Active surveillance of diffuse-type tenosynovial giant cell tumors: A retrospective, multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: Diffuse-type tenosynovial giant cell tumor (D-TGCT) is a mono-articular, soft-tissue tumor. Although it can behave locally aggressively, D-TGCT is a non-malignant disease. This is the first study describing the natural course of D-TGCT and evaluating active surveillance as possible treatment strategy. METHODS: This retrospective, multicenter study included therapy naïve patients with D-TGCT from eight sarcoma centers worldwide between 2000 and 2019. Patients initially managed by active surveillance following their first consultation were eligible. Data regarding the radiological and clinical course and subsequent treatments were collected. RESULTS: Sixty-one patients with primary D-TGCT were initially managed by active surveillance. Fifty-nine patients had an MRI performed around first consultation: D-TGCT was located intra-articular in most patients (n = 56; 95 %) and extra-articular in 14 cases (24 %). At baseline, osteoarthritis was observed in 13 patients (22 %) on MRI. Most of the patients' reported symptoms: pain (n = 43; 70 %), swelling (n = 33; 54 %). Eight patients (13 %) were asymptomatic. Follow-up data were available for 58 patients; the median follow-up was 28 months. Twenty-one patients (36 %) had radiological progression after 21 months (median). Eight of 45 patients (18 %) without osteoarthritis at baseline developed osteoarthritis during follow-up. Thirty-seven patients (64 %) did not clinically deteriorate during follow-up. Finally, eighteen patients (31 %) required a subsequent treatment. CONCLUSION: Active surveillance can be considered adequate for selected therapy naïve D-TGCT patients. Although follow-up data was limited, almost two-thirds of the patients remained progression-free, and 69 % did not need treatment during the follow-up period. However, one-fifth of patients developed secondary osteoarthritis. Prospective studies on active surveillance are warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".