Factors predicting recurrence in thymic epithelial neoplasms
Bibliographic record
Abstract
OBJECTIVES: Thymic epithelial neoplasms (TENs) represent a heterogeneous group of rare thoracic malignancies. We analysed the clinicopathological features, survival outcomes, risk factors, and patterns of recurrence in patients undergoing resection. METHODS: Records were reviewed for adult patients with TEN who underwent resection from 2006 to 2019. Survival rates were assessed using the Kaplan-Meier method. Univariable and multivariable analyses were performed using the log-rank test and Cox proportional hazards model. RESULTS: A total of 100 patients were analysed (51 females, median age 58 years). Thymoma was the most common histology (n = 92), followed by thymic carcinoma (n = 5) and thymic neuroendocrine tumour (n = 3). Stage II (Masaoka) tumours were most common (n = 51), followed by stage I (n = 27). World Health Organization B2/B3 was the most prominent histological subtype (n = 34). Complete resection (R0) was achieved in 91 patients: 86/92 thymoma, 4/5 thymic carcinoma and 1/3 neuroendocrine tumour. The most common treatment modality was surgery alone in 72 patients, followed by surgery and radiation therapy in 24, and adjuvant chemoradiotherapy in 3 patients. Only one patient with thymic carcinoma received neoadjuvant chemotherapy. The 10-year overall and disease-free survival rates were 86.6% and 83.9%, respectively. Recurrence was most common in neuroendocrine tumours (3/3). Risk factors for recurrence identified on multivariable analyses were: R1/2 resection (hazard ratio 9.30; 95% confidence interval 1.82-36.1), TEN subtype (hazard ratio 8.08; 95% confidence interval 1.24-34.6), and presence of lymphovascular invasion (hazard ratio 9.56; 95% confidence interval 2.56-25.8). CONCLUSIONS: Complete resection remains critical in patients with TEN. Incomplete resection, high-risk histology, and lymphovascular invasion highlight the need for effective adjuvant modalities. Given the rarity of these diseases, emphasis must be placed on collaborative research conducted on TEN.
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How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".