良性子宫平滑肌肿瘤和恶性子宫平滑肌肿瘤之间的鉴别诊断具有挑战性。我们评估了基于临床和超声的算法在预测间质子宫恶性肿瘤 (MUM) 方面的准确性，包括恶性潜能不确定的平滑肌肿瘤 (STUMP)。我们报告了一项观察性、前瞻性、单中心的 12 个月随访一项研究纳入了超声检查中至少有一个子宫肌层病变≥3 cm 的女性。这些患者根据症状和超声特征根据三级诊断算法进行分类。 “白色”患者每年接受为期 2 年的电话随访，“绿色”患者在 6、12 和 24 个月时接受临床和超声随访，“橙色”患者接受手术。我们进一步开发了一个风险分类系统来对恶性肿瘤风险进行分层。纳入了 2,268 名女性，目标病变在 2,158 名 (95.1%) 中被分类为良性，在 58 名 (2.6%) 中被分类为其他恶性肿瘤，在 52 名 (2.3%) 中被分类为间叶性子宫恶性肿瘤。患者。多变量分析时，年龄 (OR 1.05 (95% CI 1.03-1.07)、肿瘤直径 >8 cm (OR 5.92 (95% CI 2.87-12.24))、不规则边缘 (OR 2.34 (95% CI 1.09-4.98))、颜色评分=4 (OR 2.73 (95% CI 1.28-5.82))，被确定为恶性肿瘤的独立危险因素，而声影则是一个独立的保护因素 (OR 0.39 (95% CI 0.19-0.82)。该模型包括年龄作为连续变量和病变直径作为二分变量（截止值 81 mm），提供了最佳 AUC（0.87（95% CI 0.82-0.91）），开发了风险分级系统，并将患者分类为低风险。 （预测模型值 <0.39%：0/606 种恶性肿瘤，风险 0%）、中度风险（预测模型值 0.40%-2.2%：9/1,093 种恶性肿瘤，风险 0.8%）、高风险（预测模型值≥2.3%： 43/566 恶性肿瘤，风险 7.6%）。术前三级诊断算法和风险分级系统可以根据恶性肿瘤风险对女性进行分层。如果在多中心研究中得到证实，我们的研究结果将允许区分良性和 MUM，从而实现个性化。临床方法。无需声明。版权所有 © 2024。由 Elsevier Inc. 出版。

Differential diagnosis between benign uterine smooth muscle tumors and malignant counterpart is challenging. We evaluated the accuracy of a clinical and ultrasound based algorithm in predicting mesenchimal uterine malignancies (MUMs), including smooth muscle tumors of uncertain malignant potential (STUMPs).We report the twelve-months follow-up of an observational, prospective, single-centre study that included women with at least one myometrial lesion ≥3 cm on ultrasound examination. These patients were classified according to a three-class diagnostic algorithm, using symptoms and ultrasound features. "White" patients underwent annual telephone follow-up for 2 years, "Green" patients underwent a clinical and ultrasound follow-up at 6, 12 and 24 months and "Orange" patients underwent surgery. We further developed a risk class system to stratify the malignancy risk.2,268 women were included andtarget lesion was classified as benign in 2,158 (95.1%), as other malignancies in 58 (2.6%) an as mesenchymal uterine malignancies in 52 (2.3%) patients. At multivariable analysis, age (OR 1.05 (95% CI 1.03-1.07), tumor diameter >8 cm (OR 5.92 (95% CI 2.87-12.24), irregular margins (OR 2.34 (95% CI 1.09-4.98), color score=4 (OR 2.73 (95% CI 1.28-5.82), were identified as independent risk factors for malignancies, whereas acoustic shadow resulted in an independent protective factor (OR 0.39 (95% CI 0.19-0.82). The model, which included age as a continuous variable and lesion diameter as a dichotomized variable (cut-off 81 mm), provided the best AUC (0.87 (95% CI 0.82-0.91)). A risk class system was developed, and patients were classified as low-risk (predictive model value <0.39%: 0/606 malignancies, risk 0%), intermediate risk (predictive model value 0.40%-2.2%: 9/1,093 malignancies, risk 0.8%), high risk (predictive model value ≥2.3%: 43/566 malignancies, risk 7.6%).The preoperative three-class diagnostic algorithm and risk class system can stratify women according to risk of malignancy. Our findings, if confirmed in a multicentre study, will permit differentiation between benign and MUMs allowing a personalized clinical approach.Nothing to declare.Copyright © 2024. Published by Elsevier Inc.