Zheng Yuting, Mei Peiyuan, Wang Mingliang, Luo Qinyue, Li Hanting, Ding Chengyu, Zhang Kailu, Chen Leqing, Gu Jin, Li Yumin, Guo Tingting, Zhang Chi, Yao Wenjian, Wei Li, Liao Yongde, Han Xiaoyu, Shi Heshui
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
BMC Med Imaging. 2024 Dec 3;24(1):329. doi: 10.1186/s12880-024-01503-1.
The study aimed to investigate the predictive value of delta-radiomics derived from computed tomography (CT) for pathological complete response (pCR) to neoadjuvant immunochemotherapy (NICT) among patients with esophageal squamous cell carcinoma (ESCC), helping clinicians determine whether to modify the neoadjuvant treatment strategy, proceed to surgery, or forgo surgery altogether.
A total of 140 ESCC patients from two institutions (Database 1 = 93; Database 2 = 47) who underwent NICT and surgery were retrospectively included in the study. The training set consisted of patients from Database 1, while the testing set included patients from Database 2. All patients underwent contrast-enhanced CT scans before the start of the treatment and prior to the operation. The delta-radiomics features were calculated as the relative net change of radiomics features between the two-time points. Feature selection was conducted using Pearson correlation analysis, intraclass correlation coefficients, and the fivefold cross-validation with least absolute shrinkage and selection analysis. Four models were established, comprising a clinical model, a pre-treatment radiomics model, a delta-radiomics model, and a mixed model. Area under the curve (AUC) and decision curve analysis were used to assess the performance and the clinical value of the models.
Less than half of the tumors (40/140, 28.6%) showed pCR following NICT. The delta-radiomics model displayed AUC of 0.827 and 0.790 in the training and testing set for predicting pCR, which was superior to the clinical model based on age and clinical tumor node metastasis (cTNM) stage (0.758 and 0.615) and the pre-treatment radiomics model (0.787 and 0.621). Furthermore, the delta-radiomics model demonstrated a more excellent AUC value in the testing set than the mixed model (0.847 and 0.719), which integrated clinical and delta-radiomics features.
The delta-radiomics model exhibited better diagnostic performance in preoperative prediction of pCR for NICT in ESCC patients compared to the clinical, pre-treatment radiomics, and mixed models.
本研究旨在探讨计算机断层扫描（CT）衍生的delta-放射组学对食管鳞状细胞癌（ESCC）患者新辅助免疫化疗（NICT）后病理完全缓解（pCR）的预测价值，以帮助临床医生确定是否修改新辅助治疗策略、进行手术或完全放弃手术。
本研究回顾性纳入了来自两个机构的140例接受NICT和手术的ESCC患者（数据库1 = 93例；数据库2 = 47例）。训练集由数据库1中的患者组成，而测试集包括数据库2中的患者。所有患者在治疗开始前和手术前均接受了增强CT扫描。delta-放射组学特征计算为两个时间点之间放射组学特征的相对净变化。使用Pearson相关分析、组内相关系数以及具有最小绝对收缩和选择分析的五折交叉验证进行特征选择。建立了四个模型，包括临床模型、治疗前放射组学模型、delta-放射组学模型和混合模型。采用曲线下面积（AUC）和决策曲线分析评估模型的性能和临床价值。
不到一半的肿瘤（40/140，28.6%）在NICT后显示pCR。delta-放射组学模型在训练集和测试集中预测pCR的AUC分别为0.827和0.790，优于基于年龄和临床肿瘤淋巴结转移（cTNM）分期的临床模型（0.758和0.615）以及治疗前放射组学模型（0.787和0.621）。此外，delta-放射组学模型在测试集中的AUC值比整合了临床和delta-放射组学特征的混合模型（0.847和0.719）更优。
与临床、治疗前放射组学和混合模型相比，delta-放射组学模型在术前预测ESCC患者NICT后pCR方面表现出更好的诊断性能。
