A computed tomography-based radiomics prediction model for BRAF mutation status in colorectal cancer.
Purpose: The aim of this study was to develop and validate CT venous phase image-based radiomics to predict BRAF gene mutation status in preoperative colorectal cancer patients. Methods: In this study, 301 patients with pathologically confirmed colorectal cancer were retrospectively enrolled, compri...
| Publicado en: | Abdominal Radiology Vol. 50; no. 11; pp. 5162 - 5175 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2025
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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=188952033&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188952033 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2025 vid: 50 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188952033 185144830 10.1007/s00261-025-04983-z 188952033 ppf: 5162 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A computed tomography-based radiomics prediction model for BRAF mutation status in colorectal cancer. aug: au: Zhou, Boqi Tan, Huaqing Wang, Yuxuan Huang, Bin Wang, Zhijie Zhang, Shihui Zhu, Xiaobo Wang, Zhan Zhou, Junlin Cao, Yuntai affil: https://ror.org/000j1tr86 Qinghai University Affiliated Hospital, Xining, China sug: ab: Purpose: The aim of this study was to develop and validate CT venous phase image-based radiomics to predict BRAF gene mutation status in preoperative colorectal cancer patients. Methods: In this study, 301 patients with pathologically confirmed colorectal cancer were retrospectively enrolled, comprising 225 from Centre I (73 mutant and 152 wild-type) and 76 from Centre II (36 mutant and 40 wild-type). The Centre I cohort was randomly divided into a training set (n = 158) and an internal validation set (n = 67) in a 7:3 ratio, while Centre II served as an independent external validation set (n = 76). The whole tumor region of interest was segmented, and radiomics characteristics were extracted. To explore whether tumor expansion could improve the performance of the study objectives, the tumor contour was extended by 3 mm in this study. Finally, a t-test, Pearson correlation, and LASSO regression were used to screen out features strongly associated with BRAF mutations. Based on these features, six classifiers—Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were constructed. The model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves, decision curve analysis, accuracy, sensitivity, and specificity. Results: Gender was an independent predictor of BRAF mutations. The unexpanded RF model, constructed using 11 imaging histologic features, demonstrated the best predictive performance. For the training cohort, it achieved an AUC of 0.814 (95% CI 0.732–0.895), an accuracy of 0.810, and a sensitivity of 0.620. For the internal validation cohort, it achieved an AUC of 0.798 (95% CI 0.690–0.907), an accuracy of 0.761, and a sensitivity of 0.609. For the external validation cohort, it achieved an AUC of 0.737 (95% CI 0.616–0.847), an accuracy of 0.658, and a sensitivity of 0.667. Conclusions: A machine learning model based on CT radiomics can effectively predict BRAF mutations in patients with colorectal cancer. The unexpanded RF model demonstrated optimal predictive performance. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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