基于钆布醇增强 MRI 的临床-影像学特征和放射组学特征预测肝纤维化进展。

Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis.

机构信息

Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.

MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China.

出版信息

Eur Radiol. 2023 Jan;33(1):633-644. doi: 10.1007/s00330-022-08992-0. Epub 2022 Jul 19.

DOI:10.1007/s00330-022-08992-0

PMID:35852575

Abstract

OBJECTIVES

To develop and validate a combined model based on Gd-BOPTA-enhanced MRI to identify advanced liver fibrosis.

METHODS

A total of 102 patients with chronic HBV infection were divided into a training cohort (n = 80) and a time-independent testing cohort 1 (n = 22). In the training cohort, radiomics signatures were extracted from the hepatobiliary phase. Model 1 was constructed with clinic-radiological factors using multivariable logistic regression to predict advanced liver fibrosis, and model 2 incorporated radiomics signatures based on model 1. The diagnostic performances were compared with serum fibrosis tests and FibroScan tests using area under curve (AUC) in testing cohort 1. Another 45 patients with other causes were collected in testing cohort 2 for further validation.

RESULTS

Model 1 showed age (OR = 1.079) and periportal space widening (OR = 7.838) were the independent factors for predicting advanced fibrosis. After integrating radiomics signatures, model 2 enabled more accurately than model 1 in training cohort (0.940 vs. 0.802, p = 0.003). In testing cohort 1, model 2 demonstrated a superior AUC compared with model 1 (0.900 vs. 0.813，p = 0.131), FibroScan test (0.900 vs. 0.733, p = 0.193), and serum fibrosis tests (APRI and Fib-4 was 0.667 and 0.791). In testing cohort 2, model 2 incorporating radiomics signatures showed satisfactory performance (0.874 vs. 0.757，p = 0.010) compared with model 1.

CONCLUSIONS

Radiomics signatures derived from Gd-BOPTA-enhanced HBP images may offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis.

KEY POINTS

• Linear or reticular hyperintensity on T2WI, periportal space widening, and diffuse periportal enhancement on HBP can be useful for predicting advanced liver fibrosis. • Clinic-radiological features such as patient age and periportal space widening are the two independent factors predicting advanced fibrosis. • Radiomics signatures derived from Gd-BOPTA-enhanced HBP images offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis.

摘要

目的

开发并验证一种基于钆塞酸二钠增强 MRI 的联合模型，以识别进展性肝纤维化。

方法

将 102 例慢性 HBV 感染患者分为训练队列（n = 80）和时间独立测试队列 1（n = 22）。在训练队列中，从肝胆期提取放射组学特征。使用多变量逻辑回归，基于临床放射学因素构建模型 1 以预测进展性肝纤维化，基于模型 1 纳入放射组学特征构建模型 2。使用测试队列 1 中的曲线下面积（AUC）比较血清纤维化检测和 FibroScan 检测的诊断性能。另外，在测试队列 2 中收集了 45 例其他原因的患者进行进一步验证。

结果

模型 1 显示年龄（OR = 1.079）和门脉周围空间增宽（OR = 7.838）是预测进展性纤维化的独立因素。在纳入放射组学特征后，模型 2 在训练队列中的预测能力优于模型 1（0.940 与 0.802，p = 0.003）。在测试队列 1 中，模型 2 与模型 1 相比，AUC 更高（0.900 与 0.813，p = 0.131），与 FibroScan 测试（0.900 与 0.733，p = 0.193）和血清纤维化检测（APRI 和 Fib-4 分别为 0.667 和 0.791）相比。在测试队列 2 中，纳入放射组学特征的模型 2 与模型 1 相比，表现出令人满意的性能（0.874 与 0.757，p = 0.010）。