A clinical ultrasound algorithm to identify uterine sarcoma and smooth muscle tumors of uncertain malignant potential in patients with myometrial lesions: the MYometrial Lesion UltrasouNd And mRi study

Abstract

Background

Differential diagnosis between benign uterine smooth muscle tumors and malignant counterpart is challenging.

OBJECTIVE

To evaluate the accuracy of a clinical and ultrasound based algorithm in predicting mesenchymal uterine malignancies, including smooth muscle tumors of uncertain malignant potential.

STUDY DESIGN

We report the 12-month follow-up of an observational, prospective, single-center study that included women with at least 1 myometrial lesion ≥3 cm on ultrasound examination. These patients were classified according to a 3-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.

RESULTS

Two thousand two hundred sixty-eight women were included and target lesion was classified as benign in 2158 (95.1%), as other malignancies in 58 (2.6%) an as mesenchymal uterine malignancies in 52 (2.3%) patients. At multivariable analysis, age (odds ratio 1.05 [95% confidence interval 1.03–1.07]), tumor diameter >8 cm (odds ratio 5.92 [95% confidence interval 2.87–12.24]), irregular margins (odds ratio 2.34 [95% confidence interval 1.09–4.98]), color score=4 (odds ratio 2.73 [95% confidence interval 1.28–5.82]), were identified as independent risk factors for malignancies, whereas acoustic shadow resulted in an independent protective factor (odds ratio 0.39 [95% confidence interval 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 area under the curve (0.87 [95% confidence interval 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%: 9/1093 malignancies, risk 0.8%), high risk (predictive model value ≥2.3%: 43/566 malignancies, risk 7.6%).

Conclusion

The preoperative 3-class diagnostic algorithm and risk class system can stratify women according to risk of malignancy. Our findings, if confirmed in a multicenter study, will permit differentiation between benign and mesenchymal uterine malignancies allowing a personalized clinical approach.