Lee Seungeun, Lee So-Yeon, Kim Sanghee, Huh Yeon-Jung, Lee Jooyeon, Lee Ko-Eun, Jung Joon-Yong
Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Department of Radiology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea.
Diagnostics (Basel). 2023 Feb 16;13(4):755. doi: 10.3390/diagnostics13040755.
Osteolytic lesions can be seen in both multiple myeloma (MM), and osteolytic bone metastasis on computed tomography (CT) scans. We sought to assess the feasibility of a CT-based radiomics model to distinguish MM from metastasis. This study retrospectively included patients with pre-treatment thoracic or abdominal contrast-enhanced CT from institution 1 (training set: 175 patients with 425 lesions) and institution 2 (external test set: 50 patients with 85 lesions). After segmenting osteolytic lesions on CT images, 1218 radiomics features were extracted. A random forest (RF) classifier was used to build the radiomics model with 10-fold cross-validation. Three radiologists distinguished MM from metastasis using a five-point scale, both with and without the assistance of RF model results. Diagnostic performance was evaluated using the area under the curve (AUC). The AUC of the RF model was 0.807 and 0.762 for the training and test set, respectively. The AUC of the RF model and the radiologists (0.653-0.778) was not significantly different for the test set ( ≥ 0.179). The AUC of all radiologists was significantly increased (0.833-0.900) when they were assisted by RF model results ( < 0.001). In conclusion, the CT-based radiomics model can differentiate MM from osteolytic bone metastasis and improve radiologists' diagnostic performance.
在计算机断层扫描(CT)上,溶骨性病变可见于多发性骨髓瘤(MM)和溶骨性骨转移。我们试图评估基于CT的放射组学模型区分MM与转移瘤的可行性。本研究回顾性纳入了来自机构1(训练集:175例患者,425个病灶)和机构2(外部测试集:50例患者,85个病灶)的治疗前胸部或腹部增强CT患者。在CT图像上分割溶骨性病变后,提取了1218个放射组学特征。使用随机森林(RF)分类器通过10倍交叉验证构建放射组学模型。三名放射科医生使用五点量表区分MM与转移瘤,分别在有和没有RF模型结果辅助的情况下进行。使用曲线下面积(AUC)评估诊断性能。RF模型在训练集和测试集的AUC分别为0.807和0.762。在测试集中,RF模型和放射科医生的AUC(0.653 - 0.778)差异不显著(≥0.179)。当放射科医生得到RF模型结果辅助时,其AUC显著增加(0.833 - 0.900)(<0.001)。总之,基于CT的放射组学模型可以区分MM与溶骨性骨转移,并提高放射科医生的诊断性能。