Development and validation a radiomics nomogram for predicting thymidylate synthase status in hepatocellular carcinoma based on Gd-DTPA contrast enhanced MRI

作者：Zongren Ding, Yijun Wu, Guoxu Fang, Zhaowang Lin, Kongying Lin, Jun Fu, Qizhen Huang, Yanyan Tang, Wuyi You, Jingfeng Liu, Yongyi Zeng · 发表于：BMC Cancer · 年份：2023 · DOI：10.1186/s12885-023-11096-7 · 被引用次数：1 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Cholangiocarcinoma and Gallbladder Cancer Studies

OBJECTIVES: The purpose of this study was to develop and validate a radiomics nomogram for predicting thymidylate synthase (TYMS) status in hepatocellular carcinoma (HCC) by using Gd-DTPA contrast enhanced MRI. METHODS: We retrospectively enrolled 147 consecutive patients with surgically confirmed HCC and randomly allocated to training and validation set (7:3). The TYMS status was immunohistochemical determined and classified into low TYMS (positive cells ≤ 25%) and high TYMS (positive cells > 25%) groups. Radiomics features were extracted from the arterial phases and portal venous phase of Gd-DTPA contrast enhanced MRI. Least absolute shrinkage and selection operator (LASSO) were applied for generating the Rad score. Clinical data and MRI findings were assessed to build a clinical model. Rad score combined with clinical features was used to construct radiomics nomogram. RESULTS: A total of 2260 features were extracted and reduced to 7 features as the most important discriminators to build the Rad score. InAFP was identified as the only independent clinical factors for TYMS status. The radiomics nomogram showed good discrimination in training (AUC, 0.759; 95% CI 0.665-0.838) and validation set (AUC, 0.739; 95% CI 0.585-0.860), and showed better discrimination capability (P < 0.05) compared with clinical model in training (AUC, 0.656; 95% CI 0.555-0.746) and validation set (AUC, 0.622; 95% CI 0.463-0.764). CONCLUSIONS: The radiomics nomogram shows favorable predictive efficacy...