Identification of patients with low-risk for aneuploidy: comparative discriminatory models using linear and machine-learning classifiers in prostate cancer.
BACKGROUND
Prostate needle biopsy (PNB) ploidy status has proven utility to predict adverse outcomes after prostatectomy. We sought to develop models to predict ploidy status using clinicopathologic variables.


METHODS
We identified a cohort of 169 patients with a diagnosis of prostatic adenocarcinoma on PNB, and estimated ploidy status (determined using Feulgen stained biopsy tissue) using four predictors, including age, prebiopsy PSA, highest Gleason score (GS), and the percentage of involvement by carcinoma at the biopsy site with the highest GS (PCARBX). Logistic regression (LR), Neural Network (NN), and CART classifiers were constructed.


RESULTS
Univariate analyses revealed all four predictors to be significantly associated with ploidy status. On multivariable analyses, LR identified a 2-parameter model, including GS and PCARBX that had a significant ability to predict ploidy status with a 74% and 75% correct classification rate (CCR), respectively. Using the same variables, CART and NN yielded similar CCRs of 70.4%. Within GS = 6 cohort, the CART model classified over 90% of biopsies as diploid when patients had a PCARBX < 55% and a log(PSA) < 1.7.


CONCLUSIONS
Our study demonstrates that models using GS and PCARBX are able to predict PNB ploidy status with acceptable accuracy. While machine learning classifier-derived models yield similar accuracy as LR-derived models, the latter methodology has the distinct advantage of being applicable in future datasets to estimate case-specific predictions. This information may be useful in identifying potentially aneuploid patients, who can then be targeted for more aggressive therapy.
Venkataraman
Girish
G
Department of Pathology, Loyola University Medical Center, Maywood, Illinois 60153, USA. gvenkat@lumc.edu
Heinze
Georg
G
Holmes
Earle W
EW
Ananthanarayanan
Vijayalakshmi
V
Bostwick
David G
DG
Paner
Gladell P
GP
Bradford-De La garza
Carrie M
CM
Brown
Henry G
HG
Flanigan
Robert C
RC
Wojcik
Eva M
EM
D000230:Adenocarcinoma / Q000097:blood / Q000145:classification / Q000175:diagnosis / Q000235:genetics*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000782:Aneuploidy; D001707:Biopsy, Needle; D015331:Cohort Studies; D004273:DNA, Neoplasm / Q000235:genetics; D016002:Discriminant Analysis; D006801:Humans; D019044:Image Cytometry; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000145:classification / Q000175:diagnosis / Q000235:genetics*; D018570:Risk Assessment
D004273:DNA, Neoplasm; D017430:Prostate-Specific Antigen