基于深度学习的预后和预测模型，用于识别Ⅱ期鼻咽癌患者单纯调强放疗的最佳候选者：一项回顾性多中心研究

A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.

作者信息

Liang Jiong-Lin, Wen Yue-Feng, Huang Ying-Ping, Guo Jia, He Yun, Xing Hong-Wei, Guo Ling, Mai Hai-Qiang, Yang Qi

机构信息

State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.

Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China.

出版信息

Radiother Oncol. 2025 Feb;203:110660. doi: 10.1016/j.radonc.2024.110660. Epub 2024 Dec 5.

DOI:10.1016/j.radonc.2024.110660

PMID:39645201

Abstract

PURPOSE

To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom intensity-modulated radiotherapy (IMRT) alone is sufficient.

METHODS

This multicenter, retrospective study enrolled 999 patients with stage II NPC from two centers. 3DResNet was used to extract deep learning MRI features and eXtreme Gradient Boosting model was employed to integrate the pre-trained features and clinical information to obtain an overall score for each patient. Based on the optimal cutoff value of the overall score, patients were stratified into high- and low- risk groups. Model performance was evaluated using concordance indexes (C-indexes), the area under the curve (AUC) values and calibration tests. Survival curves were used to analyze the clinical benefits of additional chemotherapy in each risk group.

RESULTS

The combined model achieved a concordance index of 0.789 (95 % confidence interval [CI] 0.787-0.791), 0.768 (95 % CI 0.764-0.771), and 0.804 (95 % CI 0.801-0.807) for the training, internal validation, and external test cohorts, respectively, demonstrating a statistically significant improvement compared to the MRI model, T Stage, and N Stage. An overall score of < 0.405 in patients was significantly associated with a low risk of progression. In the low-risk group, patients treated with IMRT alone had comparable or even superior progression-free survival (PFS) compared to those who received additional chemotherapy.

CONCLUSION

The model demonstrated a satisfactory prognostic and predictive performance for PFS. Patients with stage II NPC were stratified into different risk groups to help identify optimal candidates who could benefit from IMRT alone.

摘要

目的

开发并验证一种整合深度学习MRI特征和临床信息的预后及预测模型，用于II期鼻咽癌（NPC）患者，以识别进展风险低、单纯调强放疗（IMRT）即可的患者。

方法

这项多中心回顾性研究纳入了来自两个中心的999例II期NPC患者。使用3DResNet提取深度学习MRI特征，并采用极端梯度提升模型整合预训练特征和临床信息，以获得每位患者的综合评分。根据综合评分的最佳截断值，将患者分为高风险和低风险组。使用一致性指数（C指数）、曲线下面积（AUC）值和校准测试评估模型性能。生存曲线用于分析各风险组中额外化疗的临床获益。

结果

联合模型在训练、内部验证和外部测试队列中的一致性指数分别为0.789（95%置信区间[CI]0.787 - 0.791）、0.768（95%CI 0.764 - 0.771）和0.804（95%CI 0.801 - 0.807），与MRI模型、T分期和N分期相比，显示出统计学上的显著改善。患者综合评分<0.405与低进展风险显著相关。在低风险组中，单纯接受IMRT治疗的患者与接受额外化疗的患者相比，无进展生存期（PFS）相当甚至更优。