Hua Hong-Li, Li Song, Huang Huan, Zheng Yong-Fa, Li Fen, Li Sheng-Lan, Deng Yu-Qin, Tao Ze-Zhang
Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
Quant Imaging Med Surg. 2023 Jun 1;13(6):3569-3586. doi: 10.21037/qims-22-1226. Epub 2023 May 4.
Concurrent chemoradiotherapy (CCRT) and induction chemotherapy (IC) plus CCRT (IC + CCRT) are the main treatments for patients with advanced nasopharyngeal carcinoma (NPC). We aimed to develop deep learning (DL) models using magnetic resonance (MR) imaging to predict the risk of residual tumor after each of the 2 treatments and to provide a reference for patients to select the best treatment option.
A retrospective study was conducted on 424 patients with locoregionally advanced NPC who underwent CCRT or IC + CCRT between June 2012 and June 2019 in the Renmin Hospital of Wuhan University. According to the evaluation of MR images taken 3 to 6 months after radiotherapy, patients were divided into 2 categories: residual tumor and non-residual tumor. Transferred U-net and Deeplabv3 neural networks were trained, and the better-performance segmentation model was used to segment the tumor area on axial T1-weighted enhanced MR images. Then, 4 pretrained neural networks for prediction of residual tumors were trained with CCRT and IC + CCRT datasets, and the performances of the models trained using each image and each patient as a unit were evaluated. Patients in the test cohort of CCRT and IC + CCRT datasets were successively classified by the trained CCRT and IC + CCRT models. Model recommendations were formed according to the classification and compared with the treatment decisions of physicians.
The Dice coefficient of Deeplabv3 (0.752) was higher than that of U-net (0.689). The average area under the curve (aAUC) of the 4 networks was 0.728 for the CCRT and 0.828 for the IC + CCRT models trained using a single image as a unit, whereas the aAUC for models trained using each patient as a unit was 0.928 for the CCRT and 0.915 for the IC + CCRT models, respectively. The accuracy of the model recommendation and the decision of physicians was 84.06% and 60.00%, respectively.
The proposed method can effectively predict the residual tumor status of patients after CCRT and IC + CCRT. Recommendations based on the model prediction results can protect some patients from receiving additional IC and improve the survival rate of patients with NPC.
同步放化疗（CCRT）以及诱导化疗（IC）联合CCRT（IC + CCRT）是晚期鼻咽癌（NPC）患者的主要治疗方法。我们旨在利用磁共振（MR）成像开发深度学习（DL）模型，以预测这两种治疗方法各自实施后残留肿瘤的风险，并为患者选择最佳治疗方案提供参考。
对2012年6月至2019年6月期间在武汉大学人民医院接受CCRT或IC + CCRT治疗的424例局部晚期NPC患者进行了一项回顾性研究。根据放疗后3至6个月所拍摄MR图像的评估结果，将患者分为两类：残留肿瘤和无残留肿瘤。对迁移的U-net和Deeplabv3神经网络进行训练，并使用性能更佳的分割模型在轴向T1加权增强MR图像上分割肿瘤区域。然后，使用CCRT和IC + CCRT数据集对4个用于预测残留肿瘤的预训练神经网络进行训练，并评估以每张图像和每位患者为单位训练的模型的性能。CCRT和IC + CCRT数据集测试队列中的患者先后由训练好的CCRT和IC + CCRT模型进行分类。根据分类结果形成模型推荐，并与医生的治疗决策进行比较。
Deeplabv3的Dice系数（0.752）高于U-net（0.689）。以单张图像为单位训练的CCRT模型和IC + CCRT模型中，4个网络的平均曲线下面积（aAUC）分别为0.728和0.828，而以每位患者为单位训练的CCRT模型和IC + CCRT模型的aAUC分别为0.928和0.915。模型推荐与医生决策的准确率分别为84.06%和60.00%。
所提出的方法能够有效预测CCRT和IC + CCRT治疗后患者的残留肿瘤状态。基于模型预测结果的推荐可以使部分患者避免接受额外的IC治疗，并提高NPC患者的生存率。
