Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features
PurposeTo develop and validate computed tomography (CT)-based intratumoral and peritumoral radiomics signatures for preoperative prediction of lymph node metastasis (LNM) in patients with ovarian cancer (OC).MethodsPatients with pathological diagnosis of OC were retrospectively included. Intratumora...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1543873/full |
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| author | Jing Zhang Qiyuan Li Haoyu Liang Yao Wang Li Sun Qingyuan Zhang Chuanping Gao |
| author_facet | Jing Zhang Qiyuan Li Haoyu Liang Yao Wang Li Sun Qingyuan Zhang Chuanping Gao |
| author_sort | Jing Zhang |
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| description | PurposeTo develop and validate computed tomography (CT)-based intratumoral and peritumoral radiomics signatures for preoperative prediction of lymph node metastasis (LNM) in patients with ovarian cancer (OC).MethodsPatients with pathological diagnosis of OC were retrospectively included. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral, peritumoral, and combined radiomics signatures were constructed, and their radiomics scores were calculated. Univariate and multivariate logistic regression analyses were performed to identify predictors of clinical outcomes. A radiomics nomogram was developed by incorporating the combined radiomics signature with clinical risk factors. The prediction efficiency of the various models was evaluated using the accuracy value, the area under the receiver-operating characteristic curve (AUC) and decision curve analysis (DCA).ResultsTwo hundred and seventy-three patients with OC were enrolled and randomly divided into a training cohort (n=190) and a test cohort (n=83) in a 7:3 ratio. The intratumoral, peritumoral, and combined radiomics signatures were constructed using 18, 11, and 17 radiomics features, respectively. The combined radiomics signature showed the best prediction ability, with accuracy of 0.783 and an AUC of 0.860 (95% confidence interval 0.779–0.941). The DCA results showed that the combined radiomics signature had better clinical application than the clinical model and the radiomics nomogram.ConclusionsA CT-based combined radiomics signature incorporating intratumoral and peritumoral radiomics features can predict LNM in patients with OC before surgery. |
| format | Article |
| id | doaj-art-4eef65fa845e444daee49fa813faa03e |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-4eef65fa845e444daee49fa813faa03e2025-08-20T03:49:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-05-011510.3389/fonc.2025.15438731543873Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics featuresJing Zhang0Qiyuan Li1Haoyu Liang2Yao Wang3Li Sun4Qingyuan Zhang5Chuanping Gao6Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaHuashan Hospital, Fudan University, Shanghai, ChinaDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, ChinaPurposeTo develop and validate computed tomography (CT)-based intratumoral and peritumoral radiomics signatures for preoperative prediction of lymph node metastasis (LNM) in patients with ovarian cancer (OC).MethodsPatients with pathological diagnosis of OC were retrospectively included. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral and peritumoral radiomics features were extracted from contrast-enhanced CT images. Intratumoral, peritumoral, and combined radiomics signatures were constructed, and their radiomics scores were calculated. Univariate and multivariate logistic regression analyses were performed to identify predictors of clinical outcomes. A radiomics nomogram was developed by incorporating the combined radiomics signature with clinical risk factors. The prediction efficiency of the various models was evaluated using the accuracy value, the area under the receiver-operating characteristic curve (AUC) and decision curve analysis (DCA).ResultsTwo hundred and seventy-three patients with OC were enrolled and randomly divided into a training cohort (n=190) and a test cohort (n=83) in a 7:3 ratio. The intratumoral, peritumoral, and combined radiomics signatures were constructed using 18, 11, and 17 radiomics features, respectively. The combined radiomics signature showed the best prediction ability, with accuracy of 0.783 and an AUC of 0.860 (95% confidence interval 0.779–0.941). The DCA results showed that the combined radiomics signature had better clinical application than the clinical model and the radiomics nomogram.ConclusionsA CT-based combined radiomics signature incorporating intratumoral and peritumoral radiomics features can predict LNM in patients with OC before surgery.https://www.frontiersin.org/articles/10.3389/fonc.2025.1543873/fullovarian cancerlymph node metastasisradiomicstomography - methodsx-ray computed |
| spellingShingle | Jing Zhang Qiyuan Li Haoyu Liang Yao Wang Li Sun Qingyuan Zhang Chuanping Gao Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features Frontiers in Oncology ovarian cancer lymph node metastasis radiomics tomography - methods x-ray computed |
| title | Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features |
| title_full | Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features |
| title_fullStr | Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features |
| title_full_unstemmed | Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features |
| title_short | Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features |
| title_sort | preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast enhanced computed tomography based intratumoral and peritumoral radiomics features |
| topic | ovarian cancer lymph node metastasis radiomics tomography - methods x-ray computed |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1543873/full |
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