Yang Ri-Hui, Lin Zhi-Ping, Dong Ting, Fan Wei-Xiong, Qin Hao-Dong, Jiang Gui-Hua, Dai Hai-Yang
Department of Magnetic Resonance, Meizhou People's Hospital, Meizhou 514031, Guangdong Province, China.
GE Healthcare, Guangzhou 510623, Guangdong Province, China.
World J Radiol. 2025 Aug 28;17(8):110307. doi: 10.4329/wjr.v17.i8.110307.
Esophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.
To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort ( = 147) and a validation cohort ( = 63) in a ratio of 7:3. All patients underwent a preoperative MRI scan from the neck to the abdomen. High-throughput and quantitative radiomics features were extracted from T2-weighted imaging (T2WI) and gadolinium contrast-enhanced T1-weighted imaging (T1WI)-Gd images. Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Then a logistic regression model was built to predict the EC stages. The diagnostic performance of the radiomics model for discriminating between stages I-II and III-IV was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE).
A total of 214 radiomics features were extracted. Following feature dimension reduction, the T1WI and T2WI sequences were retained, and 14 features from the T1WI sequence and 3 features from the T2WI sequence were selected to construct radiomics signatures. The radiomics signature combining T2WI with T1WI-Gd demonstrated superior discrimination of stages in the validation cohort (AUC: 0.851; SEN: 0.697; SPE: 0.793), which outperformed single-sequence models (AUC: 0.779, 0.844; SEN: 0.667, 0.636; SPE: 0.8, 0.8).
MRI-based radiomics signatures could identify EC stages before treatment, which could serve as a noninvasive and quantitative approach aiding personalized treatment planning.
食管癌（EC）是最常见的恶性胃肠道肿瘤之一；在治疗前准确预测EC分期具有重要意义。
探索一种基于磁共振成像（MRI）预测EC术前分期的合理放射组学方法。
本回顾性研究纳入210例经病理证实的EC患者，按7:3的比例随机分为初级队列（n = 147）和验证队列（n = 63）。所有患者均接受了从颈部到腹部的术前MRI扫描。从T2加权成像（T2WI）和钆对比增强T1加权成像（T1WI-Gd）图像中提取高通量定量放射组学特征。使用最小冗余最大相关性和最小绝对收缩与选择算子选择放射组学特征。然后建立逻辑回归模型来预测EC分期。使用曲线下面积（AUC）、敏感性（SEN）和特异性（SPE）评估放射组学模型区分I-II期和III-IV期的诊断性能。
共提取214个放射组学特征。经过特征降维后，保留了T1WI和T2WI序列，从T1WI序列中选择14个特征，从T2WI序列中选择3个特征来构建放射组学特征。将T2WI与T1WI-Gd相结合的放射组学特征在验证队列中表现出对分期的卓越区分能力（AUC：0.851；SEN：0.697；SPE：0.793），优于单序列模型（AUC：0.779，0.844；SEN：0.667，0.636；SPE：0.8，0.8）。
基于MRI的放射组学特征可在治疗前识别EC分期，可作为一种无创且定量的方法辅助个性化治疗规划。
