基于ct的放射组学预测食管鳞状细胞癌新辅助免疫化疗的病理反应:一项多中心研究。

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-12-03 DOI:10.1186/s12880-024-01503-1
Yuting Zheng, Peiyuan Mei, Mingliang Wang, Qinyue Luo, Hanting Li, Chengyu Ding, Kailu Zhang, Leqing Chen, Jin Gu, Yumin Li, Tingting Guo, Chi Zhang, Wenjian Yao, Li Wei, Yongde Liao, Xiaoyu Han, Heshui Shi
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引用次数: 0

摘要

背景:本研究旨在探讨计算机断层扫描(CT)衍生的放射组学对食管鳞状细胞癌(ESCC)患者新辅助免疫化疗(NICT)病理完全缓解(pCR)的预测价值,帮助临床医生决定是否修改新辅助治疗策略,继续手术,或完全放弃手术。方法:来自两所医院的140例ESCC患者(数据库1 = 93;数据库2 = 47)接受NICT和手术的患者被回顾性纳入研究。训练集包括来自数据库1的患者,而测试集包括来自数据库2的患者。所有患者在治疗开始前和手术前都进行了对比增强CT扫描。delta-radiomics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、类内相关系数、最小绝对收缩的五重交叉验证和选择分析进行特征选择。建立了临床模型、治疗前放射组学模型、δ放射组学模型和混合模型。采用曲线下面积(AUC)和决策曲线分析评价模型的性能和临床价值。结果:不到一半的肿瘤(40/140,28.6%)在NICT后出现pCR。在预测pCR的训练和测试集中,delta-radiomics模型的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)和治疗前放射组学模型(0.787和0.621)。此外,delta-radiomics模型在测试集中表现出比混合模型更优秀的AUC值(0.847和0.719),该模型综合了临床和delta-radiomics的特征。结论: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的pCR诊断中表现出更好的诊断性能。
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CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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