Integrating ¹⁸F-FDG PET/CT radiomics and body composition for enhanced prognostic assessment in patients with esophageal cancer.

IF 3.4 2区医学 Q2 ONCOLOGY

BMC Cancer Pub Date : 2024-11-14 DOI:10.1186/s12885-024-13157-x

Yeye Zhou, Jin Zhou, Xiaowei Cai, Shushan Ge, Shibiao Sang, Yi Yang, Bin Zhang, Shengming Deng

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引用次数: 0

Abstract

Background: This study aimed to develop a predictive model utilizing radiomics and body composition features derived from ¹⁸F-FDG PET/CT scans to forecast progression-free survival (PFS) and overall survival (OS) outcomes in patients with esophageal squamous cell carcinoma (ESCC).

Methods: We analyzed data from 91 patients who underwent baseline ¹⁸F-FDG PET/CT imaging. Radiomic features extracted from PET and CT images and subsequent radiomics scores (Rad-scores) were calculated. Body composition metrics were also quantified, including muscle and fat distribution at the L3 level from CT scans. Multiparametric survival models were constructed using Cox regression analysis, and their performance was assessed using the area under the time-dependent receiver operating characteristic (ROC) curve (AUC) and concordance index (C-index).

Results: Multivariate analysis identified Rad-score_PFS (P = 0.003), sarcopenia (P < 0.001), and visceral adipose tissue index (VATI) (P < 0.001) as independent predictors of PFS. For OS, Rad-score_OS (P = 0.001), sarcopenia (P = 0.002), VATI (P = 0.037), stage (P = 0.042), and body mass index (BMI) (P = 0.008) were confirmed as independent prognostic factors. Integration of the Rad-score with clinical variables and body composition parameters enhanced predictive accuracy, yielding C-indices of 0.810 (95% CI: 0.737-0.884) for PFS and 0.806 (95% CI: 0.720-0.891) for OS.

Conclusions: This study underscored the potential of combining Rad-score with clinical and body composition data to refine prognostic assessment in ESCC patients.

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整合 18F-FDG PET/CT 放射组学和身体成分，加强食管癌患者的预后评估。

研究背景本研究旨在利用18F-FDG PET/CT扫描得出的放射组学和身体成分特征建立一个预测模型，以预测食管鳞状细胞癌（ESCC）患者的无进展生存期（PFS）和总生存期（OS）结果：我们分析了91名接受基线18F-FDG PET/CT成像的患者的数据。从 PET 和 CT 图像中提取放射组学特征，然后计算放射组学评分（Rad-scores）。此外，还量化了身体成分指标，包括 CT 扫描中 L3 层的肌肉和脂肪分布。使用 Cox 回归分析构建了多参数生存模型，并使用与时间相关的接收者操作特征曲线（ROC）下面积（AUC）和一致性指数（C-index）评估了这些模型的性能：多变量分析发现，Rad-scorePFS（P = 0.003）、肌少症（P OS（P = 0.001）、肌少症（P = 0.002）、VATI（P = 0.037）、分期（P = 0.042）和体重指数（BMI）（P = 0.008）被确认为独立的预后因素。将Rad-score与临床变量和身体成分参数相结合提高了预测的准确性，得出PFS的C指数为0.810（95% CI：0.737-0.884），OS的C指数为0.806（95% CI：0.720-0.891）：这项研究强调了将Rad-score与临床和身体成分数据相结合以完善ESCC患者预后评估的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Cancer 医学-肿瘤学

CiteScore

6.00

自引率

2.60%

发文量

1204

审稿时长

6.8 months

期刊介绍： BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.