Wang Jincheng, Tang Shengnan, Mao Yingfan, Wu Jin, Xu Shanshan, Yue Qi, Chen Jun, He Jian, Yin Yin
Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Hepatol Int. 2022 Jun;16(3):627-639. doi: 10.1007/s12072-022-10326-7. Epub 2022 Mar 28.
To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.
This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men).
In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.
Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.
建立并验证基于放射组学的模型，用于在对比增强CT图像上对肝纤维化进行分期。
这项回顾性研究在一个由332例经活检证实为肝纤维化的患者（年龄中位数59岁；四分位间距51 - 67岁；男性256例）组成的训练队列中，开发了两种基于放射组学的模型（R评分：放射组学特征；R纤维化：整合放射组学和血清变量），这些患者在2017年1月至2020年12月期间接受了对比增强CT检查。从平扫、动脉期和门静脉期CT图像中提取放射组学特征，并使用最小绝对收缩和选择算子（LASSO）逻辑回归进行选择，以区分F3 - F4期与F0 - F2期。通过受试者操作特征曲线分析确定诊断显著纤维化（F2 - F4期）、进展性纤维化（F3 - F4期）和肝硬化（F4期）的最佳截断值。通过曲线下面积、奥布霍夫斯基指数、校准和决策曲线分析评估诊断性能。在111例随机分配的患者（年龄中位数58岁；四分位间距49 - 66岁；男性89例）中进行了内部验证。
在验证队列中，R评分和R纤维化（奥布霍夫斯基指数分别为0.843和0.846）在肝纤维化分期方面显著优于天冬氨酸转氨酶与血小板比值（APRI）（奥布霍夫斯基指数为0.651；p < 0.001）和纤维化-4指数（FIB-4）（奥布霍夫斯基指数为0.676；p < 0.001）。使用这些截断值，R纤维化和R评分在诊断显著纤维化、进展性纤维化和肝硬化时，灵敏度范围为70 - 87%，特异性范围为71 - 97%，准确性范围为82 - 86%。
对比增强CT图像的放射组学分析对肝纤维化具有良好的诊断性能。
