Metastatic Prostatic Adenocarcinoma in Patient With Muir–Torre Syndrome Misdiagnosed as Metastatic Sebaceous Carcinoma: Case Report and Systematic Literature Review
Bibliographic record
Abstract
Muir-Torre syndrome (MTS) is a rare autosomal dominant condition characterized by the presence of at least one cutaneous sebaceous tumor and one visceral malignancy, arising mostly from the gastrointestinal tract. We present the case of a 63-year-old man with several cutaneous and visceral neoplasias in the context of MTS, and a pelvic lymph node lesion diagnosed initially as metastatic sebaceous carcinoma, but later identified as metastasis from a newly diagnosed prostatic adenocarcinoma. Histological similarities between these 2 lesions are discussed. A systematic literature review was conducted evaluating all published cases of patients with MTS in which metastases were reported. Eighteen articles were included in the final synthesis, representing 20 patients with a total of 26 metastases. Seventeen patients (85%) exhibited metastases originating from MTS-related neoplasms, whereas only 2 patients (11%) exhibited metastases from concomitant malignancies. Of the 85% of patients with metastases from MTS-related malignancies, most originated from noncutaneous sources (78% from visceral neoplasms and 22% from sebaceous carcinomas). When stratifying according to metastases, 23 cases (88%) originated from MTS-related lesions, whereas only 3 (12%) originated from unrelated malignancies. Our findings thus demonstrate that most metastases found in MTS patients (88%) do indeed originate from MTS-related neoplasms. Nevertheless, it remains imperative that a broad differential diagnosis is maintained when assessing a novel lesion, to avoid misdiagnoses, as in the present case, with significant therapeutic and prognostic implications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".