Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert-Koch-Strasse 40, 37075, Goettingen, Germany.
Section of Interventional Radiology, Yale School of Medicine, New Haven, CT, USA.
Eur Radiol. 2022 Feb;32(2):981-989. doi: 10.1007/s00330-021-08201-4. Epub 2021 Jul 31.
To assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors.
Adult patients diagnosed with renal sarcomas from 1995 to 2018 were included from 11 European tertiary referral centers (Germany, Belgium, Turkey). Renal sarcomas were 1:4 compared to patients with non-sarcoma renal tumors. CT/MRI findings were assessed using 21 predefined imaging features. A random forest model was trained to predict "renal sarcoma vs. non-sarcoma renal tumors" based on demographics and imaging features.
n = 34 renal sarcomas were included and compared to n = 136 non-sarcoma renal tumors. Renal sarcomas manifested in younger patients (median 55 vs. 67 years, p < 0.01) and were more complex (high RENAL score complexity 79.4% vs. 25.7%, p < 0.01). Renal sarcomas were larger (median diameter 108 vs. 43 mm, p < 0.01) with irregular shape and ill-defined margins, and more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein, compared to non-sarcoma renal tumors (p < 0.05, each). The random forest algorithm yielded a median AUC = 93.8% to predict renal sarcoma histology, with sensitivity, specificity, and positive predictive value of 90.4%, 76.5%, and 93.9%, respectively. Tumor diameter and RENAL score were the most relevant imaging features for renal sarcoma identification.
Renal sarcomas are rare tumors commonly manifesting as large masses in young patients. A random forest model using demographics and imaging features shows good diagnostic accuracy for discrimination of renal sarcomas from non-sarcoma renal tumors, which might aid in clinical decision-making.
• Renal sarcomas commonly manifest in younger patients as large, complex renal masses. • Compared to non-sarcoma renal tumors, renal sarcomas more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein. • Using demographics and standardized imaging features, a random forest showed excellent diagnostic performance for discrimination of sarcoma vs. non-sarcoma renal tumors (AUC = 93.8%, sensitivity = 90.4%, specificity = 76.5%, and PPV = 93.9%).
评估原发性肾肉瘤的影像学特征，以便更好地区分它们与非肉瘤性肾肿瘤。
从 11 个欧洲三级转诊中心（德国、比利时、土耳其）纳入 1995 年至 2018 年诊断为肾肉瘤的成年患者。肾肉瘤与非肉瘤性肾肿瘤的比例为 1:4。使用 21 个预先定义的影像学特征评估 CT/MRI 结果。基于人口统计学和影像学特征，采用随机森林模型预测“肾肉瘤与非肉瘤性肾肿瘤”。
纳入 34 例肾肉瘤，并与 136 例非肉瘤性肾肿瘤进行比较。肾肉瘤发生于更年轻的患者（中位年龄 55 岁比 67 岁，p < 0.01），且更复杂（高 RENAL 评分复杂性 79.4%比 25.7%，p < 0.01）。肾肉瘤较大（中位直径 108 毫米比 43 毫米，p < 0.01），形状不规则，边界不清，更常侵犯肾静脉或下腔静脉、肿瘤坏死、直接侵犯邻近器官、与肾动脉或静脉接触，而非肉瘤性肾肿瘤则不然（p < 0.05，每项）。随机森林算法预测肾肉瘤组织学的中位 AUC = 93.8%，敏感性、特异性和阳性预测值分别为 90.4%、76.5%和 93.9%。肿瘤直径和 RENAL 评分是肾肉瘤识别的最相关影像学特征。
肾肉瘤是罕见的肿瘤，通常表现为年轻患者的大肿块。使用人口统计学和影像学特征的随机森林模型对肾肉瘤与非肉瘤性肾肿瘤的鉴别具有较好的诊断准确性，可能有助于临床决策。
肾肉瘤常见于年轻患者，表现为大而复杂的肾肿块。
与非肉瘤性肾肿瘤相比，肾肉瘤更常侵犯肾静脉或下腔静脉、肿瘤坏死、直接侵犯邻近器官，并与肾动脉或静脉接触。
使用人口统计学和标准化影像学特征，随机森林对肉瘤与非肉瘤性肾肿瘤的鉴别具有出色的诊断性能（AUC = 93.8%，敏感性 = 90.4%，特异性 = 76.5%，PPV = 93.9%）。
