Three-dimensional modelling and printing for preoperative planning in the resection of an ischiorectal epithelioid sarcoma: a case report
Bibliographic record
Abstract
Epithelioid sarcomas are rare malignant mesenchymal tumours, accounting for approximately 3–4 % of pediatric soft-tissue sarcomas, and present unique surgical challenges due to their infiltrative nature and proximity to critical anatomical structures. While three-dimensional modelling has emerged as a valuable adjunct in preoperative planning for complex tumours, its use in pediatric soft-tissue sarcomas is rare, with no described cases involving epithelioid sarcomas. A 13-year-old girl presented with a year-long history of perineal pain and a palpable mass. Imaging revealed a tumour involving the rectum and external sphincter, abutting the posterior vaginal wall. Biopsy confirmed epithelioid sarcoma and staging showed indeterminate pulmonary nodules. She underwent neoadjuvant chemoradiotherapy. Two surgical options were considered: abdominoperineal resection with hysterectomy or vaginal preservation with increased risk of residual disease. A three-dimensional model with 2cm margins was created to aid decision-making. After multidisciplinary review and model consultation, the patient opted for vaginal preservation. She underwent abdominoperineal resection with VRAM flap reconstruction. Final pathology confirmed negative margins. Nearly three years later, she developed a pancreatic tail metastasis and progressive pulmonary lesions, which were treated with distal pancreatectomy, splenectomy, radiotherapy, and cryoablation. Additional metastases in the stomach, scalp, retroperitoneum, and flank are being managed with palliative radiotherapy. Three-dimensional printed models can be helpful in the preoperative planning for the resection of tumours of the ischiorectal region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| 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".