ArticleJournal of ovarian research2026
Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer.
Article in Journal of ovarian research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe tumor-stroma ratio (TSR) has emerged as a promising prognostic biomarker in epithelial ovarian cancer (EOC); however, its preoperative assessment remains challenging.
objectiveTo develop a non-invasive CT-based radiomics machine learning model for preoperative TSR prediction and to evaluate its association with platinum resistance and survival outcomes in EOC.
methodsThis retrospective study included 172 patients with pathologically confirmed EOC. TSR was histologically classified as stroma-rich (≥ 50%) or stroma-poor (< 50%). A total of 718 radiological features—including 4 conventional imaging features and 714 quantitative radiomic descriptors—were extracted from contrast-enhanced CT images, along with 26 clinical variables. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression, and a linear support vector machine (SVM) classifier was constructed. The dataset was randomly divided into a training cohort (70%) and validation cohort (30%). Model performance was evaluated using five-fold cross-validation in the training cohort and tested on the independent validation cohort. Associations between the predicted TSR and clinical outcomes were analyzed using multivariable logistic and Cox regression models to assess the clinical value of the model.
resultsStroma-rich tumors were significantly associated with advanced FIGO stage, poorer differentiation, ascites, lymph node metastasis, worse completeness of cytoreduction, and platinum resistance. The SVM model achieved a mean cross-validated AUC of 0.83 ± 0.08 in the training cohort and an AUC of 0.83 in the independent validation cohort. Although histological TSR demonstrated superior statistical potency in survival discrimination, the predicted TSR remained an independent predictor of platinum resistance, progression-free survival (PFS) and overall survival (OS) in the preoperative setting.
conclusionsThe proposed CT-based radiomics model enables reliable, non-invasive estimation of TSR and provides a biologically interpretable imaging biomarker for risk stratification in EOC. Radiomics-predicted TSR may help identify patients at increased risk of platinum resistance and poor prognosis, supporting individualized treatment planning.
Indexed as
Identifiers
What Socratic 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 Socratic graph.