Chen Xijie, Chen Junguo, He Xiaosheng, Xu Liang, Liu Wei, Lin Dezheng, Luo Yuxuan, Feng Yue, Lian Lei, Hu Jiancong, Lan Ping
Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Front Physiol. 2022 Apr 27;13:880981. doi: 10.3389/fphys.2022.880981. eCollection 2022.
Although the wait and watch (W&W) strategy is a treatment choice for locally advanced rectal cancer (LARC) patients who achieve clinical complete response (cCR) after neoadjuvant therapy (NT), the issue on consistency between cCR and pathological CR (pCR) remains unsettled. Herein, we aimed to develop a deep convolutional neural network (DCNN) model using endoscopic images of LARC patients after NT to distinguish tumor regression grade (TRG) 0 from non-TRG0, thus providing strength in identifying surgery candidates. A total of 1000 LARC patients (6,939 endoscopic images) who underwent radical surgery after NT from April 2013 to April 2021 at the Sixth Affiliated Hospital, Sun Yat-sen University were retrospectively included in our study. Patients were divided into three cohorts in chronological order: the training set for constructing the model, the validation set, and the independent test set for validating its predictive capability. Besides, we compared the model's performance with that of three endoscopists on a class-balanced, randomly selected subset of 20 patients' LARC images (10 TRG0 patients with 70 images and 10 non-TRG0 patients with 72 images). The measures used to evaluate the efficacy for identifying TRG0 included overall accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). There were 219 (21.9%) cases of TRG0 in the included patients. The constructed DCNN model in the training set obtained an excellent performance with good accuracy of 94.21%, specificity of 94.39%, NPV of 98.11%, and AUROC of 0.94. The validation set showed accuracy, specificity, NPV, and AUROC of 92.13%, 93.04%, 96.69%, and 0.95, respectively; the corresponding values in the independent set were 87.14%, 92.98%, 91.37%, and 0.77, respectively. In the reader study, the model outperformed the three experienced endoscopists with an AUROC of 0.85. The proposed DCNN model achieved high specificity and NPV in detecting TRG0 LARC tumors after NT, with a better performance than experienced endoscopists. As a supplement to radiological images, this model may serve as a useful tool for identifying surgery candidates in LARC patients after NT.
尽管等待观察（W&W）策略是局部晚期直肠癌（LARC）患者新辅助治疗（NT）后达到临床完全缓解（cCR）时的一种治疗选择，但cCR与病理完全缓解（pCR）之间的一致性问题仍未解决。在此，我们旨在利用NT后LARC患者的内镜图像开发一种深度卷积神经网络（DCNN）模型，以区分肿瘤退缩分级（TRG）0和非TRG0，从而为识别手术候选者提供助力。我们回顾性纳入了2013年4月至2021年4月在中山大学附属第六医院接受NT后根治性手术的1000例LARC患者（6939张内镜图像）。患者按时间顺序分为三个队列：用于构建模型的训练集、验证集以及用于验证其预测能力的独立测试集。此外，我们在20例患者的LARC图像（10例TRG0患者有70张图像，10例非TRG0患者有72张图像）的类平衡随机选择子集中，将该模型的性能与三位内镜医师的性能进行了比较。用于评估识别TRG0有效性的指标包括总体准确率、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）以及受试者工作特征曲线下面积（AUROC）。纳入患者中共有219例（21.9%）TRG0病例。训练集中构建的DCNN模型表现出色，准确率达94.21%，特异性为94.39%，NPV为98.11%，AUROC为0.94。验证集的准确率、特异性、NPV和AUROC分别为92.13%、93.04%、96.69%和0.95；独立测试集中的相应值分别为87.14%、92.98%、91.37%和0.77。在读者研究中，该模型的AUROC为0.85，优于三位经验丰富的内镜医师。所提出的DCNN模型在检测NT后TRG0的LARC肿瘤方面具有高特异性和NPV，性能优于经验丰富的内镜医师。作为对放射图像的补充，该模型可作为识别NT后LARC患者手术候选者的有用工具。
