MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.
Purpose: To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). Methods: A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly di...
| Publicado en: | Abdominal Radiology Vol. 50; no. 4; pp. 1822 - 1831 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184038953&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184038953 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Apr2025 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184038953 180315936 10.1007/s00261-024-04547-7 184038953 ppf: 1822 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden. aug: au: Meng, Xuxu Yang, Dawei Jin, He Xu, Hui Lu, Jun Liu, Zhenhao Wang, Zhenchang Wang, Liang Yang, Zhenghan affil: https://ror.org/013xs5b60 Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China sug: ab: Purpose: To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). Methods: A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. Results: Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. Conclusions: The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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