Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: A predictive model for identifying false-negative results

Abstract

Abstract Background: Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, however, it is vital that false-negative results are accurately identified. Accordingly, the present study was designed with the goal of identifying relevant predictors for the construction of a model capable of predicting false-negative cases among patients undergoing CTB for PNs who receive non-malignant results. Materials and Methods: Consecutive patients from two centers who received CTB-based non-malignant pathological results when undergoing evaluation for PNs from January 2016 to December 2020 were retrospectively evaluated. A training cohort was used to identify factors that were predictive of false negative results, enabling the establishment of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. Results: The training cohort enrolled 102 patients with PNs exhibiting CTB-based non-malignant pathological findings, each of whom underwent CTB for a single nodule. Of these patients, 85 and 17 respectively exhibited true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. These two predictors were then combined to establish a predictive model. The area under the receiver operating characteristic curve (AUC) for this model was 0.945, with corresponding sensitivity and specificity values of 88.2% and 87.1%. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. Conclusions: The predictive model developed herein exhibited good diagnostic utility when identifying false-negative CTB-based non-malignant pathological results among patients with PNs.