Predicting NRAS gene status in colorectal cancer using computed tomography (CT)-based radiomics.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
151 patients assigned to the training set and 65 to the test set.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Clinical decision curve analysis demonstrated the clinical value of radiomics in predicting NRAS status. [CONCLUSION] The CT-based radiomics model effectively predicts NRAS gene status in colorectal cancer patients.
[AIM] Establish and validate a radiomics models based on computed tomography (CT) images to predict NRAS status in colorectal cancer patients.
- p-value P < 0.05
- 95% CI 0.730-0.876
APA
Yang Y, Zhang C, et al. (2025). Predicting NRAS gene status in colorectal cancer using computed tomography (CT)-based radiomics.. Clinical radiology, 91, 107116. https://doi.org/10.1016/j.crad.2025.107116
MLA
Yang Y, et al.. "Predicting NRAS gene status in colorectal cancer using computed tomography (CT)-based radiomics.." Clinical radiology, vol. 91, 2025, pp. 107116.
PMID
41187697
Abstract
[AIM] Establish and validate a radiomics models based on computed tomography (CT) images to predict NRAS status in colorectal cancer patients.
[MATERIALS AND METHODS] Clinical and imaging characteristics associated with NRAS status were analysed. Imaging data from 216 colorectal cancer patients at hospital A were used, with 151 patients assigned to the training set and 65 to the test set. Regions of interest (ROIs) were delineated on enhanced venous phase CT images using ITK-SNAP. Features were selected based on analysis of variance, with logistic regression as the classifier. Five-fold cross-validation was applied to ensure model stability and generalisability. The Hosmer-Lemeshow test was used to evaluate the calibration curve, while clinical decision curve analysis assessed the clinical utility of the model.
[RESULTS] No significant association was found between clinical features and NRAS gene status (P < 0.05). Five radiomic features were selected for model construction. The model achieved an area under the curve (AUC) of 0.803 (95% confidence interval [CI]: 0.750-0.864) in the training set and 0.766 (95% CI: 0.730-0.876) in the test set. Calibration curves demonstrated excellent agreement between the predicted and actual probabilities in both the training and test sets, supported by nonstatistically significant differences in the Hosmer-Lemeshow test (χ = 6.727, P = 0.56; χ = 4.854, P = 0.77). Clinical decision curve analysis demonstrated the clinical value of radiomics in predicting NRAS status.
[CONCLUSION] The CT-based radiomics model effectively predicts NRAS gene status in colorectal cancer patients.
[MATERIALS AND METHODS] Clinical and imaging characteristics associated with NRAS status were analysed. Imaging data from 216 colorectal cancer patients at hospital A were used, with 151 patients assigned to the training set and 65 to the test set. Regions of interest (ROIs) were delineated on enhanced venous phase CT images using ITK-SNAP. Features were selected based on analysis of variance, with logistic regression as the classifier. Five-fold cross-validation was applied to ensure model stability and generalisability. The Hosmer-Lemeshow test was used to evaluate the calibration curve, while clinical decision curve analysis assessed the clinical utility of the model.
[RESULTS] No significant association was found between clinical features and NRAS gene status (P < 0.05). Five radiomic features were selected for model construction. The model achieved an area under the curve (AUC) of 0.803 (95% confidence interval [CI]: 0.750-0.864) in the training set and 0.766 (95% CI: 0.730-0.876) in the test set. Calibration curves demonstrated excellent agreement between the predicted and actual probabilities in both the training and test sets, supported by nonstatistically significant differences in the Hosmer-Lemeshow test (χ = 6.727, P = 0.56; χ = 4.854, P = 0.77). Clinical decision curve analysis demonstrated the clinical value of radiomics in predicting NRAS status.
[CONCLUSION] The CT-based radiomics model effectively predicts NRAS gene status in colorectal cancer patients.
🏷️ 키워드 / MeSH
같은 제1저자의 인용 많은 논문 (5)
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