Lu Na, Guan Xiao, Zhu Jianguo, Li Yuan, Zhang Jianping
Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China.
Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China.
Cancers (Basel). 2023 Sep 10;15(18):4497. doi: 10.3390/cancers15184497.
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.
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.
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.
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.
本研究旨在构建一个深度学习系统，利用增强计算机断层扫描（CT）门静脉期图像预测结直肠癌患者的术前分期和RAS基因突变状态。
对比增强CT图像数据集包括来自231例结直肠癌患者回顾性队列的CT门静脉期图像。通过迁移学习开发深度学习系统，用于结直肠癌检测、分期和RAS基因突变状态预测。本研究使用了预训练的Yolov7、视觉Transformer（VIT）、Swin Transformer（SWT）、EfficientNetV2和ConvNeXt。肿瘤识别和分期数据集中包含4620张对比增强CT图像以及标注的肿瘤边界框。共有19700张对比增强CT图像构成RAS基因突变状态预测数据集。
在验证队列中，基于Yolov7的检测模型检测和分期肿瘤的平均精度（交并比=0.5）（mAP_0.5）为0.98。基于VIT的预测模型在预测RAS基因的突变状态时，测试集和验证集的受试者操作特征曲线下面积（AUC）分别为0.9591和0.9554。深度学习系统的检测网络和预测网络在解释对比增强CT图像方面表现出色。
在本研究中，基于对比增强CT门静脉期成像建立了一个深度学习系统，用于术前预测结直肠癌患者的分期和RAS突变状态。该系统将帮助临床医生选择最佳治疗方案，以提高结直肠癌患者的生存机会和生活质量。
