To develop and prospective validate an ultrasound (US) prediction model to differentiate between benign and malignant subpleural pulmonary lesions (SPLs).
This study was conducted retrospectively from July 2017 to December 2018 (development cohort [DC], n = 592) and prospectively from January to April 2019 (validation cohort [VC], n = 220). A total of 18 parameters of B-mode US and contrast-enhanced US (CEUS) were acquired. Based on the DC, a model was developed using binary logistic regression. Then its discrimination and calibration were verified internally in the DC and externally in the VC, and its diagnostic performance was compared with those of the existing US diagnostic criteria in the two cohorts. The reference criteria were from the comprehensive diagnosis of clinical-radiological-pathological made by two senior respiratory physicians.
The model was eventually constructed with 6 parameters: the angle between lesion border and thoracic wall, basic intensity, lung-lesion arrival time difference, ratio of arrival time difference, vascular sign, and non-enhancing region type. In both internal and external validation, the model provided excellent discrimination of benign and malignant SPLs (C-statistic: 0.974 and 0.980 respectively), which is higher than that of “lesion-lung AT difference ≥ 2.5 s” (C-statistic: 0.842 and 0.777 respectively,
The prediction model integrating multiple parameters of B-mode US and CEUS can accurately predict the malignancy probability, so as to effectively differentiate between benign and malignant SPLs, and has better diagnostic performance than the existing US diagnostic criteria.