Wang Ke-Ying, Xiao Mei-Ling, Fang Yu-Han, Cheng Jie-Jun, Lin Zi-Jing, Li Ying, Qiang Jin-Wei
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Front Oncol. 2025 Aug 21;15:1612691. doi: 10.3389/fonc.2025.1612691. eCollection 2025.
To develop a magnetic resonance imaging (MRI)-based radiomics nomogram to predict lymphovascular space invasion (LVSI) status in patients with early-stage cervical adenocarcinoma (CAC).
Clinicopathological and MRI data from 310 patients with histopathologically confirmed early-stage CAC were retrospectively analyzed. Patients were divided into training (n = 186) and validation (n = 124) cohorts. Tumor volumes of interest (VOIs) were segmented on T2-weighted imaging (FS-T2WI) and aligned to diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and T1-weighted imaging (CE-T1WI) sequences. Radiomics features were extracted and screened using Pearson correlation and least absolute shrinkage and selection operator (LASSO) regression, and a radscore was calculated for each patient. Multivariate logistic regression identified clinical risk factors, and a radiomics nomogram was constructed by integrating the radscore with clinical risk factors. Receiver operating characteristic (ROC) curves and areas under the curve (AUCs) were used to evaluate the performance of the clinical model, radiomics model, and nomogram. Decision curve analysis was used to assesses the clinical utility of the nomogram.
Seventeen radiomics features were selected to construct the radscore. Menopause and tumor diameter were identified as independent clinical risk factors for LVSI. The radiomics nomogram achieved AUCs of 0.80 (95% CI: 0.74-0.86) and 0.78 (95% CI: 0.69-0.86) in the training and validation cohorts, outperforming the clinical model (AUCs: 0.69 and 0.62) and comparable to the radiomics model (AUCs: 0.79 and 0.78). Decision curve analysis showed the nomogram provided clinical benefit.
The radiomics nomogram, integrating radiomic features and clinical risk factors, could be used to predict LVSI status in early-stage CAC accurately, supporting preoperative clinical decision-making.
开发一种基于磁共振成像（MRI）的放射组学列线图，以预测早期宫颈腺癌（CAC）患者的淋巴管间隙浸润（LVSI）状态。
回顾性分析310例经组织病理学证实为早期CAC患者的临床病理和MRI数据。患者分为训练组（n = 186）和验证组（n = 124）。在T2加权成像（FS-T2WI）上分割感兴趣的肿瘤体积（VOI），并与扩散加权成像（DWI）、表观扩散系数（ADC）图和T1加权成像（CE-T1WI）序列对齐。使用Pearson相关性和最小绝对收缩和选择算子（LASSO）回归提取和筛选放射组学特征，并为每位患者计算放射学评分。多变量逻辑回归确定临床危险因素，并通过将放射学评分与临床危险因素整合来构建放射组学列线图。采用受试者操作特征（ROC）曲线和曲线下面积（AUC）评估临床模型、放射组学模型和列线图的性能。决策曲线分析用于评估列线图的临床实用性。
选择17个放射组学特征来构建放射学评分。绝经和肿瘤直径被确定为LVSI的独立临床危险因素。放射组学列线图在训练组和验证组中的AUC分别为0.80（95%CI：0.74 - 0.86）和0.78（95%CI：0.69 - 0.86），优于临床模型（AUC：0.69和0.62），与放射组学模型相当（AUC：0.79和0.78）。决策曲线分析表明列线图具有临床益处。
整合放射组学特征和临床危险因素的放射组学列线图可准确预测早期CAC的LVSI状态，支持术前临床决策。
