Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Armed Forces Daejeon Hospital, Daejeon, Republic of Korea.
Sci Rep. 2024 Oct 9;14(1):23541. doi: 10.1038/s41598-024-74311-w.
This multi-center, retrospective study focused on periampullary cancer patients undergoing MRI for hepatic metastasis and abscess differentiation. T1-weighted, T2-weighted, and arterial phase images were utilized to create radiomics models. In the training-set, 112 lesions in 54 patients (median age [IQR, interquartile range], 73 [63-80]; 38 men) were analyzed, and 123 lesions in 55 patients (72 [66-78]; 34 men) comprised the validation set. The T1-weighted + T2-weighted radiomics model showed the highest AUC (0.82, 95% CI 0.75-0.89) in the validation set. Notably, < 30% T1-T2 size discrepancy in MRI findings predicted metastasis (Ps ≤ 0.037), albeit with AUCs of 0.64-0.68 for hepatic metastasis. The radiomics model enhanced radiologists' performance (AUCs, 0.85-0.87 vs. 0.80-0.84) and significantly increased diagnostic confidence (P < 0.001). Although the performance increase lacked statistical significance (P = 0.104-0.281), the radiomics model proved valuable in differentiating small hepatic lesions and enhancing diagnostic confidence. This study highlights the potential of MRI-based radiomics in improving accuracy and confidence in the diagnosis of periampullary cancer-related hepatic lesions.
这项多中心、回顾性研究专注于接受 MRI 检查以区分肝转移和脓肿的壶腹周围癌患者。研究使用 T1 加权、T2 加权和动脉期图像来创建放射组学模型。在训练集中，对 54 名患者的 112 个病灶（中位数[IQR]，73[63-80]；38 名男性）进行了分析，55 名患者的 123 个病灶（72[66-78]；34 名男性）构成了验证集。T1 加权+T2 加权放射组学模型在验证集中显示出最高的 AUC（0.82，95%CI 0.75-0.89）。值得注意的是，MRI 结果中 T1-T2 大小差异<30%预测转移（Ps≤0.037），尽管肝转移的 AUC 为 0.64-0.68。放射组学模型提高了放射科医生的表现（AUCs，0.85-0.87 与 0.80-0.84），并显著提高了诊断信心（P<0.001）。尽管性能提高没有统计学意义（P=0.104-0.281），但放射组学模型在区分小肝病变和增强诊断信心方面具有价值。本研究强调了基于 MRI 的放射组学在提高壶腹周围癌相关肝病变诊断准确性和信心方面的潜力。
