A clinical ultrasound algorithm to identify uterine sarcoma and smooth muscle tumors of uncertain malignant potential (STUMPs) in patients with myometrial lesions: the MYLUNAR Study

Abstract

BackgroundDifferential 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). MethodsWe 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. Findings2,268 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%). ConclusionThe 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. FundingNothing to declare.