Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, P. R. China.
Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, P. R. China.
J Cancer Res Clin Oncol. 2024 Sep 9;150(9):415. doi: 10.1007/s00432-024-05930-z.
To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.
357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.
The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).
The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.
构建一个整合放射组学和病理组学的模型，用于预测非转移性鼻咽癌（NPC）患者的无进展生存期（PFS）。
本研究纳入了 357 例接受治疗前 MRI 和病理全切片成像（WSI）检查的 NPC 患者，将其随机分为训练集（n=250）和验证集（n=107）。使用最小冗余最大相关性和最小绝对收缩和选择算子方法从 MRI 中提取放射组学特征。基于 WSI 的病理组学特征使用 Swin Transformer 深度学习架构构建。通过纳入三个特征集：放射组学特征、病理组学特征和独立临床因素，构建放射病理组学模型。使用一致性指数（C-index）评估模型的预后效能。通过对数秩检验测试分层风险组的 Kaplan-Meier 曲线。
与任何单一模态模型（放射组学：0.619，95%置信区间[CI]：0.553-0.706；病理组学：0.732，95%CI：0.662-0.802；临床模型：0.655，95%CI：0.581-0.728）相比，放射病理组学模型在训练集和验证集中的预测性能均更优，C 指数分别为 0.791（95%CI：0.724-0.871）和 0.785（95%CI：0.716-0.875）（均 P<0.05）。放射病理组学模型在训练集和验证集中均能有效将患者分为高风险和低风险组（均 P<0.001）。
所开发的放射病理组学模型在预测 NPC 患者的 PFS 方面具有可靠性。它能够有效地将个体患者分为不同的风险组，为预后评估提供有价值的信息。
