Optimization of breathing instructions and timing of late arterial phase acquisition on gadobutrol-enhanced MRI of the liver

Style Transfer-assisted Deep Learning Method for Kidney Segmentation at Multiphase MRI

Purpose

To develop and validate a semisupervised style transfer-assisted deep learning method for automated segmentation of the kidneys using multiphase contrast-enhanced (MCE) MRI acquisitions.

Materials and Methods

This retrospective, Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included 125 patients (mean age, 57.3 years; 67 male, 58 female) with renal masses. Cohort 1 consisted of 102 coronal T2-weighted MRI acquisitions and 27 MCE MRI acquisitions during the corticomedullary phase. Cohort 2 comprised 92 MCE MRI acquisitions (23 acquisitions during four phases each, including precontrast, corticomedullary, early nephrographic, and nephrographic phases). The kidneys were manually segmented on T2-weighted images. A cycle-consistent generative adversarial network (CycleGAN) was trained to generate anatomically coregistered synthetic corticomedullary style images using T2-weighted images as input. Synthetic images for precontrast, early nephrographic, and nephrographic phases were then generated using the synthetic corticomedullary images as input. Mask region-based convolutional neural networks were trained on the four synthetic phase series for kidney segmentation using T2-weighted masks. Segmentation performance was evaluated in a different cohort of 20 originally acquired MCE MRI examinations by using Dice and Jaccard scores.

Results

The CycleGAN network successfully generated anatomically coregistered synthetic MCE MRI-like datasets from T2-weighted acquisitions. The proposed deep learning approach for kidney segmentation achieved high mean Dice scores in all four phases of the original MCE MRI acquisitions (0.91 for precontrast, 0.92 for corticomedullary, 0.91 for early nephrographic, and 0.93 for nephrographic).

Conclusion

The proposed deep learning approach achieved high performance in kidney segmentation on different MCE MRI acquisitions.Keywords: Kidney Segmentation, Generative Adversarial Network, CycleGAN, Convolutional Neural Network, Transfer Learning Supplemental material is available for this article. Published under a CC BY 4.0 license.

How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score

The widespread use of cross-sectional imaging has led to a continuous increase in the number of incidentally detected indeterminate renal masses. Frequently, these clinical scenarios involve an older patient with comorbidities and a small renal mass (≤4 cm). Despite aggressive treatment in early stages of the disease, a clear positive effect in reducing kidney cancer-specific mortality is lacking, indicating that many renal cancers exhibit an indolent oncologic behavior. Furthermore, in general, one in five small renal masses is histologically benign and may not benefit from aggressive treatment. Although active surveillance is increasingly recognized as a management option for some patients, the absence of reliable clinical and imaging predictive biologic markers of aggressiveness can contribute to patient anxiety and limit its use in clinical practice. A standardized approach to the image interpretation of solid renal masses has not been broadly implemented. The clear cell likelihood score (ccLS) derived from multiparametric MRI is useful in noninvasively identifying the clear cell subtype, the most common and aggressive form of kidney cancer. Herein, a review of the ccLS is presented, including a step-by-step guide for image interpretation and additional guidance for its implementation in clinical practice.

Alternative approach of hepatocellular carcinoma surveillance: abbreviated MRI

This review focuses on emerging abbreviated magnetic resonance imaging (AMRI) surveillance of patients with chronic liver disease for hepatocellular carcinoma (HCC). This surveillance strategy has been proposed as a high-sensitivity alternative to ultrasound for identification of patients with early-stage HCC, particularly in patients with cirrhosis or obesity, in whom sonographic visualization of small tumors may be compromised. Three general AMRI approaches have been developed and studied in the literature - non-contrast AMRI, dynamic contrast-enhanced AMRI, and hepatobiliary phase contrast-enhanced AMRI - each comprising a small number of selected sequences specifically tailored for HCC detection. The rationale, general technique, advantages and disadvantages, and diagnostic performance of each AMRI approach is explained. Additionally, current gaps in knowledge and future directions are discussed. Based on emerging evidence, we cautiously recommend the use of AMRI for HCC surveillance in situations where ultrasound is compromised.

Prevalence and clinical significance of discordant LI-RADS® observations on multiphase contrast-enhanced MRI in patients with cirrhosis

PURPOSE

To determine the prevalence and clinical significance of discordant LI-RADS^® (Liver Imaging Reporting and Data System) liver observations on multiphase contrast-enhanced (MCE) magnetic resonance imaging (MRI) in patients with cirrhosis.

METHODS

This cross-sectional study included 93 cirrhosis patients who underwent 1.5 or 3 T MCE MRI for evaluation of hepatocellular carcinoma (HCC). Two abdominal radiologists independently reviewed T1-, T2-, diffusion-weighted unenhanced images as well as MCE T1-weighted fat-suppressed images and reported liver observations using LI-RADS^®. Concordance were recorded for detection (co-detected by both radiologists or not), size category (< 10; 10-19; ≥ 20 mm), and LI-RADS^® category assignment as reportable (LR-3/4/5/M) and actionable (LR-4/5/M). The overall concordance (i.e., concordant in detection, size, and LR-category) was calculated with 95% confidence interval [CI], and separately for detection, size, and LR-category. Clinical significance of discordance was assessed as impact on follow-up imaging, referral for biopsy, liver transplant eligibility, or treatment modality.

RESULTS

Reportable and actionable observations were overall concordant between two radiologists only in 32.3% [24.6, 41.0] and 40.1% [29.5, 51.5] of cases, respectively. Poor overall concordance was related to detection concordance of 52.0% [44.3, 59.5] and 62.5% [52.3, 71.8], as well as LR-category concordance of 73.7% [61.6, 83.1] and 70.9% [57.3, 81.6], for reportable and actionable observations, respectively. Discordant LI-RADS^® observations would have impacted clinical management in 30 subjects (43.5%), most (66.7%) of whom were due to discordant detection.

CONCLUSION

Discordant MRI LI-RADS^® observations are common in patients with cirrhosis and may have potential implications for patient management.