Lai Penghao, Chen Xiaobo, Pei Wei, Huang Xia, Fang Zhen, Yang Fan, Ying Yujie, Song Yaxuan, Jin Weifeng, Lu Shaolu, Lu Bingfeng, Liao Hai
The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Quant Imaging Med Surg. 2025 Jun 6;15(6):5347-5361. doi: 10.21037/qims-24-1860. Epub 2025 May 27.
Locally advanced nasopharyngeal carcinoma (LANPC) is a common malignant tumor of the nasopharynx, characterized by poor prognosis and a high susceptibility to recurrence and metastasis after surgery. The aim of this study was to establish and validate a radiomics model based on clinicopathological data and magnetic resonance imaging (MRI) information to predict progression-free survival (PFS) in LANPC patients, and to reveal the internal prediction process of the model through SHapley Additive exPlanation (SHAP) and image visualization techniques.
A total of 1,098 patients with pathologically and clinically diagnosed LANPC were recruited from three hospitals {training, n=700 [70% from hospitals I (Guangxi Medical University Cancer Hospital) and II (Wuzhou Red Cross Hospital)]; internal validation, n=300 (remaining 30%); and external validation, n=98 [hospital III (The Second Affiliated Hospital of Guangxi Medical University)]}. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select radiomics features. A combined model integrating the radiomics score (radscore) and important clinicopathological factors was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm. SHAP and image visualization techniques were used for interpretability analysis of prognostic models.
Optimal predictive performance was observed in the combined model, which integrated the radscore and important clinicopathological factors [induction chemotherapy (IC), Epstein-Barr virus (EBV)-DNA, and albumin], with Harrell concordance index (C-index) values of 0.762, 0.729, and 0.752 in the training, internal, and external validation cohorts, respectively. Ten radiomics features with the highest predictive contributions were identified using the SHAP algorithm. Two LANPC patients with similar clinicopathological stages but distinct risk levels were selected to visualize the top three radiomics features, revealing notable pixel-level visual differences in the largest layer of the tumor images. Kaplan-Meier survival analysis revealed prognostic differences between low- and high-risk groups, and the model's performance was stable across subgroups (all log-rank P<0.05).
The interpretable model was able to accurately predict the PFS in LANPC patients. SHAP and image visualization techniques provided quantitative contribution values and image-level radiomics information, which could provide valuable additional information for individualized prognostic evaluation and clinical decision-making for patients with LANPC.
局部晚期鼻咽癌（LANPC）是一种常见的鼻咽部恶性肿瘤，其特点是预后较差，术后复发和转移的易感性较高。本研究的目的是建立并验证一种基于临床病理数据和磁共振成像（MRI）信息的放射组学模型，以预测LANPC患者的无进展生存期（PFS），并通过SHapley加性解释（SHAP）和图像可视化技术揭示该模型的内部预测过程。
从三家医院招募了1098例经病理和临床诊断为LANPC的患者{训练组，n = 700 [70%来自医院I（广西医科大学附属肿瘤医院）和医院II（梧州市红十字会医院）]；内部验证组，n = 300（其余30%）；外部验证组，n = 98 [医院III（广西医科大学第二附属医院）]}。采用单因素Cox分析和最小绝对收缩和选择算子（LASSO）Cox回归来选择放射组学特征。使用极端梯度提升（XGBoost）算法构建了一个整合放射组学评分（radscore）和重要临床病理因素的联合模型。采用SHAP和图像可视化技术对预后模型进行可解释性分析。
在整合了radscore和重要临床病理因素[诱导化疗（IC）、爱泼斯坦-巴尔病毒（EBV）-DNA和白蛋白]的联合模型中观察到了最佳预测性能，训练组、内部验证组和外部验证组的Harrell一致性指数（C-index）值分别为0.762、0.729和0.752。使用SHAP算法确定了预测贡献最高的10个放射组学特征。选择了两名临床病理分期相似但风险水平不同的LANPC患者，以可视化排名前三的放射组学特征，揭示了肿瘤图像最大层面上显著的像素级视觉差异。Kaplan-Meier生存分析显示低风险组和高风险组之间存在预后差异，并且该模型在各亚组中的性能稳定（所有对数秩P<0.05）。
该可解释模型能够准确预测LANPC患者的PFS。SHAP和图像可视化技术提供了定量贡献值和图像级放射组学信息，可为LANPC患者的个体化预后评估和临床决策提供有价值的额外信息。
