一种临床超声算法，可鉴定肌层病变患者的子宫肉瘤和具有不确定恶性潜力的平滑肌肿瘤：肌层病变超声和MRI研究

良性子宫平滑肌肿瘤与恶性对应物之间的差异诊断是具有挑战性的。要评估基于临床和超声的算法在预测间充质性子宫内恶性肿瘤方面的准确性进行超声检查。这些患者使用症状和超声特征根据三级诊断算法对这些患者进行分类。 “白人”患者进行了每年的电话随访2年，“绿色”患者在6、12和24个月接受了临床和超声随访，“橙色”患者接受了手术。我们进一步开发了一个风险类系统来分层恶性风险。包括成千上万的六十八名妇女，目标病变在2158年（95.1％）分类为良性，因为58（2.6％）的其他恶性肿瘤为58例（2.6％）的52例（2.6％）患者的患者。在多变量分析中，年龄（优势比1.05 [95％置信区间1.03-1.07]），肿瘤直径> 8 cm（优势比5.92 [95％置信区间2.87-12.24]），不规则置信率比值比2.34 [95％置信区间[95％置信区间] [95％置信区间1.09-9-4.98]，彩色。 1.28-5.82]），被确定为恶性肿瘤的独立风险因素，而声阴影导致独立的保护因子（优势比0.39 [95％置信区间0.19-0.82 [）[）。该模型将年龄作为连续变量和病变直径作为二分法变量（截止81毫米），在曲线下提供了最佳面积（0.87 [95％置信区间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%).The preoperative 3-class diagnostic算法和风险类系统可以根据恶性肿瘤的风险对女性进行分层。如果在一项多中心研究中得到证实，我们的发现将允许良性和间质子宫恶性肿瘤之间的分化，允许采用个性化临床方法。

Differential diagnosis between benign uterine smooth muscle tumors and malignant counterpart is challenging.To evaluate the accuracy of a clinical and ultrasound based algorithm in predicting mesenchymal uterine malignancies, including smooth muscle tumors of uncertain malignant potential.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.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%).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.