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Lay N, Tsehay Y, Greer MD, et al. Detection of prostate cancer in multiparametric MRI using random forest with instance weighting. J Med Imaging (Bellingham). 2017;4(2):024506doi: 10.1117/1.JMI.4.2.024506.
Lay, N., Tsehay, Y., Greer, M. D., Turkbey, B., Kwak, J. T., Choyke, P. L., Pinto, P., Wood, B. J., & Summers, R. M. (2017). Detection of prostate cancer in multiparametric MRI using random forest with instance weighting. Journal of medical imaging (Bellingham, Wash.), 4(2), 024506. https://doi.org/10.1117/1.JMI.4.2.024506
Lay, Nathan, et al. "Detection of prostate cancer in multiparametric MRI using random forest with instance weighting." Journal of medical imaging (Bellingham, Wash.) vol. 4,2 (2017): 024506. doi: https://doi.org/10.1117/1.JMI.4.2.024506
Lay N, Tsehay Y, Greer MD, Turkbey B, Kwak JT, Choyke PL, Pinto P, Wood BJ, Summers RM. Detection of prostate cancer in multiparametric MRI using random forest with instance weighting. J Med Imaging (Bellingham). 2017 Apr;4(2):024506. doi: 10.1117/1.JMI.4.2.024506. Epub 2017 Jun 12. PMID: 28630883; PMCID: PMC5467765.
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J Med Imaging (Bellingham). 2017 Apr;4(2):024506. doi: 10.1117/1.JMI.4.2.024506. Epub 2017 Jun 12.

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting.

Journal of medical imaging (Bellingham, Wash.)

Nathan Lay, Yohannes Tsehay, Matthew D Greer, Baris Turkbey, Jin Tae Kwak, Peter L Choyke, Peter Pinto, Bradford J Wood, Ronald M Summers

Affiliations

  1. National Institutes of Health, Clinical Center, Imaging Biomarkers and Computer Aided Diagnosis Laboratory, Bethesda, Maryland, United States.
  2. National Institutes of Health, National Cancer Institute, Urologic Oncology Branch and Molecular Imaging Program, Bethesda, Maryland, United States.
  3. National Institutes of Health, Clinical Center, Center for Interventional Oncology, Bethesda, Maryland, United States.

PMID: 28630883 PMCID: PMC5467765 DOI: 10.1117/1.JMI.4.2.024506

Abstract

A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.

Keywords: computer-aided diagnosis; multiparametric MRI; prostate

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