CT Fluoroscopy Guided Thoracic Biopsies (CTTB) Are Highly Accurate and Safe: Outcomes and Predictive Modeling of Complications Utilizing Machine Learning
| Autor: | Eduardo J. Mortani Barbosa, Nicholas Sachs |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Image-Guided Biopsy
Patient demographics Logistic regression Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Biopsy Humans Medicine Radiology Nuclear Medicine and imaging Ct fluoroscopy Retrospective Studies medicine.diagnostic_test business.industry medicine.disease Pneumothorax Fluoroscopy 030220 oncology & carcinogenesis Thoracic diseases Cohort Artificial intelligence Tomography X-Ray Computed business Complication computer |
| Zdroj: | Academic Radiology. 28:608-618 |
| ISSN: | 1076-6332 |
| DOI: | 10.1016/j.acra.2020.03.036 |
| Popis: | Purpose 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. Materials and Methods 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. Results 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%. Conclusion 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. |
| Databáze: | OpenAIRE |
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