Journal of cancer research and clinical oncology · 2024
Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics.
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Yang Yanlin, Wang Haoru, Si Jiajun, Zhang Li, Ding Hao, Wang Fang, He Ling, Chen Xin
Аннотация
OBJECTIVE: To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma.
METHODS: Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical-radiomics model incorporated both clinical and radiomics features.
RESULTS: The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563-0.845), 0.830 (95% CI: 0.704-0.959), and 0.874 (95% CI: 0.768-0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616-0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively.
CONCLUSION: CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.
METHODS: Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical-radiomics model incorporated both clinical and radiomics features.
RESULTS: The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563-0.845), 0.830 (95% CI: 0.704-0.959), and 0.874 (95% CI: 0.768-0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616-0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively.
CONCLUSION: CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.