Laparoscopic-Assisted Percutaneous Cryoablation of Abdominal Wall Desmoid Fibromatosis: Case Series and Local Experience
Bibliographic record
Abstract
Background: Desmoid tumors (DTs) are rare, non-metastatic but locally aggressive connective tissue neoplasms. While standard treatments include surgery, radiation, and ablation, current guidelines advocate active surveillance unless tumors progress or symptoms worsen. Cryotherapy has shown promise in treating DTs; however, its application in rectus abdominis DTs has been limited due to proximity to critical intra-abdominal structures. Methods: This case series describes a novel approach involving laparoscopic-assisted cryoablation in three patients with rectus abdominis DTs. Laparoscopic visualization was employed to improve tumor localization and procedural safety during percutaneous cryoablation. Results: The average tumor size was 7.4 cm, and a mean of 14 cryoprobes were used per case. All patients experienced complete symptom resolution. One patient developed a complication—injury to the inferior epigastric artery—requiring embolization. Follow-up imaging at three months showed significant tumor shrinkage and necrosis in two patients. The third patient had increased lesion volume due to post-procedural hematoma, although radiological markers of cryoablation efficacy were present. Conclusions: Laparoscopic-assisted cryoablation appears to be a feasible and effective technique for treating rectus abdominis DTs, providing symptom relief and favorable early tumor response. Further studies are warranted to evaluate long-term outcomes and validate this approach in broader clinical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".