Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Eur Radiol. 2021 Nov;31(11):8786-8796. doi: 10.1007/s00330-021-07877-y. Epub 2021 May 10.
To evaluate diagnostic performance of a radiomics model for classifying hepatic cyst, hemangioma, and metastasis in patients with colorectal cancer (CRC) from portal-phase abdominopelvic CT images.
This retrospective study included 502 CRC patients who underwent contrast-enhanced CT and contrast-enhanced liver MRI between January 2005 and December 2010. Portal-phase CT images of training (n = 386) and validation (n = 116) cohorts were used to develop a radiomics model for differentiating three classes of liver lesions. Among multiple handcrafted features, the feature selection was performed using ReliefF method, and random forest classifiers were used to train the selected features. Diagnostic performance of the developed model was compared with that of four radiologists. A subgroup analysis was conducted based on lesion size.
The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall, 0.8037; hemangioma-specific, 0.6653; metastasis-specific, 0.8027) than the radiologists (overall, 0.9622-0.9680; hemangioma-specific, 0.9452-0.9630; metastasis-specific, 0.9511-0.9869). For subgroup analysis, the PDI of the radiomics model was different according to the lesion size (< 10 mm, 0.6486; ≥ 10 mm, 0.8264) while that of the radiologists was relatively maintained. For classifying metastasis from benign lesions, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% and an AUC of 0.9426.
Albeit inferior to the radiologists, the radiomics model achieved substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRC patients. This model was limited particularly to classifying hemangiomas and subcentimeter lesions.
• Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancer patients. • The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions.
评估基于结直肠癌（CRC）患者门静脉期腹部 CT 图像的放射组学模型对肝囊肿、血管瘤和转移瘤的诊断性能。
本回顾性研究纳入了 2005 年 1 月至 2010 年 12 月期间接受增强 CT 和增强 MRI 检查的 502 例 CRC 患者。训练集（n=386）和验证集（n=116）的门静脉期 CT 图像用于开发一种用于区分三种肝病变类别的放射组学模型。在多种手工特征中，使用 ReliefF 方法进行特征选择，然后使用随机森林分类器训练选择的特征。比较了所开发模型的诊断性能与四位放射科医生的诊断性能。基于病变大小进行了亚组分析。
与放射科医生相比（整体，0.9622-0.9680；血管瘤特异性，0.9452-0.9630；转移瘤特异性，0.9511-0.9869），放射组学模型的整体和血管瘤特异性、转移瘤特异性多分类判别指数（PDI）均显著较低（整体，0.8037；血管瘤特异性，0.6653；转移瘤特异性，0.8027）。对于亚组分析，放射组学模型的 PDI 因病变大小而异（<10mm，0.6486；≥10mm，0.8264），而放射科医生的 PDI 相对保持不变。对于将转移瘤与良性病变区分开来，放射组学模型的诊断性能优异，准确率为 84.36%，AUC 为 0.9426。
尽管放射组学模型的性能逊于放射科医生，但在区分 CRC 患者门静脉期 CT 图像中的肝病变方面仍具有相当的诊断性能。该模型在鉴别血管瘤和亚厘米级病变方面存在局限性。
尽管放射组学模型的性能逊于放射科医生，但它可以使用结直肠癌患者的门静脉期 CT 图像来区分囊肿、血管瘤和转移瘤，具有相当的诊断性能。
该放射组学模型在鉴别血管瘤和亚厘米级肝病变方面存在局限性。
