Outcomes and prognostic factors in primary intrathoracic soft tissue sarcoma
Bibliographic record
Abstract
Background: Intrathoracic soft tissue sarcomas are rare and heterogenous tumors with limited data guiding their management. Due to anatomical complexity and delayed diagnosis, treatment can be challenging. This study aimed to assess clinical outcomes and prognostic factors in intrathoracic soft tissue sarcoma patients treated at a high-volume referral sarcoma center. Methods: This bi-institutional retrospective cohort study (2007-2022) analyzed patients with histopathologically confirmed intrathoracic soft tissue sarcomas. Data from two prospective registries were used to assess overall survival (OS), local recurrence (LR), and distant metastasis (DM) via Kaplan-Meier, cumulative incidence, and multivariable Cox regression. Results: Among 49 patients (median age: 56 years, 55.1% male), 40 (81.6%) had localized and 9 (18.4%) had metastatic disease at diagnosis. The median follow-up was 15.6 months. The median OS was 37.1 months for localized, 5.9 months for metastatic cases. Among localized cases, 55% had recurrences, evenly split between local and distant. LR incidence was 39.4% at 12 months, 53.7% at 24 months; DM incidence was 38.8% and 53.4%. OS was worse in patients >65 years [hazard ratio (HR): 2.69, P=0.04] and with metastatic disease (HR: 4.84, P=0.02). DM risk was higher in grade 2 (HR: 19.38, P=0.03) and grade 3 tumors (HR: 12.65, P=0.02) and those in the lungs, pleura, or diaphragm (HR: 3.56, P=0.046). LR was not associated with grade, margin status, or perioperative therapy. Treatment modality had no impact on DM. Conclusions: Despite a small sample, this is the largest clinical cohort on intrathoracic soft tissue sarcomas, which carry high local and distant recurrence risks. Worse outcomes were linked to age >65 years, metastatic presentation, higher grade, and lung, pleura, or diaphragm location. Given their rarity, multi-institutional studies are needed to refine risk factors, treatment, and outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".