CT Fluoroscopy Guided Thoracic Biopsies (CTTB) Are Highly Accurate and Safe: Outcomes and Predictive Modeling of Complications Utilizing Machine Learning

Abstract

CT guided transthoracic biopsy (CTTB) is an established, minimally invasive method for diagnostic evaluation of a variety of thoracic diseases. We assessed a large CTTB cohort diagnostic accuracy, complication rates, and developed machine learning models to predict complications. We retrospectively identified 796 CTTB patients in a tertiary hospital (5-year interval). We gathered and coded patient demographics, characteristics of each lesion biopsied, type of biopsy, diagnostic yield, type of diagnosis, and complication rates. Statistical analyses included summary statistics, multivariate logistic regression and machine learning (neural network) methods. Seven hundred ninety-six CTTBs were performed (43% fine needle aspirations, 5% core biopsies, 52% both). Diagnostic yield was 97.0% (73.9% malignant, 23.1% benign). Complications occurred in 14.7% (12.7% minor, 2.0% major). The most common complication was pneumothorax (13.1%), mostly minor. Multivariate logistic regression models could predict severity of complications with accuracies ranging from 65.5% to 83.5%, with smaller lesion dimension the strongest predictor. Type of biopsy was not a statistically significant predictor. A neural network model improved accuracy to 77.0%-94.2%. CTTB performed by thoracic radiologists in a tertiary hospital demonstrate excellent diagnostic yield (97.0%) with a low clinically important complication rate (2.0%). Machine learning methods including neural networks can accurately predict the likelihood of complications, offering pathways to potentially improve patient selection and procedural technique, in order to further optimize the risk-benefit ratio of CTTB.