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Objectives <p>To 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.</p> Methods and Materials <p>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.</p> Results <p>Platinum 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 <i>p</i> &lt; 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 (<i>p</i> &lt; 0.05).</p> Conclusions <p>The 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.</p>

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Spatial habitats radiomics from multiparametric magnetic resonance imaging predict platinum resistance in epithelial ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Lingling Lin,
  • Huawei Wu,
  • Chao Wang,
  • Jialu Xu,
  • Enhui Xin,
  • Yafen Li,
  • Jun Zhu,
  • Jianli Yu,
  • Yu Wang,
  • Jiejun Cheng

摘要

Objectives

To 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.

Results

Platinum 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).

Conclusions

The 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.