Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.
Comput Math Methods Med. 2022 Feb 21;2022:5334095. doi: 10.1155/2022/5334095. eCollection 2022.
Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA.
This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients.
In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA.
We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.
考虑到手术的时间窗口很窄，早期诊断肝癌仍然是一个需要探索的基本问题。肝细胞癌（HCC）和肝内胆管细胞癌（ICCA）被认为是两种不同类型的肝癌，因为它们具有不同的发病机制、病理特征、预后和对辅助治疗的反应。图像的定性分析不足以对肝癌进行区分，尤其是早期 HCC 或 ICCA。
本回顾性研究在 122 例患者的训练队列中建立了一个基于放射组学的模型。从计算机断层扫描（CT）扫描中提取放射组学特征。采用最小绝对值收缩和选择算子（LASSO）逻辑方法进行特征选择。选择支持向量机（SVM）来构建模型。在 89 例患者中进行了内部验证。
在训练集中，放射组学评估的 AUC 为 0.855，高于放射科医生的 0.689。在评估队列中，AUC 为 0.847，验证为 0.659，这表明所建立的模型在区分 HCC 和 ICCA 方面具有显著更好的性能。
我们开发了一种基于 CT 图像的放射组学诊断模型，能够快速区分 HCC 和 ICCA，这可能有助于未来 HCC 和 ICCA 的鉴别诊断。
