Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements

Short repetition time diffusion-weighted imaging improves visualization of prostate cancer

PURPOSE

This study aimed to assess whether short repetition time (TR) diffusion-weighted imaging (DWI) could improve diffusion contrast in patients with prostate cancer (PCa) compared with long TR (conventional) reference standard DWI.

MATERIALS AND METHODS

Our Institutional Review Board approved this retrospective study and waived the need for informed consent. Twenty-five patients with suspected PCa underwent multiparametric magnetic resonance imaging (mp-MRI) using a 3.0-T system. DWI was performed with TR of 1850 ms (short) and 6000 ms (long) with b-values of 0, 1000, and 2000s/mm2. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), visual score, apparent diffusion coefficient (ADC), and diagnostic performance were compared between short and long TR DWI for both b-values. The statistical tests included paired t-test for SNR and CNR; Wilcoxon signed-rank test for VA; Pearson's correlation and Bland-Altman plot analysis for ADC; and McNemar test and receiver operating characteristic analysis and Delong test for diagnostic performance.

RESULTS

Regarding b1000, CNR and visual score were significantly higher in short TR compared with long TR (P = .003 and P = .002, respectively), without significant difference in SNR (P = .21). Considering b2000, there was no significant difference in visual score between short and long TR (P = .07). However, SNR and CNR in long TR were higher (P = .01 and P = .04, respectively). ADC showed significant correlations, without apparent bias for ADC between short and long TR for both b-values. For diagnostic performance of DWI between short and long TR for both b-values, one out of five readers noted a significant difference, with the short TR for both b-values demonstrating superior performance.

CONCLUSIONS

Our data showed that the short TR DWI1000 may provide better image quality than did the long TR DWI1000 and may improve visualization and diagnostic performance of PCa for readers.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.

Analysis of Apparent Diffusion Coefficient Value and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters of Prostate Cancer Patients after Diagnosis and Treatment with Magnetic Resonance Imaging

This research was aimed at exploring the changes in the apparent diffusion coefficient (ADC) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters of prostate cancer (PCa) patients. Sixty PCa patients from the hospital were recruited as the research object, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans were performed to determine the shape, scope, and enhancement characteristics of prostate lesions and their relationship with surrounding tissues. The quantitative parameters of ADC and DCE-MRI were measured. There were 4 patients (6.67%) with a Gleason score of 6 and 15 patients (25%) with a 4 + 3 score. The ADC with Gleason = 6 is 0.81 ± 0.08 × 10⁻³ s/mm², the ADC with Gleason = 3 + 4 is 0.74 ± 0.07 × 10⁻³ s/mm², the ADC with Gleason = 4 + 3 is 0.73 ± 0.05 × 10⁻³ s/mm², the ADC with Gleason = 9 is 0.65 ± 0.06 × 10⁻³ s/mm², and the ADC with Gleason = 10 is 0.59 ± 0.07 × 10⁻³ s/mm². As the Gleason score increased, the ADC decreased and the permeation parameter transfer constant increased. When the ADC was combined with the permeability parameter transfer constant, the AUC of Gleason = 6 points and Gleason = 7 points was greatly different (P < 0.05). The 95% CI of the ADC combined permeability parameter transport constant when Gleason = 6 points and Gleason = 7 points was 0.898-0.934, the sensitivity was 75.4%, and the specificity was 86.2%. The ADC value was negatively correlated with Gleason score. The ADC value combined with VTC value has good diagnostic performance in evaluating the invasion of PCa, which is very important for making treatment plan and evaluating prognosis.