Background:Cervical cancer remains a major global health concern, with high recurrence rates in advanced stages. [18F]FDG PET/CT provides prognostic biomarkers such as SUV, MTV, and TLG, though these are not routinely integrated into clinical protocols. Radiomics offers quantitative analysis of tumor heterogeneity, supporting risk stratification.Purpose:To evaluate the prognostic value of clinical and radiomic features for disease-free survival (DFS) in locoregionally advanced cervical cancer using machine learning (ML).Methods:Sixty-three patients (mean age 47.9 ± 14.5 years) were diagnosed between 2015 and 2020. Radiomic features were extracted from pre-treatment PET/CT (IBSI-compliant PyRadiomics). Clinical variables included age, T-stage, Dmax, lymph node involvement, SUVmax, and TMTV. Forty-two models were built by combining six feature-selection techniques (UCI, MD, MI, VH, VH.VIMP, IBMA) with seven ML algorithms (CoxPH, CB, GLMN, GLMB, RSF, ST, EV) using nested 3-fold cross-validation with bootstrap resampling. External validation was performed on 95 patients (mean age 50.6 years, FIGO IIB–IIIB) from an independent cohort with different preprocessing protocols.Results:Recurrence occurred in 31.7% (n= 20). SUVmax of lymph nodes, lymph node involvement, and TMTV were the most predictive individual features (C-index ≤ 0.77). The highest performance was achieved by UCI + EV/GLMB on combined clinical + radiomic features (C-index = 0.80,p< 0.05). For single feature sets, IBMA + RSF performed best for clinical (C-index = 0.72), and VH.VIMP + GLMN for radiomics (C-index = 0.71). External validation confirmed moderate generalizability (best C-index = 0.64).Conclusions:UCI-based feature selection with GLMB or EV yielded the best predictive accuracy, while VH.VIMP + GLMN offered superior external generalizability for radiomics-only models. These findings support the feasibility of integrating radiomics and ML for individualized DFS risk stratification in cervical cancer.
背景：宫颈癌仍是全球重大健康问题，晚期复发率高。[18F]FDG PET/CT可提供SUV、MTV、TLG等预后生物标志物，但这些指标尚未常规纳入临床诊疗方案。影像组学能对肿瘤异质性进行定量分析，为风险分层提供支持。 目的：运用机器学习方法评估临床特征与影像组学特征对局部晚期宫颈癌无病生存期的预后预测价值。 方法：纳入2015-2020年确诊的63例患者（平均年龄47.9±14.5岁）。从治疗前PET/CT图像中提取符合IBSI标准的影像组学特征（使用PyRadiomics工具）。临床变量包括年龄、T分期、肿瘤最大径、淋巴结转移、SUV最大值及肿瘤代谢体积。通过六种特征选择方法（UCI、MD、MI、VH、VH.VIMP、IBMA）与七种机器学习算法（CoxPH、CB、GLMN、GLMB、RSF、ST、EV）组合构建42个预测模型，采用嵌套3折交叉验证与自助重采样方法。在95例独立队列患者（平均年龄50.6岁，FIGO分期IIB–IIIB）中进行外部验证，该队列采用不同的影像预处理方案。 结果：复发率为31.7%（20例）。淋巴结SUV最大值、淋巴结转移状态及肿瘤代谢体积是最具预测价值的单因素指标（C指数≤0.77）。临床特征与影像组学特征联合分析中，UCI+EV/GLMB组合获得最佳预测性能（C指数=0.80，p<0.05）。在单特征集分析中，IBMA+RSF对临床特征预测最优（C指数=0.72），VH.VIMP+GLMN对影像组学特征预测最优（C指数=0.71）。外部验证显示模型具有中等泛化能力（最佳C指数=0.64）。 结论：基于UCI的特征选择结合GLMB或EV算法可获得最佳预测精度，而VH.VIMP+GLMN组合在纯影像组学模型中展现出更优的外部泛化能力。本研究证实了影像组学与机器学习技术整合应用于宫颈癌个体化无病生存期风险分层的可行性。
Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer
肿瘤(癌症)患者之家