文章：

基于增强CT的深度学习系统用于术前预测结直肠癌分期及RAS突变状态

A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation

原文发布日期：10 September 2023

DOI: 10.3390/cancers15184497

类型: Article

开放获取: 是

英文摘要：

Purpose: This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients’ preoperative staging and RAS gene mutation status. Methods: The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset. Results: In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images. Conclusion: In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients’ chances of survival and quality of life.

摘要翻译： 

目的：本研究旨在构建一个基于增强计算机断层扫描（CT）门静脉期图像的深度学习系统，用于预测结直肠癌患者的术前分期及RAS基因突变状态。方法：增强CT图像数据集包含231例结直肠癌患者回顾性队列的CT门静脉期图像。通过迁移学习开发了用于结直肠癌检测、分期及RAS基因突变状态预测的深度学习系统。本研究采用了预训练的Yolov7、视觉变换器（VIT）、滑动窗口变换器（SWT）、EfficientNetV2和ConvNeXt模型。肿瘤识别与分期数据集包含4620幅增强CT图像及标注的肿瘤边界框，RAS基因突变状态预测数据集共包含19,700幅增强CT图像。结果：在验证队列中，基于Yolov7的检测模型以0.98的平均精度均值（交并比阈值为0.5）实现了肿瘤检测与分期。基于VIT的预测模型在测试集和验证集中预测RAS基因突变状态的受试者工作特征曲线下面积分别为0.9591和0.9554。该深度学习系统的检测网络与预测网络在解释增强CT图像方面表现出优异性能。结论：本研究基于增强CT门静脉期成像构建的深度学习系统，能够术前预测结直肠癌患者的分期及RAS突变状态。该系统将有助于临床医生选择最佳治疗方案，从而提高结直肠癌患者的生存机会与生活质量。

原文链接：

A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation