Classification of suspicious lesions on prostate multiparametric MRI using machine learning

Deukwoo Kwon; Isildinha M. Reis; Adrian L. Breto; Yohann Tschudi; Nicole Gautney; Olmo Zavala-Romero; Christopher Lopez; John C. Ford; Sanoj Punnen; Alan Pollack; Radka Stoyanova

doi:10.1117/1.JMI.5.3.034502

6 September 2018 Classification of suspicious lesions on prostate multiparametric MRI using machine learning

Deukwoo Kwon, Isildinha M. Reis, Adrian L. Breto, Yohann Tschudi, Nicole Gautney, Olmo Zavala-Romero, Christopher Lopez, John C. Ford, Sanoj Punnen, Alan Pollack, Radka Stoyanova

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 3, 034502 (September 2018). https://doi.org/10.1117/1.JMI.5.3.034502

Abstract

We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a “training dataset” (330 suspected lesions from 204 patients) and a “test dataset” (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusion-weighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.

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Deukwoo Kwon, Isildinha M. Reis, Adrian L. Breto, Yohann Tschudi, Nicole Gautney, Olmo Zavala-Romero, Christopher Lopez, John C. Ford, Sanoj Punnen, Alan Pollack, and Radka Stoyanova "Classification of suspicious lesions on prostate multiparametric MRI using machine learning," Journal of Medical Imaging 5(3), 034502 (6 September 2018). https://doi.org/10.1117/1.JMI.5.3.034502

Received: 20 February 2018; Accepted: 6 August 2018; Published: 6 September 2018

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