Abstract
BackgroundProstate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data.MethodsIn this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.ResultsThe performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.ConclusionsComprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.
Highlights
IntroductionTwo recent major randomized clinical trials [5, 6] have demonstrated that prostate-specific antigen (PSA) screening contains a significant risk of overdiagnosis for prostate cancer where it is estimated that 50 % of screened men are diagnosed with prostate cancer
Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America
Two recent major randomized clinical trials [5, 6] have demonstrated that prostate-specific antigen (PSA) screening contains a significant risk of overdiagnosis for prostate cancer where it is estimated that 50 % of screened men are diagnosed with prostate cancer
Summary
Two recent major randomized clinical trials [5, 6] have demonstrated that PSA screening contains a significant risk of overdiagnosis for prostate cancer where it is estimated that 50 % of screened men are diagnosed with prostate cancer. This leads to painful needle biopsies and subsequent potential overtreatment [5,6,7,8]. The PSA testing remains an important biomarker in diagnosing prostate cancers that are clinically significant. The remaining challenge is how to improve the prostate cancer diagnosis to reduce the overdiagnosis of clinically insignificant cancers
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