Improved prostate cancer grading by incorporating Gleason pattern quantification, invasive cribriform and intraductal carcinoma in the new QUICC-score.

Authors

Lisa J Kroon, Cláudia Cruz Oliveira, Eva Hollemans, Chris H Bangma, Monique J Roobol, Michelle R Downes, Ngoc-Nhu Jennifer Nguyen, Roselyne Choiniere, Theodorus H van der Kwast, David van Klaveren, Geert J L H van Leenders

Published in

Annals of diagnostic pathology. Volume 85. Pages 152675. Jun 26, 2026. Epub Jun 26, 2026.

Abstract

Pathological Gleason grading is the cornerstone of risk assessment in prostate cancer (PCa) patients. Detailed Gleason pattern (GP) quantification and presence of invasive cribriform and intraductal carcinoma (CR/IDC) have recently been recognized to optimize risk stratification. This proof-of principle-study aimed to develop a comprehensive numerical pathological score including GP4 and 5 percentage and CR/IDC presence in radical prostatectomy (RP) to predict biochemical recurrence-free survival (BCRFS) and metastasis-free survival (MFS). The novel QUICC score was developed using RP data from 835 patients who had undergone RP in one medical center in The Netherlands (2000-2017). Regression coefficients of the Fine and Gray model that discriminated best for BCRFS were translated into a point score, which was externally validated in 435 patients who had undergone RP in Canada (2010-2017). Predictive performance was measured using area under the time-dependent receiver-operating characteristic curves (AUC). At 10-year follow-up in the development cohort, 234 patients experienced BCR and 72 metastasis. The QUICC score outperformed Grade Groups for predicting BCRFS and MFS in both the development cohort (AUC BCRFS 0.83 versus 0.73; MFS 0.90 versus 0.78) and the validation cohort (AUC BCRFS 0.69 versus 0.65; MFS 0.83 versus 0.76). The QUICC score attributes 1 point per percent GP4, 2 points per percent GP5, and 100 points if CR/IDC is present, adding up to a maximum of 300 points. This provides a simpler risk estimate aiming to improve interpretation of pathological variables in clinical nomograms for clinicians and patients.

PMID:
42391874
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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