Li Jie, Li Yatong, Du Lianze, Yuan Qinghai, Han Qinghe
Department of Radiology, The Second Hospital of Jilin University, Changchun, China.
Front Oncol. 2025 Jul 10;15:1599522. doi: 10.3389/fonc.2025.1599522. eCollection 2025.
Non-invasive preoperative prediction of lymphovascular space invasion (LVSI) in cervical cancer (CC) is clinically important for guiding surgical planning and adjuvant therapy, while avoiding the risks associated with invasive procedures. However, current studies using amide proton transfer-weighted (APTw) MRI for LVSI prediction typically analyze only the mean values from a limited number of intratumoral regions of interest (ROIs), which fails to fully capture tumor heterogeneity. This study investigates the added value of whole-tumor APTw habitat radiomics in predicting LVSI and its advantages over conventional analysis methods.
This prospective study included consecutive adult patients with suspected CC who underwent APTw MRI between December 2022 and December 2024; a portion of the cohort has been reported previously. APTw values were extracted using two methods: (1) the conventional approach, calculating the mean signal from three ROIs on a representative slice; and (2) habitat radiomics, involving whole-tumor segmentation, k-means clustering to identify functional subregions, and radiomic feature extraction. Pathological assessment of LVSI from hysterectomy specimens served as the reference standard. Multivariable logistic regression identified variables associated with LVSI and developed diagnostic models. Model robustness was evaluated by 5-fold cross-validation, with AUC and DeLong's test used for performance assessment.
Among 124 patients (74 LVSI-, 50 LVSI+), the APTw_h3 model achieved a higher AUC (0.796 [95% CI: 0.709-0.882]) for predicting LVSI positivity than the clinical-radiological model (AUC = 0.733, 95% CI: 0.638-0.817). The combined model integrating clinical, radiological, and APTw_h3 features achieved the highest AUC (0.903, 95% CI: 0.841-0.952), which was significantly higher than those of both the clinical-radiological and APTw_h3 models (both < 0.001). Moreover, the addition of APTw_h3 to the clinical-radiological model improved sensitivity (88% vs. 82%) and specificity (83.8% vs. 64.9%) for determining LVSI positivity.
Whole-tumor APTw habitat radiomics demonstrates superior performance over conventional mean-value APTw analysis for preoperative prediction of LVSI in CC. Notably, integrating habitat radiomic features with clinical and radiological parameters further improves predictive accuracy, demonstrating potential for enhanced individualized patient management.
宫颈癌（CC）中淋巴管间隙浸润（LVSI）的术前无创预测对于指导手术规划和辅助治疗具有重要临床意义，同时可避免侵入性操作带来的风险。然而，目前使用酰胺质子转移加权（APTw）MRI预测LVSI的研究通常仅分析有限数量的肿瘤内感兴趣区域（ROI）的平均值，无法充分捕捉肿瘤异质性。本研究探讨全肿瘤APTw栖息地放射组学在预测LVSI中的附加价值及其优于传统分析方法的优势。
这项前瞻性研究纳入了2022年12月至2024年12月期间连续接受APTw MRI检查的疑似CC成年患者；该队列的一部分此前已报告。使用两种方法提取APTw值：（1）传统方法，计算代表性切片上三个ROI的平均信号；（2）栖息地放射组学，包括全肿瘤分割､k均值聚类以识别功能子区域以及放射组学特征提取。子宫切除标本的LVSI病理评估作为参考标准。多变量逻辑回归确定与LVSI相关的变量并建立诊断模型。通过5折交叉验证评估模型稳健性，使用AUC和德龙检验进行性能评估。
在124例患者（74例LVSI阴性、50例LVSI阳性）中，APTw_h3模型在预测LVSI阳性方面的AUC（0.796 [95% CI：0.709 - 0.882]）高于临床 - 放射学模型（AUC = 0.733，95% CI：0.638 - 0.817）。整合临床、放射学和APTw_h3特征的联合模型获得了最高的AUC（0.903，95% CI：0.
