Cancer Center, Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Longcheng Street No.99, Taiyuan, China.
Cancer Center, Tongji Shanxi Hospital, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Cancer Imaging. 2024 Oct 24;24(1):144. doi: 10.1186/s40644-024-00789-2.
This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.
We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).
Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.
The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.
本研究旨在开发和验证一种预测模型，该模型将临床特征、MRI 放射组学和营养-炎症生物标志物相结合，以预测接受同期放化疗（CCRT）的宫颈癌（CC）患者的无进展生存期（PFS）。目的是识别高危患者并指导个体化治疗。
我们对来自两个中心的 188 名患者进行了回顾性分析，分为训练集（132 名）和验证集（56 名）。收集了临床数据、系统炎症标志物和免疫营养指数。从三个 MRI 序列中提取放射组学特征，并选择具有预测价值的特征。我们使用 C 指数开发和评估了包含临床特征、营养-炎症指标和放射组学的五个模型。使用最佳表现模型创建了一个列线图，并通过 ROC 曲线、校准图和决策曲线分析（DCA）进行验证。
模型 5 整合了临床特征、全身免疫炎症指数（SII）、预后营养指数（PNI）和 MRI 放射组学，表现出最高的性能。它在训练集和验证集中的 C 指数分别为 0.833（95%CI：0.792-0.874）和 0.789（95%CI：0.679-0.899）。来自模型 5 的列线图有效地将患者分为风险组，在训练集中，1 年、3 年和 5 年 PFS 的 AUC 分别为 0.833、0.941 和 0.973，在验证集中，1 年、3 年和 5 年 PFS 的 AUC 分别为 0.812、0.940 和 0.944。
将临床特征、营养-炎症生物标志物和放射组学相结合的综合模型为预测接受 CCRT 的 CC 患者的 PFS 提供了一种强大的工具。列线图提供了精确的预测，支持其在个体化患者管理中的应用。
