ReviewCancers2022
Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Deep learning-based classification of gh-secreting pituitary neuroendocrine tumors granulation patterns using H&E-stained histopathology.BMC endocrine disorders · 2026Article
- Towards Accurate Breslow Measurements: Mitigating Issues in Histopathological Imaging.Entropy (Basel, Switzerland) · 2026Article
- A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.Nature communications · 2025Article
- Large-Scale Dermatopathology Dataset for Lesion Segmentation: Model Development and Analysis.Journal of Korean medical science · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Review of Non-Invasive Imaging Technologies for Cutaneous Melanoma.Biosensors · 2025Review
- Review
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.Diagnostic pathology · 2024Article
- Overdiagnosis in Melanoma Screening: Is It a Real Problem?Dermatology practical & conceptual · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.
Indexed as
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What OpenQuestion holds
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.