Computed Tomography-Guided Transthoracic Needle Biopsy: Predictors for Diagnostic Failure and Tissue Adequacy for Molecular Testing

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Abstract

Background: Adequate and representative tissue from lung tumor is important in the era of precision medicine. The aim of this study is to identify detailed procedure-related variables and factors influencing diagnostic success and tissue adequacy for molecular testing in CT-guided TTNB. Methods: Consecutive patients undergoing CT-guided TTNB were retrospectively enrolled between January 2013 and May 2020. Multivariate analysis was performed for predictors for diagnostic accuracy and tissue adequacy for molecular testing. Logistic regression was used to identify risk factors for procedure-related complications. Results: A total of 2,556 patients undergoing CT-guided TTNB were enrolled and overall success rate was 91.5% (2,338/2,556). For lung nodules ≤3 cm, predictors for diagnostic success included coaxial needle use [OR = 0.34 (0.16–0.71), p = 0.004], CT scan slice thickness of 2.5 mm [OR = 0.42 (0.15–0.82), p = 0.011] and additional prefire imaging [OR = 0.31 (0.14–0.68), p = 0.004]. For lung tumor >3 cm, ground glass opacity part more than 50% [OR = 7.53 (2.81–20.23), p < 0.001] or presence of obstructive pneumonitis [OR = 2.31 (1.53–3.48), p < 0.001] had higher risk of diagnostic failure. For tissue adequacy, tissue submitted in two cassettes (98.9 vs. 94.9%, p = 0.027) was a positive predictor; while male (5.7 vs. 2.5%, p = 0.032), younger age (56.61 ± 11.64 vs. 65.82 ± 11.98, p < 0.001), and screening for clinical trial (18.5 vs. 0.7%, p < 0.001) were negative predictors. Conclusions: Using a coaxial needle, with thin CT slice thickness (2.5 mm), and obtaining additional prefire imaging improved diagnostic success, while obtaining more than two tissue cores and submitting in two cassettes improved tissue adequacy for molecular testing.

Original languageEnglish
Article number650381
JournalFrontiers in Medicine
Volume8
DOIs
Publication statusPublished - 2021 May 19

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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