Prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer using contrast-enhanced ultrasound radiomics
10.3760/cma.j.cn131148-20230730-00040
- VernacularTitle:基于超声造影影像组学预测局部进展期直肠癌新辅助放化疗病理完全缓解的应用研究
- Author:
Qiong QIN
1
;
Yuquan WU
;
Rong WEN
;
Xiumei BAI
;
Ruizhi GAO
;
Yadan LIN
;
Jiayi LYU
;
Yun HE
;
Hong YANG
Author Information
1. 广西医科大学第一附属医院超声医学科，南宁 530021
- Keywords:
Contrast-enhanced ultrasound;
Radiomics;
Locally advanced rectal cancer;
Neoadjuvant chemoradiotherapy;
Pathological complete response
- From:
Chinese Journal of Ultrasonography
2024;33(1):63-70
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.
