增强CT放射组学结合临床特征预测食管鳞癌新辅助化疗联合免疫治疗的疗效。

IF 2.8 4区 医学 Q3 ONCOLOGY
Technology in Cancer Research & Treatment Pub Date : 2025-01-01 Epub Date: 2025-08-17 DOI:10.1177/15330338251370437
Xiang Qin, Fen Wang, Shaohong Wu, Dong Han, Genji Bai, Lili Guo
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引用次数: 0

摘要

目的探讨增强CT放射组学结合临床特征对食管鳞状细胞癌(ESCC)患者新辅助化疗加免疫治疗疗效的预测作用。方法回顾性分析2020年1月至2024年10月接受新辅助化疗免疫治疗的189例经病理证实的食管鳞状细胞癌患者。根据病理反应将患者分为缓解组和非缓解组,并随机分为训练组(n = 114)和测试组(n = 75)(比例为6:4)。临床预测因子采用逻辑回归构建临床模型。在CT增强扫描中,从人工划定的肿瘤区域中提取放射组学特征,并建立放射组学模型。然后建立了一个整合临床变量和放射组学概率的组合模型,并以nomogram形式呈现。采用受试者工作特征(ROC)曲线(通过Delong检验比较曲线下面积)、校准曲线和决策曲线分析(DCA)来评估模型的性能。结果多变量分析发现治疗周期数是显著的临床预测因子。选择10个放射学特征作为最终模型。在训练集中,临床模型的AUC为0.705 (95% CI 0.607-0.802),放射组学和联合模型的AUC分别为0.905 (95% CI 0.843-0.967)和0.914 (95% CI 0.857-0.970)。在测试集中也观察到类似的趋势,其中联合模型(AUC 0.859, 95% CI 0.768-0.950)优于放射组学(AUC 0.815)和临床(AUC 0.644)模型。结论增强CT放射组学模型对食管鳞状细胞癌患者新辅助化疗联合免疫治疗缓解有较好的预测效果,且联合模型具有较大的预测价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.

Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.

Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.

Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.

IntroductionTo evaluate the predictive efficacy of enhanced Computed Tomograph(CT) radiomics combined with clinical features for assessing treatment response to neoadjuvant chemotherapy plus immunotherapy in esophageal squamous cell carcinoma (ESCC) patients.MethodsWe retrospectively analyzed 189 pathologically confirmed esophageal squamous cell carcinoma patients (treated between January 2020 and October 2024) who underwent neoadjuvant chemoimmunotherapy. Patients were stratified into remission and non-remission groups based on pathological response and randomly divided into training (n = 114) and testing (n = 75) sets (6:4 ratio). Clinical predictors were identified using logistic regression to construct a clinical model. Radiomic features were extracted from manually delineated tumor regions on contrast-enhanced CT scans, and a radiomics model was developed. A combined model integrating clinical variables and radiomics probabilities was then built and presented as a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves (AUC, Area Under the Curve) comparison via Delong test), calibration curves, and decision curve analysis (DCA).ResultsMultivariable analysis identified treatment cycle number as a significant clinical predictor. Ten radiomic features were selected for the final model. In the training set, the clinical model achieved an AUC of 0.705 (95% CI 0.607-0.802), while the radiomics and combined models showed superior performance with AUCs of 0.905 (95% CI 0.843-0.967) and 0.914 (95% CI 0.857-0.970), respectively. Similar trends were observed in the testing set, where the combined model (AUC 0.859, 95% CI 0.768-0.950) outperformed both the radiomics (AUC 0.815) and clinical (AUC 0.644) models.ConclusionThe enhanced CT radiomics model has better predictive efficacy for remission with neoadjuvant chemotherapy combined with immunotherapy in esophageal squamous cell carcinoma patients, and the combined model has greater predictive value.

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来源期刊
CiteScore
4.40
自引率
0.00%
发文量
202
审稿时长
2 months
期刊介绍: Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.
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