Published in:
01-09-2017 | Chest
Non-specific benign pathological results on transthoracic core-needle biopsy: how to differentiate false-negatives?
Authors:
Jung Im Kim, Chang Min Park, Hyungjin Kim, Jong Hyuk Lee, Jin Mo Goo
Published in:
European Radiology
|
Issue 9/2017
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Abstract
Objectives
To determine the negative predictive value (NPV) of non-specific benign results from cone-beam CT (CBCT)-guided transthoracic core-needle biopsy (TTNB) and identify predicting factors for false-negative for malignancies.
Methods
From January 2009–December 2011, 1,108 consecutive patients with 1,116 lung lesions underwent CBCT-guided TTNB using an 18-gauge coaxial cutting needle. Among them, 226 patients with 226 TTNBs, initially diagnosed as non-specific benign, were included in this study. The medical charts, radiological or pathological follow-ups were reviewed to classify false-negative and true-negative results and to identify which variables were associated with false-negatives.
Results
Of 226 lesions, 24 (10.6%) were finally confirmed as malignancies and 202 (89.4%) as benign, of which the NPV was 89.4% (202/226). Multivariate analysis revealed that part-solid nodule (PSN) (odds ratio (OR), 3.95; P = 0.022), a biopsy result of ‘granulomatous inflammation’ (OR, 0.04; P = 0.022), and exact location of needle tip within targets (OR, 0.37; P = 0.045) were significantly associated with false-negatives among initial non-specific benign biopsy results.
Conclusion
The NPV of the non-specific benign biopsy was 89.4%. PSN was a significant positive indicator, but a biopsy result of ‘granulomatous inflammation’ and exact location of needle tip within targets were significant negative indicators for false-negatives.
Key Points
• The negative predictive value of the non-specific benign biopsy was 89.4%.
• A part-solid nodule is a significant predictor for false-negative biopsy (OR = 3.95).
• Pathological diagnosis of granulomatous inflammation is a robust indicator for ‘true-negatives’.
• Identifying needle tip within target lesions is a significant predictor for ‘true-negatives’.