Current Management of Desmoid Tumors
Bibliographic record
Abstract
Importance: Desmoid tumor (DT) is a rare and locally aggressive monoclonal, fibroblastic proliferation characterized by a variable and often unpredictable clinical course. Previously, surgery was the standard primary treatment modality; however, within the past decade, a paradigm shift toward less-invasive management has been introduced and an effort to harmonize the strategy among clinicians has been made. To update the 2020 global evidence-based consensus guideline on the management of patients with DT, the Desmoid Tumor Working Group convened a 1-day consensus meeting in Milan, Italy, on June 30, 2023, under the auspices of the European Reference Network on Rare Adult Solid Cancers and Sarcoma Patient Advocacy Global Network, the Desmoid Foundation Italy, and the Desmoid Tumor Research Foundation. The meeting brought together over 90 adult and pediatric sarcoma experts from different disciplines as well as patients and patient advocates from around the world. Observations: The 2023 update of the global evidence-based consensus guideline focused on the positioning of local therapies alongside surgery and radiotherapy in the treatment algorithm as well as the positioning of the newest class of medical agents, such as γ-secretase inhibitors. Literature searches of MEDLINE and Embase databases were performed for English-language randomized clinical trials (RCTs) of systemic therapies to obtain data to support the consensus recommendations. Of the 18 full-text articles retrieved, only 4 articles met the inclusion criteria. The 2023 consensus guideline is informed by a number of new aspects, including data for local ablative therapies such as cryotherapy; other indications for surgery; and the γ-secretase inhibitor nirogacestat, the first representative of the newest class of medical agents and first approved drug for DT. Management of DT is complex and should be carried out exclusively in designated DT referral centers equipped with a multidisciplinary tumor board. Selection of the appropriate strategy should consider DT-related symptoms, associated risks, tumor location, disease morbidities, available treatment options, and preferences of individual patients. Conclusions and Relevance: The therapeutic armamentarium of DT therapy is continually expanding. It is imperative to carefully select the management strategy for each patient with DT to optimize tumor control and enhance quality of life.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".