Immunohistochemical Screening of Upper Tract Urothelial Carcinomas for Lynch Syndrome Diagnostics: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To review the effect of universal screening of newly diagnosed upper tract urothelial carcinomas (UTUC) for mismatch repair (MMR) protein loss to aid in Lynch syndrome diagnostics. MATERIALS AND METHODS: Studies were identified through PubMed on December 1, 2021. Eligibility criteria were universal immunohistochemical analyses for at least 2 MMR proteins in unselected, consecutively collected UTUC cohorts. Exclusion criteria included reviews, case-reports, non-English language, and non-humans. Risk of bias was assessed using a modified Newcastle-Ottawa scale. Meta-analyses were performed to compare the association between clinical criteria and Lynch syndrome diagnoses. RESULTS: From 12 included studies, 1628 surgically removed UTUC from 1626 patients were screened for MMR protein loss. In 11 studies, 140 of the 1559 patients had tumors with loss (9.0%) with 80.7% showing loss of MSH2, MSH6, or both. In 7 studies, genetic testing confirmed Lynch syndrome diagnosis for 20 of 970 patients (2.1%). In 8 studies, 31 patients were given a clinical Lynch syndrome diagnosis (2.6%). In total, 51 assumed or verified Lynch syndrome patients were identified among 1087 patients (4.7%). Meta-analyses of 3 studies showed significant association between previous cancer diagnosis and Lynch syndrome-associated UTUC (P = .038). CONCLUSION: Despite the few studies conducted and lack of genetic testing, current data suggests that universal screening for MMR protein loss in UTUC may result in Lynch syndrome diagnoses in 4.7%. However, for the screening to be effective for Lynch syndrome diagnostics, follow-up investigations, such as genetic testing for MMR variants, are needed.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".