ArticleJournal of translational medicine2025
CT radiomic stratification signature to optimize clinical decisions for ovarian cancer patients receiving neoadjuvant chemotherapy and the underlying biological basis: a multicenter retrospective study.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer is a highly heterogeneous disease, with substantial variation in patients’ responses to neoadjuvant chemotherapy (NACT) and prognosis. Currently, specific stratification biomarkers for patient selection are lacking. Stratifying patients based on noninvasive imaging data prior to treatment could significantly improve treatment decisions.
methodsConsensus clustering of pretreatment radiomic features from computed tomography images identified imaging subtypes in NACT patients. Five classifiers were developed for patient stratification in a subsequent cohort, and the biological behavior behind the radiomics-based clusters was explored.
resultsConsensus clustering identified four patient clusters, with consistent survival trends across both clustering and stable classifiers. Clusters 3 and 4 showed higher complete gross resection rates, longer overall survival, and more BRCA1 mutations, while clusters 1 and 2 had poorer prognoses and lower complete gross resection rates. Despite a higher tumor stage, cluster 4 was more sensitive to chemotherapies and targeted therapies. Cluster 2 had the lowest proportion of well-differentiated tumors, and upregulated genes were enriched in pathways related to epithelial mesenchymal transition and other classic cancer pathways. Weighted gene co-expression network analysis identified key modules and hub genes in clusters 1 and 2, with RNA signature scoring used for validation.
conclusionsStratification based on noninvasive imaging revealed distinct biological patterns among ovarian cancer patients, helping to identify subgroups with favorable responses to neoadjuvant chemotherapy and better survival outcomes, thereby facilitating more personalized treatment decisions.
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