Development and validation of a machine learning-based ¹⁸F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.

IF 3.5 2区医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-07-30 DOI:10.1186/s40644-024-00741-4

Huaiqing Zhi, Yilan Xiang, Chenbin Chen, Weiteng Zhang, Jie Lin, Zekan Gao, Qingzheng Shen, Jiancan Shao, Xinxin Yang, Yunjun Yang, Xiaodong Chen, Jingwei Zheng, Mingdong Lu, Bujian Pan, Qiantong Dong, Xian Shen, Chunxue Ma

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Abstract

Background: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC.

Methods: We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness.

Results: On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis.

Conclusions: Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.

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基于机器学习的18F-氟脱氧葡萄糖PET/CT放射组学特征的开发与验证，用于预测胃癌生存率。

背景：胃癌（GC）患者的生存预后往往影响医生对其后续治疗的选择。本研究旨在开发一种基于正电子发射断层扫描（PET）的放射组学模型，结合临床肿瘤-结节-转移（TNM）分期预测胃癌患者的总生存期（OS）：我们回顾了327例接受18 F-氟脱氧葡萄糖（18 F-FDG PET）扫描的病理确诊为GC患者的临床信息。患者被随机分为训练组（229 人）和验证组（98 人）。我们从 PET 图像中提取了 171 个 PET 放射组学特征，并使用最小绝对收缩和选择算子（LASSO）和随机生存森林（RSF）确定了 PET 放射组学评分（RS）。建立的放射组学模型包括 PET RS 和临床 TNM 分期，用于预测 GC 患者的 OS。对该模型的区分度、校准和临床实用性进行了评估：结果：在多变量 COX 回归分析中，GC 患者的年龄、癌胚抗原（CEA）、临床 TNM 分期和 PET RS 之间的差异具有统计学意义（p 结论：PET RS 和临床 TNM 分期在预测 GC 患者的 OS 方面具有重要作用：基于 PET RS、临床 TNM 和临床特征的放射组学模型可为预测 GC 患者的 OS 提供新的工具。

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

Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

CiteScore

7.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.