MP79-11 IMPACT OF KI67 LABELING INDEX ON PROGRESSION AND DEATH OF PROSTATE CANCER POST-PROSTATECTOMY: COMPARISON OF VISUAL AND AUTOMATED SCORING
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Markers II1 Apr 2014MP79-11 IMPACT OF KI67 LABELING INDEX ON PROGRESSION AND DEATH OF PROSTATE CANCER POST-PROSTATECTOMY: COMPARISON OF VISUAL AND AUTOMATED SCORING Ta Ben-Zvi, Patrice Desmeules, Hélène Hovington, Caroline Léger, André Caron, Louis Lacombe, Yves Fradet, Bernard Têtu, and Vincent Fradet Ta Ben-ZviTa Ben-Zvi More articles by this author , Patrice DesmeulesPatrice Desmeules More articles by this author , Hélène HovingtonHélène Hovington More articles by this author , Caroline LégerCaroline Léger More articles by this author , André CaronAndré Caron More articles by this author , Louis LacombeLouis Lacombe More articles by this author , Yves FradetYves Fradet More articles by this author , Bernard TêtuBernard Têtu More articles by this author , and Vincent FradetVincent Fradet More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.2514AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Even after radical prostatectomy (RP), prostate cancer (PCa) demonstrates high variability in its aggressiveness, currently imperfectly predictable. Better prognostic markers are needed. The tumor proliferative index has shown interesting associations in PCa. Its application has limitations: low expression level, lack of uniformity and consistency in quantification. The purpose of this study was to evaluate whether the tumor proliferation index, as assessed with Ki67 by virtual microscopy and digital image analysis (DIA), could predict the occurrence of progression and death in men with PCa after radical prostatectomy (RP). METHODS A tissue microarray series of PCa (251 men; RP 1998-2006) at CHU-L’Hôtel-Dieu de Québec was immunostained for Ki67 (Mib-1, DAKO) and digitized. Ki67 positive nuclei in malignant cells (%) were assessed manually on virtual slides. For DIA, tissue segmentation and staining quantification algorithms using CalopixTM (Tribvn, distributed by AGFA Healthcare) were developed, and Histoscore (H-score) was calculated. Patients underwent complete follow-up (bi-annual PSA and clinical follow-up). Adjusted hazard ratios (HR) of progression (biochemical recurrence (BR); PSA ≥0.2ng/ml), death of disease (DOD), and 95% confidence intervals (CI) were estimated via Cox proportional hazard regression model. RESULTS Kaplan-Meier analyses showed that high Ki67 labeling index (≥4.2%) was associated with increased risk of BR and DOD (logrank p<0.0131; p<0.0101), when assessed manually. H-score (≥7%) utilizing DIA results demonstrated increased risk for BR and DOD (logrank p<0.0085; p<0.0004). In multivariate Cox regression models, correcting for age, PSA and grade, a high proliferation index was significantly associated with BR and DOD. Meanwhile, both univariate and multivariate Cox regression models have shown that Ki-67 is not associated with the risk of death from other causes. CONCLUSIONS In this cohort, high Ki67 index is an independent prognostic factor for BR and DOD. Ki67 quantification by DIA reproduced all significant associations observed with manual scoring. These findings support the use of automated DIA tools for objective and reproducible biomarker quantification and subsequent integration in future clinical algorithms. Multivariate cox proportional hazards regression analysis to predict biochemical recurrence and disease-specific mortality in prostate cancer, using Ki67 proliferative index Biochemical recurrence P-value Biochemical recurrence P-value Hazard ratio (95% CI) Hazard ratio (95% CI) Visual scoring < 4.2% 1.0 1.0 > 4.2% 1.8 (1.1-2.9) 0.02 5,6 (1,5-20,9) 0.01 Digital image analysis H-Score < 7% 1.0 1.0 H-Score > 7% 1.7 (1.1-2.7) 0.03 9.0 (2,1-38,4) 0.003 © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e933-e934 Advertisement Copyright & Permissions© 2014Metrics Author Information Ta Ben-Zvi More articles by this author Patrice Desmeules More articles by this author Hélène Hovington More articles by this author Caroline Léger More articles by this author André Caron More articles by this author Louis Lacombe More articles by this author Yves Fradet More articles by this author Bernard Têtu More articles by this author Vincent Fradet More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".