当前位置： SCI文献检索 > Journal of Applied Clinical Medical Physics期刊下所有文献 > Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.

Abstract：

PURPOSE:The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp-MRI), comprised of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. MATERIALS AND METHODS:In this work, 191 radiomic features were extracted from mp-MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp-MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave-one-patient-out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. RESULTS:The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. CONCLUSIONS:Combination of noncontrast mp-MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

journal_name

J Appl Clin Med Phys

journal_title

Journal of applied clinical medical physics

authors

Gholizadeh N,Simpson J,Ramadan S,Denham J,Lau P,Siddique S,Dowling J,Welsh J,Chalup S,Greer PB

doi

10.1002/acm2.12992

subject

Has Abstract

pub_date

2020-10-01 00:00:00

pages

179-191

issue

issn

1526-9914

journal_volume

pub_type

杂志文章

在线工具

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.