ArticleCancer medicine2024
Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence-Based Histopathological Analysis to Assist Pathologists in Diagnosing Ewing Sarcoma and Selected Tumor Entities Using Tissue Microarrays.International journal of molecular sciences · 2026Article
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile.Scientific reports · 2025Article
- Expression of marker genes to assess the spermatogenic capacity in patients with idiopathic non-obstructive azoospermia.Journal of assisted reproduction and genetics · 2025Article
- Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.Cancer medicine · 2024Article
- Real World Data from Patients With Osteosarcoma Treated in 8 Medical Centers in Greece: Ninety Percent or Greater Tumor Necrosis Is Associated With Disease-free Survival.Cancer diagnosis & prognosisArticle
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Authors and funding
8 authors.
Funding
Abstract
backgroundPrompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS.
methodsOur retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation.
resultsThe cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852.
conclusionAI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.
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