Impact of tumour thickness on survival after radical radiation and surgery in malignant pleural mesothelioma
Bibliographic record
Abstract
Tumour thickness was assessed to determine if this parameter could refine patients' selection for multimodality therapy in malignant pleural mesothelioma. We reviewed 65 consecutive treatment-naïve malignant pleural mesothelioma patients undergoing surgery for mesothelioma after radiation therapy (SMART). Total tumour thickness was determined by measuring the maximal thickness on nine predefined sectors on the chest wall, mediastinum and diaphragm. After a median follow-up of 19 months, 40 patients (62%) developed recurrence and 36 died (55%). Total tumour thickness, ranging between 2.4 and 21 cm (median 6.9 cm), correlated with tumour volume (p<0.0001, R2=0.29) and maximum standardised uptake value (p=0.006, R2=0.11). Total tumour thickness had a significant impact on overall survival and disease-free survival in univariate analysis. In multivariate analysis, total tumour thickness remained an independent predictor of survival (p=0.02, hazard ratio (HR) 1.12, 95% CI 1.02–1.23) and disease-free survival (p=0.01, HR 1.13, 95% CI 1.03–1.24) along with epithelial histologic subtype (p<0.0001, HR 0.25, 95% CI 0.13–0.50) and pN2 disease (p=0.03, HR 2.15, 95% CI 1.07–4.33). Diaphragmatic tumour thickness correlated best with time to recurrence (p=0.002, R2=0.22) and time to death (p=0.003, R2=0.2). The impact of tumour thickness on survival and disease-free survival independent of histologic subtypes and nodal disease is extremely encouraging. This parameter could potentially be used to refine the clinical staging of malignant pleural mesothelioma and optimise patient selection for radical treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".