Clinical value of magnetic resonance imaging based integrated deep learning model for predic-ting the times of linear staplers used in middle-low rectal cancer resection
10.3760/cma.j.cn115610-20230826-00034
- VernacularTitle:基于磁共振成像检查的集成深度学习模型预测中低位直肠癌切除术中直线切割闭合器使用次数的临床价值
- Author:
Zhanwei FU
1
;
Zhenghao CAI
;
Shuchun LI
;
Luyang ZHANG
;
Lu ZANG
;
Feng DONG
;
Minhua ZHENG
;
Junjun MA
Author Information
1. 上海交通大学医学院附属瑞金医院胃肠外科 上海市微创外科临床医学中心，上海 200025
- Keywords:
Rectal neoplasms;
Deep-learning;
Prediction model;
Double stapling tech-nique;
Magnetic resonance imaging
- From:
Chinese Journal of Digestive Surgery
2023;22(9):1129-1138
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the clinical value of magnetic resonance imaging (MRI) based integrated deep learning model for predicting the times of linear staplers used in double stapling technique for middle-low rectal cancer resection.Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients who underwent low anterior resection (LAR) for middle-low rectal cancer in Ruijin Hospital of Shanghai Jiaotong University School of Medicine from January 2018 to December 2022 were collected as training dataset. There were 183 males and 80 females, aged 63(55,68)years. The clinicopathological data of 128 patients with middle-low rectal cancer were collected as validation dataset, including 83 males and 45 females, with age as 65(57,70)years. The training dataset was used to construct the prediction model, and the validation dataset was used to validate the prediction model. Observation indicators: (1) clinicopathological features of patients in the training dataset; (2) influencing factors for ≥3 times using of linear staplers in the operation; (3) prediction model construction; (4) efficiency evaluation of prediction model; (5) validation of prediction model. Measurement data with skewed distribution were represented as M( Q1, Q3), and Mann-Whitney U test was used for comparison between groups. Count data were expressed as absolute numbers, and comparison between groups was conducted using the chi-square test. Wilcoxon rank sum test was used for non-parametric data analysis. Univariate analysis was conducted using the Logistic regression model, and multivariate analysis was conducted using the Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was draw and the area under the curve (AUC) was calculated. The AUC of the ROC curve >0.75 indicated the prediction model as acceptable. Comparison of AUC was conducted using the Delong test. Results:(1) Clinicopathological features of patients in the training dataset. Of the 263 patients, there were 48 cases with linear staplers used in the operation ≥3 times and 215 cases with linear staplers used in the operation ≤2 times. Cases with preoperative serum carcinoembryonic antigen (CEA) >5 μg/L, cases with anastomotic leakage, cases with tumor diameter ≥5 cm were 20, 12, 13 in the 48 cases with linear staplers used ≥3 times in the operation, versus 56, 26, 21 in the 215 cases with linear staplers used ≤2 times in the operation, showing significant differences in the above indicators between them ( χ2=4.66, 5.29, 10.45, P<0.05). (2) Influencing factors for ≥3 times using of linear staplers in the operation. Results of multivariate analysis showed that preoperative serum CEA >5 μg/L and tumor diameter ≥5 cm were independent risk factors for ≥3 times using of linear staplers in the operation ( odds ratio=2.26, 3.39, 95% confidence interval as 1.15-4.43, 1.50-7.65, P<0.05). (3) Prediction model construction. According to the results of multivariate analysis, the clinical prediction model was established as Logit(P)=-2.018+0.814×preoperative serum CEA (>5 μg/L as 1, ≤5 μg/L as 0)+ 1.222×tumor diameter (≥5 cm as 1, <5 cm as 0). The image data segmented by the Mask region convolutional neural network (MASK R-CNN) was input into the three-dimensional convolutional neural network (C3D), and the image prediction model was constructed by training. The image data segmented by the MASK R-CNN and the clinical independent risk factors were input into the C3D, and the integrated prediction model was constructed by training. (4) Efficiency evaluation of prediction model. The sensitivity, specificity and accuracy of the clinical prediction model was 70.0%, 81.0% and 79.4%, respectively, with the Yoden index as 0.51. The sensitivity, specificity and accuracy of the image prediction model was 50.0%, 98.3% and 91.2%, respectively, with the Yoden index as 0.48. The sensitivity, specificity and accuracy of the integrated prediction model was 70.0%, 98.3% and 94.1%, respectively, with the Yoden index as 0.68. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.72(95% confidence interval as 0.61-0.83), 0.81(95% confidence interval as 0.71-0.91) and 0.88(95% confidence interval as 0.81-0.95), respectively. There were significant differences in the efficacy between the integrated prediction model and the image prediction model or the clinical prediction model ( Z=2.98, 2.48, P<0.05). (5) Validation of prediction model. The three prediction models were externally validated by validation dataset. The sensitivity, specificity and accuracy of the clinical prediction model was 62.5%, 66.1% and 65.6%, respectively, with the Yoden index as 0.29. The sensitivity, specificity and accuracy of the image prediction model was 58.8%, 95.5% and 92.1%, respectively, with the Yoden index as 0.64. The sensitivity, specificity and accuracy of the integrated prediction model was 68.8%, 97.3% and 93.8%, respectively, with the Yoden index as 0.66. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.65(95% confidence interval as 0.55-0.75), 0.75(95% confidence interval as 0.66-0.84) and 0.84(95% confidence interval as 0.74-0.93), respec-tively. There was significant differences in the efficacy between the clinical prediction model and the integrated prediction model ( Z=3.24, P<0.05). Conclusion:The MRI-based deep-learning model can help predicting the high-risk population with ≥3 times using of linear staplers in resection of middle-low rectal cancer with double stapling technique.
