A nomogram based on clinical and ultrasound characteristics for predicting peripheral nerve schwannomas in soft tissue.

Aims: To develop a nomogram that integrates clinical and ultrasound (US) characteristics for the preoperative prediction of peripheral nerve schwannomas in soft tissue.

Material and methods: A retrospective analysis was conducted on 301 patients with soft tissue masses who underwent surgical excision and preoperative US evaluation. Clinical data and US features were collected and analyzed. Univariate and multivariate regression analyses were performed to identify independent predictors; subsequently, a nomogram was developed for predicting schwannomas. The performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Additionally, internal validation was performed using 10-fold cross-validation with 1000 iterations.

Results: Seven independent predictors were identified, including target sign, rat tail sign, split fat sign, shape, layer, vascularity, and age. The nomogram demonstrated favorable discrimination and calibration, with an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.902-0.966). Furthermore, decision curve analysis (DCA) confirmed the nomogram's clinical utility across a wide range of risk thresholds (0.01-0.93). Internal validation yielded a corrected AUC of 0.921 (95% CI: 0.917-0.924).

Conclusion: This nomogram provides clinicians with a quantitative and visual tool for preoperative prediction of schwannomas in soft tissue, thereby improving diagnostic accuracy and assisting in clinical decision-making.