ArticleWorld journal of surgical oncology2020
Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients.
Article in World journal of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- [State of digitalization in dermatopathology].Pathologie (Heidelberg, Germany) · 2025Review
- Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability.BioData mining · 2024Article
- Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images.PLoS computational biology · 2023Article
- A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients.Scientific reports · 2022Article
- Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma.Genes · 2022Article
- Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models.Frontiers in medicine · 2022Article
- Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients.iScience · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundAccurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis.
methodsWe enrolled in this study a total of 152 patients from TCGA-SKCM (The Cancer Genome Atlas Skin Cutaneous Melanoma project) with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data, and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification.
resultsThe model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction.
conclusionsOur study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis.
trial registrationThis study was based on public open data from TCGA and hence the study objects were retrospectively registered.
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