Computed tomography-guided Tru-cut biopsy of lung mass, as an important diagnostic tool: Histopathological characteristics, age, sex distribution, and risk factors in Misurata Cancer Center, Libya
Bibliographic record
Abstract
Abstract Background: Lung biopsies may be classified according to the method of access or by the reason for biopsy. Tru-cut biopsy is a simple, relatively safe, rapid, and reliable technique for the diagnosis of lung mass lesions, particularly with the aid of computed tomography (CT) scan. This procedure is commonly used worldwide to detect different kinds of lung tumors, but its usefulness in Libya is not yet studied. Objectives: The aim of this study was to assess the usefulness of Tru-cut biopsy as a diagnostic method in lung tumors and also to determine the incidence of lung cancer in various ages and sex groups and in relation to smoking data. Patients and Methods: Retrospective study was done on 136 patients who underwent CT-guided Tru-cut biopsy from January 2012 to March 2015. The included patients have lung mass lesions suspected to be neoplastic according to chest radiograph and/or CT scan. Results: We found biopsies and histopathology reports of 136 cases. The patients’ age was from 20 to 85 years with the peak incidence in the sixth and seventh decades. The benign lesions were 18 (13.23%), and malignant lesions were 118 (86.76%) documented microscopically. The most common tumor was squamous cell carcinoma (25.17%), followed by adenocarcinoma (23.02%) and small cell lung carcinoma (17.26%). Nonspecific inflammation was found in 8.63%. Conclusion: CT-guided Tru-cut biopsy is a simple, safe, and reliable procedure with high diagnostic accuracy for the diagnosis and subtyping of lung cancer. The definitive result of malignancy is a base for correct decision for the patient's treatment after careful clinical correlation with performance state, radiology findings, and complete serological investigations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".