Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis

Improved Value of Multiplexed Sensitivity Encoding DWI with Reversed Polarity Gradients in Diagnosing Prostate Cancer: A Comparison Study with Single-Shot DWI and MUSE DWI

RATIONALE AND OBJECTIVES

This study aimed to investigate the value of multiplexed sensitivity encoding with reversed polarity gradients in improving the quality of diffusion-weighted imaging (DWI) images of the prostate and the diagnostic efficacy of prostate cancer.

MATERIALS AND METHODS

Seventy-three patients with prostate disease underwent multiplexed sensitivity encoding with reversed polarity gradients (RPG-MUSE), multiplexed sensitivity encoding (MUSE), and single-shot echo-planar imaging (ssEPI) DWI. Three radiologists performed a qualitative image analysis of the three DWI sequences. Qualitative image analysis included artifact minimization, anatomical detail, and sharpness of prostate edges. Two radiologists measured the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion rate, and the apparent diffusion coefficient (ADC) values of the prostate disease tissue. Two radiologists jointly performed Prostate Imaging Reporting and Data System scoring of prostate lesions and compared the diagnostic efficacy of the three DWI sequences for prostate cancer.

RESULTS

There was good agreement among radiologists in the evaluation and measurement of the three DWI sequence images (intraclass correlation coefficient >0.75, P < 0.05). The RPG-MUSE DWI images were rated higher than those of MUSE and ssEPI in terms of artifact minimization, anatomical details, and sharpness of prostate edges (P < 0.05). The SNR and CNR of the RPG-MUSE DWI images were higher than those of MUSE and ssEPI (P < 0.05), and the geometric distortion rate was lower than that of the other two sequences (P < 0.05). There were no statistical differences in ADC values between the three DWI sequences (P > 0.05). The diagnostic efficacy of RPG-MUSE and MUSE DWI was higher than that of ssEPI (P < 0.017).

CONCLUSION

RPG-MUSE can reduce the artifacts and geometric distortion in DWI images of the prostate, improve the SNR and CNR of the images, improve the clarity of anatomical details and boundaries without affecting the measurement of ADC values, has the potential to improve the diagnostic efficacy of prostate lesions, and facilitates the clear display and accurate assessment of prostate lesions.

Contrast-Enhanced Ultrasonography (CEUS) in Imaging of the Reproductive System in Dogs: A Literature Review

The use of contrast-enhanced ultrasound (CEUS) has been widely reported for reproductive imaging in humans and animals. This review aims to analyze the utility of CEUS in characterizing canine reproductive physiology and pathologies. In September 2022, a search for articles about CEUS in canine testicles, prostate, uterus, placenta, and mammary glands was conducted on PubMed and Scopus from 1990 to 2022, showing 36 total results. CEUS differentiated testicular abnormalities and neoplastic lesions, but it could not characterize tumors. In prostatic diseases, CEUS in dogs was widely studied in animal models for prostatic cancer treatment. In veterinary medicine, this diagnostic tool could distinguish prostatic adenocarcinomas. In ovaries, CEUS differentiated the follicular phases. In CEH-pyometra syndrome, it showed a different enhancement between endometrium and cysts, and highlighted angiogenesis. CEUS was shown to be safe in pregnant dogs and was able to assess normal and abnormal fetal-maternal blood flow and placental dysfunction. In normal mammary glands, CEUS showed vascularization only in diestrus, with differences between mammary glands. CEUS was not specific for neoplastic versus non-neoplastic masses and for benign tumors, except for complex carcinomas and neoplastic vascularization. Works on CEUS showed its usefulness in a wide spectrum of pathologies of this non-invasive, reliable diagnostic procedure.

Multi-scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images

PURPOSE

The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI.

METHODS

First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM.

RESULTS

For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm.

CONCLUSIONS

The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.