CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study
Abstract Objective Platinum resistance carries poor prognosis in epithelial ovarian carcinoma (EOC). This study aimed to assess the value of radiomics model based on contrast-enhanced CT (ceCT) in predicting response to platinum-based chemotherapy in EOC. Materials and methods Patients with histolog...
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| Format: | Article |
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BMC
2025-07-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-025-00906-9 |
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| author | Mengge He Rahul Singh Mandi Wang Grace Ho Esther M. F. Wong Keith W. H. Chiu Anthony K. T. Leung Ka Yu Tse Philip P. C. Ip Andy Hwang Lujun Han Elaine Y. P. Lee |
| author_facet | Mengge He Rahul Singh Mandi Wang Grace Ho Esther M. F. Wong Keith W. H. Chiu Anthony K. T. Leung Ka Yu Tse Philip P. C. Ip Andy Hwang Lujun Han Elaine Y. P. Lee |
| author_sort | Mengge He |
| collection | DOAJ |
| description | Abstract Objective Platinum resistance carries poor prognosis in epithelial ovarian carcinoma (EOC). This study aimed to assess the value of radiomics model based on contrast-enhanced CT (ceCT) in predicting response to platinum-based chemotherapy in EOC. Materials and methods Patients with histologically confirmed EOC and pre-treatment ceCT were retrospectively recruited from 5 centres. All patients underwent standard platinum-based chemotherapy and optimal cytoreduction. Platinum sensitivity was determined by whether it recurred within six months after platinum-based chemotherapy. The whole tumour volume was manually segmented on the baseline ceCT. Radiomics features were extracted using the open-source package PyRadiomics (version 3.0.1). Patients from centres A-C were randomly divided into training and internal validation sets in 4:1 ratio. Patients from the centres D and E were assigned as independent external validation sets. Spearman’s rank correlation followed by 5-fold stratified cross validation (SCV) elastic net repeated for 100 times, and Mann-Whitney U test were deployed for feature reduction and selection. Adaptive synthetic sampling was applied to minimize class biases. Extra Trees classifier across 10-fold SCV was used for model building. The area under curve (AUC), calibration curve assessment, and decision curve analysis (DCA) were deployed to evaluate model performance and translational clinical utility. Results Seven hundred and three EOC patients (51.6 ± 9.3 years) were recruited. The training data (n = 608) yielded the following classification metrics: AUC (0.917), sensitivity (83.9%), specificity (94.4%), and accuracy (91.7%) in the internal validation set. The external validation set using centre D (n = 44) had AUC (0.877), sensitivity (76.5%), specificity (92.6%), and accuracy (86.4%); while centre E (n = 51) had AUC (0.845), sensitivity (73.3%), specificity (86.1%), and accuracy (82.4%) in predicting platinum sensitivity. DCA illustrated net clinical benefit in internal validation set and both external validation sets. Conclusions The proposed CT-based radiomics model could be useful in predicting platinum sensitivity in EOC with potential in guiding personalized treatment in EOC. |
| format | Article |
| id | doaj-art-ad58bb4ff6214712b401aa3d0dd71d9d |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-ad58bb4ff6214712b401aa3d0dd71d9d2025-08-20T03:45:34ZengBMCCancer Imaging1470-73302025-07-0125111310.1186/s40644-025-00906-9CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre studyMengge He0Rahul Singh1Mandi Wang2Grace Ho3Esther M. F. Wong4Keith W. H. Chiu5Anthony K. T. Leung6Ka Yu Tse7Philip P. C. Ip8Andy Hwang9Lujun Han10Elaine Y. P. Lee11Department of Diagnostic Radiology, The University of Hong KongDepartment of Diagnostic Radiology, The University of Hong KongDepartment of Diagnostic Radiology, The University of Hong KongDepartment of Radiology, Queen Mary HospitalDepartment of Radiology, Pamela Youde Nethersole Eastern HospitalDepartment of Radiology & Imaging, Queen Elizabeth HospitalDepartment of Clinical Oncology, Queen Elizabeth HospitalDepartment of Obstetrics and Gynecology, The University of Hong KongDepartment of Pathology, The University of Hong KongDepartment of Diagnostic Radiology, The University of Hong KongDepartment of Radiology, Sun Yat-Sen University Cancer CentreDepartment of Diagnostic Radiology, The University of Hong KongAbstract Objective Platinum resistance carries poor prognosis in epithelial ovarian carcinoma (EOC). This study aimed to assess the value of radiomics model based on contrast-enhanced CT (ceCT) in predicting response to platinum-based chemotherapy in EOC. Materials and methods Patients with histologically confirmed EOC and pre-treatment ceCT were retrospectively recruited from 5 centres. All patients underwent standard platinum-based chemotherapy and optimal cytoreduction. Platinum sensitivity was determined by whether it recurred within six months after platinum-based chemotherapy. The whole tumour volume was manually segmented on the baseline ceCT. Radiomics features were extracted using the open-source package PyRadiomics (version 3.0.1). Patients from centres A-C were randomly divided into training and internal validation sets in 4:1 ratio. Patients from the centres D and E were assigned as independent external validation sets. Spearman’s rank correlation followed by 5-fold stratified cross validation (SCV) elastic net repeated for 100 times, and Mann-Whitney U test were deployed for feature reduction and selection. Adaptive synthetic sampling was applied to minimize class biases. Extra Trees classifier across 10-fold SCV was used for model building. The area under curve (AUC), calibration curve assessment, and decision curve analysis (DCA) were deployed to evaluate model performance and translational clinical utility. Results Seven hundred and three EOC patients (51.6 ± 9.3 years) were recruited. The training data (n = 608) yielded the following classification metrics: AUC (0.917), sensitivity (83.9%), specificity (94.4%), and accuracy (91.7%) in the internal validation set. The external validation set using centre D (n = 44) had AUC (0.877), sensitivity (76.5%), specificity (92.6%), and accuracy (86.4%); while centre E (n = 51) had AUC (0.845), sensitivity (73.3%), specificity (86.1%), and accuracy (82.4%) in predicting platinum sensitivity. DCA illustrated net clinical benefit in internal validation set and both external validation sets. Conclusions The proposed CT-based radiomics model could be useful in predicting platinum sensitivity in EOC with potential in guiding personalized treatment in EOC.https://doi.org/10.1186/s40644-025-00906-9Epithelial ovarian carcinomaComputed tomographyRadiomicsChemotherapyPlatinum-Resistance |
| spellingShingle | Mengge He Rahul Singh Mandi Wang Grace Ho Esther M. F. Wong Keith W. H. Chiu Anthony K. T. Leung Ka Yu Tse Philip P. C. Ip Andy Hwang Lujun Han Elaine Y. P. Lee CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study Cancer Imaging Epithelial ovarian carcinoma Computed tomography Radiomics Chemotherapy Platinum-Resistance |
| title | CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study |
| title_full | CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study |
| title_fullStr | CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study |
| title_full_unstemmed | CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study |
| title_short | CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study |
| title_sort | ct based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma a multicentre study |
| topic | Epithelial ovarian carcinoma Computed tomography Radiomics Chemotherapy Platinum-Resistance |
| url | https://doi.org/10.1186/s40644-025-00906-9 |
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