Laparoscopic assisted percutaneous cryoablation of abdominal wall desmoid tumor: a case report of a novel technical approach
Bibliographic record
Abstract
• Pneumoperitoneum-assisted percutaneous cryoablation of abdominal wall desmoid tumor • Multidisciplinary approach expanded treatment eligibility in high-risk cases • Combination of laparoscopy and radiology enabled ablation in vicinity of bowel • Real-time visualization minimized bowel risks Desmoid tumors are rare fibroblastic growths originating in mesenchymal tissues. While lacking metastatic potential, these tumors display heterogenous clinical behaviour. They can frequently recur and encroach upon surrounding structures, causing significant morbidity. Traditional management has shifted from surgical resection to more conservative strategies. Cryoablation has emerged as a promising therapeutic option, especially for tumors in anatomically complex or surgically challenging locations. However, in cases involving tumors abutting visceral organs, the risk of cryoablation-induced injury such as bowel perforation and enterocutaneous fistula formation limits its application. We present a novel case of a 38-year-old female with a biopsy-proven abdominal wall desmoid tumor exhibiting progressive growth, worsening pain and radiologic proximity to the colon following two prior gynecologic surgeries, who underwent successful percutaneous cryoablation guided by pneumoperitoneum-assisted laparoscopic visualization. This approach facilitated real-time visualization of intra-abdominal structures and dynamic assessment of tumor-bowel separation. This obviated the need for bowel mobilization and permitted direct and safe ultrasound-guided percutaneous cryoablation. Postoperative recovery was uneventful with no complications observed and the patient became pain free two days after surgery. This case exemplifies how a multidisciplinary strategy, combining laparoscopy and interventional radiology expertise of cryoablation, can be used in the management of complex desmoid tumors to enhance safety and broaden treatment eligibility for patients with tumors in high-risk locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".