.00202 · 2022 · External reference
The burden of skin disease in the United States
10.1016/j.jaad.2016.12.043 · 2017 · External reference
Chronic immune-related adverse events following adjuvant anti-PD-1 therapy for high-risk resected melanoma
10.1001/jamaoncol.2021.0051 · 2021 · External reference
European consensus-based interdisciplinary guideline for melanoma. Part 2: Treatment—Update 2022
10.1016/j.ejca.2022.04.018 · 2022 · External reference
Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study
10.1136/bmj.j2813 · 2017 · External reference
Intelligence artificielle : quel avenir en anatomie pathologique ?
10.1016/j.annpat.2019.01.004 · 2019 · External reference
Validating whole slide imaging for diagnostic purposes in pathology: Guideline from the College of American Pathologists Pathology and Laboratory Quality Center
10.5858/arpa.2013-0093-cp · 2013 · External reference
Whole slide imaging equivalency and efficiency study: Experience at a large academic center
10.1038/s41379-019-0205-0 · 2019 · External reference
Digital pathology in the time of corona
10.1136/jclinpath-2020-206845 · 2020 · External reference
A survey on deep learning in medical image analysis
10.1016/j.media.2017.07.005 · 2017 · External reference
Artificial intelligence in melanoma detection: A review of current technologies and future directions
10.1155/int/3164952 · 2025 · External reference
Unresolved reference
External reference
Data-efficient and weakly supervised computational pathology on whole-slide images
10.1038/s41551-020-00682-w · 2021 · External reference
Atypical Spitz nevi/tumors: Lack of consensus for diagnosis, discrimination from melanoma, and prediction of outcome
10.1016/s0046-8177(99)90193-4 · 1999 · External reference
Image analysis of cutaneous melanoma histology: A systematic review and meta-analysis
10.1038/s41598-023-31526-7 · 2023 · External reference
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
10.1016/j.ejca.2019.06.012 · 2019 · External reference
Diagnostic performance of artificial intelligence for histologic melanoma recognition compared with 18 international expert pathologists
10.1016/j.jaad.2021.02.009 · 2022 · External reference
Pan-cancer image-based detection of clinically actionable genetic alterations
10.1038/s43018-020-0087-6 · 2020 · External reference
Deep learning and pathomics analyses reveal cell nuclei as important features for mutation prediction of BRAF-mutated melanomas
10.1016/j.jid.2021.09.034 · 2022 · External reference
Predicting BRAF mutations in cutaneous melanoma patients using neural network analysis
10.1155/jskc/3690228 · 2024 · External reference
1121P Artificial intelligence to predict BRAF mutational status from whole slide images in melanoma
10.1016/j.annonc.2024.08.1189 · 2024 · External reference
Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma
10.1097/00000658-197011000-00017 · 1970 · External reference
Artificial intelligence in histopathology: Enhancing cancer research and clinical oncology
10.1038/s43018-022-00436-4 · 2022 · External reference
AI-derived prognostic biomarkers from melanoma whole slide image segmentation: An initial discovery and assessment
10.1002/2056-4538.70075 · 2026 · External reference
A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients
10.1038/s41598-022-24315-1 · 2022 · External reference
Application of deep learning on the prognosis of cutaneous melanoma based on full scan pathology images
2022 · External reference
Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning
10.1111/jdv.20538 · 2025 · External reference
Clinical validation of an AI-based prognostic assay for stage I-II cutaneous melanoma
10.1200/jco.2026.44.16_suppl.9578 · 2026 · External reference
Gene expression profiling for melanoma risk stratification: Where do we stand?
10.1007/s13671-026-00494-2 · 2026 · External reference
Comparison of an AI-based whole-slide image prognostic assay with gene expression profiling in early-stage melanoma
10.1111/jdv.70366 · 2026 · External reference
Tumor-infiltrating lymphocyte grade in primary melanomas is independently associated with melanoma-specific survival in the population-based Genes, Environment and Melanoma study
10.1200/jco.2013.51.3002 · 2013 · External reference
Objective assessment of tumor-infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms
10.1016/j.ebiom.2022.104143 · 2022 · External reference
Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma
10.1038/s41467-025-65051-0 · 2025 · External reference
AI-based histopathology analysis predicts checkpoint inhibitor response in advanced melanoma and identifies patterns associated with response
10.1016/j.ejca.2026.116831 · 2026 · External reference
10.1007/978-3-031-84460-7_21
10.1007/978-3-031-84460-7_21 · External reference
Measuring domain shift for deep learning in histopathology
10.1109/jbhi.2020.3032060 · 2021 · External reference
TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Artificial intelligence predictive model for hormone therapy use in prostate cancer
10.1056/evidoa2300023 · 2023 · External reference
Development and validation of a computational histology artificial intelligence-powered biomarker in metastatic hormone-sensitive prostate cancer on randomized phase III trials
10.1200/po-26-00321 · 2026 · External reference
AI-based pathology classifier predicts sensitivity to enzalutamide in metastatic hormone-sensitive prostate cancer: a biomarker analysis of the ENZAMET trial
10.1158/1078-0432.ccr-26-0871 · 2026 · External reference
Development and validation of a computational histology artificial intelligence-powered predictive biomarker for selection of chemotherapy in advanced pancreatic cancer
10.1200/jco-25-02199 · 2026 · External reference
Grad-CAM: Visual explanations from deep networks via gradient-based localization
10.1007/s11263-019-01228-7 · 2020 · External reference
Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) [Internet]
2024 · External reference
A vision-language foundation model for precision oncology
10.1038/s41586-024-08378-w · 2025 · External reference
Unresolved reference
External reference
Towards robust foundation models for digital pathology
10.1038/s41467-026-73923-2 · 2026 · External reference
Federated learning in computational pathology: a literature review
10.1117/1.jmi.12.6.061412 · 2025 · External reference
