ArticleJournal of ovarian research2026
Spatial habitats radiomics from multiparametric magnetic resonance imaging predict platinum resistance 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
10 authors.
Funding
Abstract
objectivesTo investigate the potential of employing spatial habitat radiomics as an innovative tumor biomarker for predicting the response of epithelial ovarian cancer (EOC) patients to platinum-based chemotherapy. METHODS AND MATERIALS: In this multicentre retrospective study, 273 EOCs with preoperative MRI data (training set = 175, Hospital A; external testing set = 98, Hospital B) were included. Multivariate logistic regression analysis was conducted to identify independent predictors for the development of the clinical model. Spatial habitats were delineated using the K-means clustering method. On T2-weighted imaging and contrast-enhanced T1-weighted imaging data, 3,668 radiomics features were extracted from the whole primary tumor and each habitat. All models were constructed utilizing logistic regression, support vector machine and random forest. The area under curve (AUC), calibration plot, and decision curve were employed to assess the performance and clinical utility of models.
resultsPlatinum resistance was detected in 48 out of 273 patients (17.6%). Multivariate regression analysis revealed that age is an independent predictor of platinum resistance in EOC patients. The primary tumors were clustered into three habitats, with Habitat 3 model showing the best predictive performance (AUC = 0.760) compared to Habitat 1 and 2 models in the testing set (both p < 0.05). The Habitat model achieved a greater AUC than the Radiomics and Clinical models (0.813 vs. 0.609 and 0.763, respectively). The habitat-based Nomogram combining 18 habitat radiomic features with clinical features had an AUC of 0.818 in the testing set, accompanied by improved calibration and clinical utility. Furthermore, the plasma lipid profile (LDL, HDL, CHOL and TG) had significant correlations with the habitat radiomics features (p < 0.05).
conclusionsThe habitat-based Nomogram could predict platinum resistance in EOC patients, with habitat 3 emerging as the most predictive habitat. The habitat radiomics features may serve as indicators of plasma lipid.
Indexed as
Identifiers
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.