The Utility of Chest Imaging for Surveillance of Atypical Lipomatous Tumors
Bibliographic record
Abstract
Synopsis. For ALTs, the utility of chest surveillance has not been well defined. This study suggests that chest imaging does not have a significant role in the surveillance of ALTs. Advanced local imaging and more intensive chest surveillance may be considered in cases of local recurrence. Background. Unlike other soft tissue sarcomas, atypical lipomatous tumors (ALTs) are thought to have a low propensity for metastasis. Despite this, a standard of care for pulmonary metastasis (PM) surveillance has not been established. This study aimed to evaluate the utility of chest imaging for PM surveillance following ALT excision. Methods. This was a multi-institution, retrospective review of all patients with primary ALTs of the extremities or superficial torso who underwent excision between 2006 and 2018. Minimum follow-up was two years. Long-term survival was evaluated using the Kaplan–Meier method. Results. 190 patients with ALT were included. Average age was 61.7 years and average follow-up was 58.6 months (24 to 180 months). MDM2 testing was positive in 88 patients (46.3%), and 102 (53.7%) did not receive MDM2 testing. 188 patients (98.9%) had marginal excision, and 127 (66.8%) had marginal or positive margins. Patients received an average of 0.9 CT scans and 1.3 chest radiographs over the surveillance period. 10-year metastasis-free survival was 100%, with no documented deaths from disease. Conclusions. This study suggests that chest imaging does not have a significant role in PM surveillance following ALT excision, but advanced local imaging and chest surveillance may be considered in cases of local recurrence or concern for dedifferentiation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".