Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

Abstract

Background: Prostate 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.

Methods: In 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.

Results: The 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.

Conclusions: Comprehensive 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.

Fig. 1

Block diagram of the proposed texture feature models

Fig. 2

Performance results for different modalities (T2w, ADC, CHB-DWI, CDI, and 4 DWI images at different b values) across all features

Fig. 3

AUC based on using sensitivity and specificity as performance evaluation criteria

Fig. 4

ROC for different texture feature models

Fig. 5

a T2w does not clearly show a tumour although there is mild signal alteration in the left peripheral zone (arrow). b ADC does not clearly show a tumour (arrow). c CHB-DWI of 2000 s/m m ²shows no tumour (arrow). d CDI clearly shows a bright nodule (arrow) corresponding to tumour

Fig. 6

Corresponding axial hematoxylin and eosin stained tissue showing a Gleason 7 (4+3) tumor circled in red corresponding to the lesion identified best on the CDI images in Fig. 5-d