Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China.
J Transl Med. 2024 Jun 18;22(1):579. doi: 10.1186/s12967-024-05392-4.
This study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiotherapy (nCRT).
The study retrospectively analyzed 232 ESCC patients who underwent pretreatment and post-treatment CT scans. Patients were divided into training (n = 186) and validation (n = 46) sets through fivefold cross-validation. 837 radiomics features were extracted from regions of interest (ROIs) delineations on CT images before and after nCRT to calculate delta values. The LASSO algorithm selected delta-radiomics features (DRF) based on classification performance. Logistic regression constructed a nomogram incorporating DRFs and clinical factors. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses evaluated nomogram performance for predicting pCR.
No significant differences existed between the training and validation datasets. The 4-feature delta-radiomics signature (DRS) demonstrated good predictive accuracy for pCR, with α-binormal-based and empirical AUCs of 0.871 and 0.869. T-stage (p = 0.001) and differentiation degree (p = 0.018) were independent predictors of pCR. The nomogram combined the DRS and clinical factors improved the classification performance in the training dataset (AUC = 0.933 and AUC = 0.941). The validation set showed similar performance with AUCs of 0.958 and 0.962.
The CT-based delta-radiomics nomogram model with clinical factors provided high predictive accuracy for pCR in ESCC patients after nCRT.
本研究通过基于 CT 的 delta-radiomics 特征和临床因素开发了一个列线图模型，以预测接受新辅助放化疗（nCRT）的食管鳞状细胞癌（ESCC）患者的病理完全缓解（pCR）。
该研究回顾性分析了 232 例接受治疗前和治疗后 CT 扫描的 ESCC 患者。通过五重交叉验证将患者分为训练集（n=186）和验证集（n=46）。从 nCRT 前后 CT 图像的感兴趣区域（ROI）描绘中提取 837 个放射组学特征，以计算 delta 值。LASSO 算法根据分类性能选择 delta-radiomics 特征（DRF）。逻辑回归构建了一个纳入 DRF 和临床因素的列线图。接收者操作特征（ROC）和曲线下面积（AUC）分析评估了列线图预测 pCR 的性能。
训练集和验证集之间没有显著差异。基于α-双正态的 4 特征 delta-radiomics 特征（DRS）表现出对 pCR 的良好预测准确性，其 AUC 为 0.871 和 0.869。T 分期（p=0.001）和分化程度（p=0.018）是 pCR 的独立预测因素。列线图结合 DRS 和临床因素提高了训练集的分类性能（AUC=0.933 和 AUC=0.941）。验证集的 AUC 为 0.958 和 0.962，表现出相似的性能。
基于 CT 的 delta-radiomics 列线图模型结合临床因素对 nCRT 后 ESCC 患者的 pCR 具有较高的预测准确性。
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