Diagnostic Yield for Cancer and Diagnostic Accuracy of Computed Tomography–guided Core Needle Biopsy of Subsolid Pulmonary Lesions
Bibliographic record
Abstract
PURPOSE: We aimed to determine the diagnostic yield for cancer and diagnostic accuracy of computed tomography-guided core needle biopsy (CTNB) in subsolid pulmonary lesions. MATERIALS AND METHODS: Fifty-two biopsies of 52 subsolid lesions in 51 patients were identified from a database of 912 lung biopsies and analyzed for the diagnostic yield for cancer and diagnostic accuracy of core CTNB diagnosis as well as complication rates. RESULTS: When indeterminate biopsy results were included in the analysis, the diagnostic yield for cancer was 80.8% and the diagnostic accuracy of core needle biopsy was 84.6% (n=52). It was 85.7% and 91.7%, respectively, when indeterminate results were excluded (n=48) and 82.4% and 82.4%, respectively, for biopsies with surgical confirmation (n=17). Attenuation was statistically significant for diagnostic yield for cancer (P=0.028) and diagnostic accuracy of core needle biopsy (P=0.001) when the indeterminate results were excluded (n=48). Attenuation and size were not statistically significant for diagnostic yield for cancer and diagnostic accuracy of needle biopsy (n=52), and size was not statistically significant for either when the indeterminate results were excluded. These results were achieved without any major complications as per the Society of Interventional Radiology Standards of Practice. CONCLUSIONS: CTNB offers a high yield in establishing a histopathologic diagnosis of subsolid pulmonary lesions, with both ground-glass and solid-predominance. The pure ground-glass category of lesions requires further research to determine the true diagnostic yield and diagnostic accuracy of core needle biopsies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".