T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study
Abstract Background Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderli...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-08-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-022-01264-x |
| _version_ | 1811321000313225216 |
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| author | Mingxiang Wei Yu Zhang Genji Bai Cong Ding Haimin Xu Yao Dai Shuangqing Chen Hong Wang |
| author_facet | Mingxiang Wei Yu Zhang Genji Bai Cong Ding Haimin Xu Yao Dai Shuangqing Chen Hong Wang |
| author_sort | Mingxiang Wei |
| collection | DOAJ |
| description | Abstract Background Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderline EOTs preoperatively. Methods A total of 417 patients (309, 78, and 30 samples in the training and internal and external validation sets) with pathologically proven benign and borderline EOTs were included in this multicenter study. In total, 1130 radiomics features were extracted from manually delineated tumor volumes of interest on images. The following three different models were constructed and evaluated: radiomics features only (radiomics model); clinical and radiological characteristics only (clinic-radiological model); and a combination of them all (combined model). The diagnostic performances of models were assessed using receiver operating characteristic (ROC) analysis, and area under the ROC curves (AUCs) were compared using the DeLong test. Results The best machine learning algorithm to distinguish borderline from benign EOTs was the logistic regression. The combined model achieved the best performance in discriminating between benign and borderline EOTs, with an AUC of 0.86 ± 0.07. The radiomics model showed a moderate AUC of 0.82 ± 0.07, outperforming the clinic-radiological model (AUC of 0.79 ± 0.06). In the external validation set, the combined model performed significantly better than the clinic-radiological model (AUCs of 0.86 vs. 0.63, p = 0.021 [DeLong test]). Conclusions Radiomics, based on T2-weighted MRI, can provide critical diagnostic information for discriminating between benign and borderline EOTs, thus having the potential to aid personalized treatment options. |
| first_indexed | 2024-04-13T13:10:29Z |
| format | Article |
| id | doaj.art-299f84c495a04538b8ff4d63d603244b |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-04-13T13:10:29Z |
| publishDate | 2022-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-299f84c495a04538b8ff4d63d603244b2022-12-22T02:45:38ZengSpringerOpenInsights into Imaging1869-41012022-08-0113111110.1186/s13244-022-01264-xT2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter studyMingxiang Wei0Yu Zhang1Genji Bai2Cong Ding3Haimin Xu4Yao Dai5Shuangqing Chen6Hong Wang7Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical UniversityDepartment of Radiology, Dushu Lake Hospital Affiliated to Soochow UniversityDepartment of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical UniversityDepartment of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical UniversityDepartment of Radiology, Dushu Lake Hospital Affiliated to Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical UniversityDepartment of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical UniversityAbstract Background Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderline EOTs preoperatively. Methods A total of 417 patients (309, 78, and 30 samples in the training and internal and external validation sets) with pathologically proven benign and borderline EOTs were included in this multicenter study. In total, 1130 radiomics features were extracted from manually delineated tumor volumes of interest on images. The following three different models were constructed and evaluated: radiomics features only (radiomics model); clinical and radiological characteristics only (clinic-radiological model); and a combination of them all (combined model). The diagnostic performances of models were assessed using receiver operating characteristic (ROC) analysis, and area under the ROC curves (AUCs) were compared using the DeLong test. Results The best machine learning algorithm to distinguish borderline from benign EOTs was the logistic regression. The combined model achieved the best performance in discriminating between benign and borderline EOTs, with an AUC of 0.86 ± 0.07. The radiomics model showed a moderate AUC of 0.82 ± 0.07, outperforming the clinic-radiological model (AUC of 0.79 ± 0.06). In the external validation set, the combined model performed significantly better than the clinic-radiological model (AUCs of 0.86 vs. 0.63, p = 0.021 [DeLong test]). Conclusions Radiomics, based on T2-weighted MRI, can provide critical diagnostic information for discriminating between benign and borderline EOTs, thus having the potential to aid personalized treatment options.https://doi.org/10.1186/s13244-022-01264-xOvaryRadiomicsMachine learningMagnetic resonance imaging |
| spellingShingle | Mingxiang Wei Yu Zhang Genji Bai Cong Ding Haimin Xu Yao Dai Shuangqing Chen Hong Wang T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study Insights into Imaging Ovary Radiomics Machine learning Magnetic resonance imaging |
| title | T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study |
| title_full | T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study |
| title_fullStr | T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study |
| title_full_unstemmed | T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study |
| title_short | T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study |
| title_sort | t2 weighted mri based radiomics for discriminating between benign and borderline epithelial ovarian tumors a multicenter study |
| topic | Ovary Radiomics Machine learning Magnetic resonance imaging |
| url | https://doi.org/10.1186/s13244-022-01264-x |
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