Diagnostic Value of Computed Tomography Imaging Features in Malignant Pleural Mesothelioma
Bibliographic record
Abstract
BACKGROUND: Medical history, thoracentesis, and imaging features are usually the first steps in the investigation of a possible malignant pleural effusion (MPE). Unfortunately, the diagnostic yield of thoracentesis in this situation is suboptimal even if the procedure is repeated, especially in the context of malignant pleural mesothelioma (MPM). The next step for confirming the diagnosis, if clinically appropriate, is thoracoscopy, but not all patients are fit to undergo this procedure, so the diagnosis is then based on the medical history and imaging features only. OBJECTIVES: Our objective was to evaluate the diagnostic value of the medical history and imaging features in MPM. METHODS: We reviewed the imaging and medical charts of 92 patients with a final diagnosis of MPE included in our prospective medical thoracoscopy database. The clinical characteristics and imaging features of patients with primary MPE were compared with those of patients with secondary MPE. RESULTS: Male sex (82 vs. 59%, p = 0.02), asbestos exposure (58 vs. 10%, p < 0.001), and mediastinal (68 vs. 33%, p = 0.04), diaphragmatic (75 vs. 31%, p = 0.001) and circumferential pleural thickening (55 vs. 19% p = 0.001) were significantly more frequent in MPM patients. In a multivariate linear regression model, only asbestos exposure (OR 11.2; 95% CI 3.4-36.9) and circumferential pleural thickening (OR 4.7; 95% CI 1.6-13.9) were significantly associated with a diagnosis of MPM. CONCLUSION: In situations where it is impossible to obtain adequate pleural samples to differentiate MPM from a secondary pleural malignancy, the combination of circumferential pleural thickening and a history of asbestos exposure may be sufficient to make a clinical diagnosis.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".