DOI: 10.5061/dryad.026cj63
title: Data from: Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

description: Background: 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 larger 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.
 Methods: 12 different supervised classifier schemes: Quadratic
 Discriminant Analysis (QDA), Support Vector Machines (SVMs), naive 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 86 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.
 Surrogate ground truth for disease extent on MRI was established by expert
 annotation of pre-operative MRI through spatial correlation with
 corresponding ex vivo whole-mount histology sections. Classifier accuracy
 in detecting PCa extent on MRI on a per-voxel basis was evaluated via area
 under the ROC curve. Results: The boosted DT classifier yielded the
 highest cross-validated AUC (= 0.744) for detecting PCa in the discovery
 cohort. However, in independent validation, the boosted QDA classifier was
 identified as the most accurate and robust for voxel-wise detection of PCa
 extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most
 accurate and robust classifier was the single QDA classifier, which also
 enjoyed the advantage of significantly lower computation times compared to
 any of the other methods. Conclusions: Our results therefore suggest that
 simpler classifiers (such as QDA and its ensemble variants) may be more
 robust, accurate, and efficient for prostate cancer CAD problems,
 especially in the context of multi-site validation.
description: Radiomic feature data
 from Site 1Radiomic features
 extracted from S1 (used for training) on a pixel-wise basis from prostate
 T2w MRIs, together with associated pixel-wise labels and feature
 names.D1.zipRadiomic feature data from Site 2Radiomic features extracted from S2 (used for multi-site validation) on a pixel-wise basis from prostate T2w MRIs, together with associated pixel-wise labels and feature names.D2.zipRadiomic feature data from Site 3Radiomic features extracted from S3 (used for multi-site validation) on a pixel-wise basis from prostate T2w MRIs, together with associated pixel-wise labels and feature names.D3.zip