PURPOSE: We determined clinical factors affecting the under grading of biopsy Gleason sum compared with prostatectomy pathology and developed a model predicting the probability of under grading. MATERIALS AND METHODS: We analyzed a cohort of 1,701 patients treated for prostate cancer at our institution between 1988 and 2007 with complete biopsy and pathological data available. Patients with a biopsy Gleason sum of 7 or less were included in our analysis. Cases were categorized as under graded or not under graded by comparing biopsy and radical prostatectomy Gleason sums. Logistic regression was used to determine the predictors of under grading based on clinical variables (race, age at diagnosis, body mass index, prostate weight, diagnostic prostate specific antigen, biopsy positive-to-total core ratio, maximal cancer percent in positive cores and time from diagnosis to surgery). A nomogram was developed to calculate the probability of under grading. Results were validated using bootstrapping. RESULTS: Under grading occurred in 46.6% of our cohort. Significant variables predicting under grading were age at diagnosis, biopsy Gleason sum, diagnostic prostate specific antigen, prostate weight, biopsy positive-to-total core ratio and maximal percent of cancer in cores (p <0.05). Nomogram predictive accuracy was 72.4%. CONCLUSIONS: The risk of Gleason sum under grading can be predicted to a satisfactory level using our nomogram. Predicting under grading would improve patient consulting and identify those who should consider repeat biopsy, ultimately enhancing the accuracy of prostate cancer diagnosis.
PURPOSE: We determined clinical factors affecting the under grading of biopsy Gleason sum compared with prostatectomy pathology and developed a model predicting the probability of under grading. MATERIALS AND METHODS: We analyzed a cohort of 1,701 patients treated for prostate cancer at our institution between 1988 and 2007 with complete biopsy and pathological data available. Patients with a biopsy Gleason sum of 7 or less were included in our analysis. Cases were categorized as under graded or not under graded by comparing biopsy and radical prostatectomy Gleason sums. Logistic regression was used to determine the predictors of under grading based on clinical variables (race, age at diagnosis, body mass index, prostate weight, diagnostic prostate specific antigen, biopsy positive-to-total core ratio, maximal cancer percent in positive cores and time from diagnosis to surgery). A nomogram was developed to calculate the probability of under grading. Results were validated using bootstrapping. RESULTS: Under grading occurred in 46.6% of our cohort. Significant variables predicting under grading were age at diagnosis, biopsy Gleason sum, diagnostic prostate specific antigen, prostate weight, biopsy positive-to-total core ratio and maximal percent of cancer in cores (p <0.05). Nomogram predictive accuracy was 72.4%. CONCLUSIONS: The risk of Gleason sum under grading can be predicted to a satisfactory level using our nomogram. Predicting under grading would improve patient consulting and identify those who should consider repeat biopsy, ultimately enhancing the accuracy of prostate cancer diagnosis.
Authors: Hiten D Patel; Zhaoyong Feng; Patricia Landis; Bruce J Trock; Jonathan I Epstein; H Ballentine Carter Journal: J Urol Date: 2013-09-20 Impact factor: 7.450
Authors: Iulia Andras; Emanuel Darius Cata; Andreea Serban; Pierre Kadula; Teodora Telecan; Maximilian Buzoianu; Maria Bungardean; Dan Vasile Stanca; Ioan Coman; Nicolae Crisan Journal: Medicina (Kaunas) Date: 2021-05-22 Impact factor: 2.430