Mr. Michael C. Yim Profile

Michael C. Yim

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Publications (3)

SPIE Journal Paper | 14 June 2019 Open Access

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Prathyush Chirra, Patrick Leo, Michael Yim, B. Nicolas Bloch, Ardeshir R. Rastinehad, Andrei Purysko, Mark Rosen, Anant Madabhushi, Satish Viswanath

JMI, Vol. 6, Issue 02, 024502, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024502

KEYWORDS: Magnetic resonance imaging, Tumors, Prostate, Feature extraction, Scanners, Tissues, Prostate cancer, Computed tomography, Visualization, Magnetism

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Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈0.8). By contrast, a majority of Laws features are highly variable across sites (reproducible in <75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies (<0.6), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.

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Proceedings Article | 8 March 2019 Paper

Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation

Michael Yim, Zhouping Wei, Jacob Antunes, Neil K. Sehgal, Kaustav Bera, Justin Brady, Kenneth Friedman, Joseph Willis, Andrei Purysko, Raj Paspulati, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10951, 109512A (2019) https://doi.org/10.1117/12.2512612

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Lymphatic system, Medical research, Feature extraction, Feature selection, Data analysis, Oncology

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Proceedings Article | 27 February 2018 Presentation + Paper

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

Prathyush Chirra, Patrick Leo, Michael Yim, B. Nicolas Bloch, Ardeshir Rastinehad, Andrei Purysko, Mark Rosen, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10575, 105750B (2018) https://doi.org/10.1117/12.2293992

KEYWORDS: Magnetic resonance imaging, Prostate, Feature extraction, Computed tomography, Scanners, Tissues, Image resolution, Data acquisition, Magnetism, Machine learning

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