Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada.
Lunenfeld Tanenbaum Research Institute, University of Toronto, Toronto, Canada.
Eur Radiol. 2021 Jan;31(1):244-255. doi: 10.1007/s00330-020-07119-7. Epub 2020 Aug 4.
To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features.
This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors.
Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI.
Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions.
• Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC. • With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.
利用 MRI 和 CT 放射组学特征的机器学习,对混合细胞型肝癌(cHCC-CC)与胆管细胞癌(CC)和肝细胞癌(HCC)进行区分。
本回顾性研究纳入了 2004 年至 2018 年间经病理证实的 85 例年龄在 32 至 86 岁的 86 例肝癌患者:24 例 cHCC-CC、24 例 CC 和 38 例 HCC。这些患者均进行了 MRI 和 CT 检查。根据初始 CT 报告和 MRI 特征的形态学评估,评估放射科医生的读片表现。在肿瘤分割后,使用 PyRadiomics 库提取了 1419 个放射组学特征,并通过主成分分析(PCA)将其减少到 20 个主成分。使用支持向量机分类器评估 MRI 和 CT 放射组学特征,以预测 cHCC-CC 与非 cHCC-CC 以及 HCC 与非 HCC。所有肿瘤均以组织病理学为参考标准。
与 CT 相比,MRI 放射组学特征在区分 cHCC-CC 与非 cHCC-CC 方面表现最佳,AUC 为 0.77(SD 0.19),而 CT 则价值有限。增强 MRI 各期及平扫和门静脉期 CT 对 HCC 与非 HCC 的区分具有出色的表现(MRI 的 AUC 分别为 0.79(SD 0.07)至 0.81(SD 0.13),CT 各期的 AUC 分别为 0.81(SD 0.06)和 0.71(SD 0.15))。放射科医生读片对 cHCC-CC 的误诊率为 CT 组的 69%,MRI 组的 58%。
本研究结果表明,基于机器学习的 MRI 和 CT 放射组学特征分析在区分 cHCC-CC 与 HCC 和 CC 方面具有良好的预测性能,这可能对治疗决策产生影响。
本回顾性研究表明,MRI 放射组学特征在区分 cHCC-CC 与 HCC 和 CC 方面具有良好的预测性能,CT 放射组学特征在区分 HCC 与 cHCC-CC 和 CC 方面具有良好的预测性能。
未来的验证研究,放射组学分析有可能为 cHCC-CC 的术前诊断提供信息,并为肝切除术和肝移植的最佳治疗决策提供依据。