Department of Radiology, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Jpn J Radiol. 2024 Dec;42(12):1458-1468. doi: 10.1007/s11604-024-01631-2. Epub 2024 Jul 24.
To assess the computed tomography (CT) findings of papillary renal neoplasm with reverse polarity (PRNRP) and develop a radiomics-based model to distinguish PRNRPs from papillary renal cell carcinomas (PRCCs).
We analyzed 31 PRNRPs and 68 PRCCs using preoperative kidney CT. We evaluated CT features that could discriminate PRNRPs from PRCCs. A radiomics signature was constructed using features selected through a least absolute shrinkage and selection operator algorithm. A radiomics-based model incorporating a radiomics signature and subjective CT parameters using multivariate logistic regression was developed. The diagnostic performance of the CT parameters, radiomics model, and their combination was evaluated using the area under the curve (AUC).
Most of PRNRPs had a round shape (93.5%), well-defined margin (100%), and persistent enhancement (77.4%). Compared with PRCC, PRNRPs exhibited distinct CT features including small size (16.7 vs. 37.7 mm, P < 0.001), heterogeneity (64.5 vs. 32.4%, P = 0.004), enhancing dot sign (16.1 vs. 1.5%, P = 0.001), and high attenuation in pre-contrast CT (44.2 vs. 35.5 HU, P = 0.003). Multivariate analysis revealed smaller mass size (odds ratio [OR]: 0.9; 95% confidence interval [CI] 0.9-1.0, P = 0.013), heterogeneity (OR: 8.8; 95% CI 1.9-41.4, P = 0.006), and higher attenuation in pre-contrast CT (OR: 1.1; 95% CI 1.0-1.2, P = 0.011) as significant independent factors for identifying PRNRPs. The diagnostic performance of the combination model was excellent (AUC: 0.923).
Smaller tumor size, heterogeneity, and higher attenuation in pre-contrast CT were more closely associated with PRNRPs than with PRCCs. Though the retrospective design, small sample size, and single-center data of this study may affect the generalizability of the findings, combining subjective CT features with a radiomics model is beneficial for distinguishing PRNRPs from PRCCs.
评估具有反向极性的乳头状肾肿瘤（PRNRP）的计算机断层扫描（CT）表现，并建立一种基于放射组学的模型来区分 PRNRP 和乳头状肾细胞癌（PRCC）。
我们使用术前肾脏 CT 分析了 31 例 PRNRP 和 68 例 PRCC。我们评估了可以区分 PRNRP 和 PRCC 的 CT 特征。通过最小绝对值收缩和选择算子算法选择特征，构建放射组学特征。使用多元逻辑回归，结合放射组学特征和主观 CT 参数建立放射组学模型。使用曲线下面积（AUC）评估 CT 参数、放射组学模型及其组合的诊断性能。
大多数 PRNRP 具有圆形（93.5%）、边界清晰（100%）和持续增强（77.4%）。与 PRCC 相比，PRNRP 表现出明显的 CT 特征，包括较小的肿瘤大小（16.7 比 37.7mm，P<0.001）、异质性（64.5 比 32.4%，P=0.004）、增强点征（16.1 比 1.5%，P=0.001）和 CT 平扫时高衰减（44.2 比 35.5HU，P=0.003）。多变量分析显示，肿瘤体积较小（优势比[OR]：0.9；95%置信区间[CI]：0.9-1.0，P=0.013）、异质性（OR：8.8；95%CI：1.9-41.4，P=0.006）和 CT 平扫时高衰减（OR：1.1；95%CI：1.0-1.2，P=0.011）是识别 PRNRP 的显著独立因素。联合模型的诊断性能优异（AUC：0.923）。
与 PRCC 相比，较小的肿瘤大小、异质性和 CT 平扫时高衰减与 PRNRP 更为密切相关。尽管本研究存在回顾性设计、样本量小和单中心数据的局限性，可能会影响研究结果的普遍性，但结合主观 CT 特征和放射组学模型有助于区分 PRNRP 和 PRCC。
