Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

BMC Medical Imaging
Satish E ViswanathAnant Madabhushi

Abstract

For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy...Continue Reading

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Citations

Apr 12, 2020·Medical Physics·Robba RaiGary P Liney
Jan 18, 2020·Computer Methods and Programs in Biomedicine·Rogier R WildeboerMassimo Mischi
Mar 29, 2021·European Urology Focus·Rodrigo Suarez-IbarrolaChristian Gratzke
Jun 13, 2020·European Journal of Radiology·Arnaldo StanzioneMassimo Imbriaco
Jun 3, 2021·Sensors·Islam R AbdelmaksoudAyman El-Baz

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Methods Mentioned

BETA
biopsies

Software Mentioned

MATLAB SVM
MATLAB Naïve Bayes
QDA
MATLAB Decision Trees
MATLAB
3FCV
Bay
LIBSVM
MATLAB TreeBagger

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