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<a href="http://doi.org/10.1186/s12880-019-0308-6" target="_blank" rel="noreferrer noopener">http://doi.org/10.1186/s12880-019-0308-6</a>
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22-22
Issue
1
Volume
19
NEOMED College
NEOMED College of Medicine
NEOMED Department
NEOMED Student Publications
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Title
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Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.
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BMC medical imaging
Date
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2019
2019-02
Subject
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*Prostate cancer; *Classifiers; *Comparison; *MRI; *Radiomics
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Viswanath Satish E; Chirra Prathyush V; Yim Michael C; Rofsky Neil M; Purysko Andrei S; Rosen Mark A; Bloch B Nicolas; Madabhushi Anant
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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 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. METHODS: Twelve 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 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. 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.
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<a href="http://doi.org/10.1186/s12880-019-0308-6" target="_blank" rel="noreferrer noopener">10.1186/s12880-019-0308-6</a>
*Classifiers
*Comparison
*MRI
*Prostate cancer
*Radiomics
2019
Bloch B Nicolas
BMC medical imaging
Chirra Prathyush V
Madabhushi Anant
NEOMED College of Medicine Student
NEOMED Student Publications
Purysko Andrei S
Rofsky Neil M
Rosen Mark A
Viswanath Satish E
Yim Michael C