Research on prediction of pathological complete response after neoadjuvant therapy for rectal cancer based on MRI high-resolution T2WI images
10.3760/cma.j.cn371439-20200527-00084
- VernacularTitle:基于磁共振高分辨T2WI影像组学预测直肠癌新辅助治疗后病理完全反应的研究
- Author:
Haidi LU
1
;
Fu SHEN
;
Jianping LU
;
Liqiang HAO
Author Information
1. 海军军医大学附属长海医院医学影像科，上海 200433
- From:
Journal of International Oncology
2020;47(10):593-597
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of MRI high-resolution T2WI based-radiomics in predicting pathologic complete response (pCR) after neoadjuvant therapy for rectal cancer.Methods:This retrospective study included 80 patients with rectal cancer confirmed by postoperative pathology, who underwent high-resolution imaging of rectal MRI before neoadjuvant therapy from January 2018 to March 2019 in our hospital. After manually delineating the volume of interest (VOI) of the lesion in the high-resolution T2WI image, the radiomics features were extracted, and the least absolute shrinkage and selection operator (LASSO) algorithm was adopted to reduce the dimension and select the features that were valuable for tumor pCR. Using Random algorithm, the data were randomly divided into training set ( n=64) and test set ( n=16) for machine learning, and 4 kinds of machine learning models including decision tree (DT), logistic regression (LR), random forests (RF) and extreme gradient boosting (XGBoost) were established and ROC curves were drawn. The area under the curve (AUC), sensitivity, specificity and 95% CI were respectively calculated, and the difference of ROC curves was compared with DeLong test. Results:Among 80 patients with rectal cancer, there were 15 cases by pCR, accounting for 18.75%, and 65 cases were non-pCR, accounting for 81.25%. A total of 1 409 imaging features were extracted. After dimension reduction by LASSO algorithm, 8 most valuable features were selected. The AUC of DT, LR, RF and XGBoost in the test set group was 0.870, 0.801, 0.912, 0.945, the AUC of XGBoost was the largest, and the differences between XGBoost and DT, LR, RF were statistically significant ( P=0.008; P=0.006; P=0.009), and the pairwise comparisons of DT, LR, RF showed no statistically significant difference ( PLR-RF=0.083; PDT-LR=0.113; PDT-RF=0.879). The sensitivity was 78.57%, 64.29%, 78.57%, 85.71%, and the specificity was 95.38%, 84.62%, 92.31%, 98.46% respectively. The 95% CI was 0.775-0.935, 0.696-0.882, 0.827-0.964, 0.870-0.984. Conclusion:The radiomics based on high-resolution T2WI images has predictive value for pCR after neoadjuvant treatment of rectal cancer. XGBoost model has better predictive efficiency than DT, LR and RF, and can be used to guide clinical individualized treatment and related interventions.
