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

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.