Identification of Numerical Chromosomal Changes Detected by Interphase Fluorescence In Situ Hybridization in High-Grade Prostate Intraepithelial Neoplasia as a Predictor of Carcinoma
Bibliographic record
Abstract
CONTEXT: High-grade prostate intraepithelial neoplasia (HPIN) is the most likely precursor of prostate cancer. The condition of many patients with a diagnosis of HPIN in prostate needle core biopsy could, if left untreated, progress to invasive cancer. Currently there is no available clinical, immunohistochemical, or morphologic criteria that are predictive of this progression. OBJECTIVE: To determine whether chromosomal instability in these precursor lesions could increase their predictive value for cancer detection. DESIGN: Dual-color interphase fluorescence in situ hybridization analysis was performed on archived prostate needle core biopsies from 54 patients with initial diagnosis of isolated HPIN and follow-up of 3 years or more. We used commercially available centromere probes for chromosomes 4, 7, 8, and 10. We had interpretable results in 44 patients as follows: (1) group A: 24 HPIN patients with persistent HPIN and/or benign lesions in the follow-up biopsies, and (2) group B: 20 HPIN patients with progression to prostate carcinoma. RESULTS: Twenty-five percent of the patients in group B displayed numeric chromosomal aberrations. Only 8.3% of the patients from group A had chromosomal abnormalities (P =.1). The observed overall chromosomal changes in HPIN were higher than those in normal or hyperplastic epithelium, with a statistically significant difference (P <.05). All aberrations were detected in the form of chromosomal gain. Overall, the commonest aberration was gain of chromosome 8, followed by gains of chromosomes 7 and 10. CONCLUSION: These results indicated that although no single numeric chromosomal abnormality could be assigned as a predictor of HPIN progression to carcinoma, the overall level of numeric chromosomal abnormalities shows a trend of elevation in HPIN patients whose condition subsequently progressed to carcinoma.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".