Development and Prospective Validation of an Ultrasound Prediction Model for the Differential Diagnosis of Benign and Malignant Subpleural Pulmonary Lesions: A Large Ambispective Cohort Study
Autor: | Ke Bi, De-meng Xia, Lin Fan, Xiao-fei Ye, Yi Zhang, Meng-jun Shen, Hong-wei Chen, Yang Cong, Hui-ming Zhu, Chun-hong Tang, Jing Yuan, Yin Wang |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Cancer Research
Logistic regression Malignancy 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine differential diagnosis Medicine RC254-282 Original Research lung ultrasound business.industry Ultrasound Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease prediction model medicine.anatomical_structure Oncology 030220 oncology & carcinogenesis Cohort Differential diagnosis business Nuclear medicine pulmonary lesion Thoracic wall contrast-enhanced ultrasound Contrast-enhanced ultrasound Cohort study |
Zdroj: | Frontiers in Oncology, Vol 11 (2021) Frontiers in Oncology |
Popis: | ObjectiveTo develop and prospective validate an ultrasound (US) prediction model to differentiate between benign and malignant subpleural pulmonary lesions (SPLs).MethodsThis 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.ResultsThe 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, P <0.001) and “AT ≥ 10 s” (C-statistic: 0.688 and 0.641 respectively, P <0.001) and the calibration curves of the model showed good agreement between actual and predictive malignancy probabilities. As for the diagnosis performance, the sensitivity and specificity of the model [sensitivity: 94.82% (DC) and 92.86% (VC); specificity: 92.42% (DC) and 92.59% (VC)] were higher than those of “lesion-lung AT difference ≥ 2.5 s” [sensitivity: 88.11% (DC) and 80.36% (VC); specificity: 80.30% (DC) and 75.00% (VC)] and “AT ≥ 10 s” [sensitivity: 64.94% (DC) and 61.61% (VC); specificity: 72.73% (DC) and 66.67% (VC)].ConclusionThe 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.Clinical Trial Registrationwww.chictr.org.cn, identifier ChiCTR1800019828. |
Databáze: | OpenAIRE |
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