Japanese Journal of Radiology最新文献

Purpose: To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC).

Materials and methods: This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10 mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3 T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance.

Results: Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types.

Conclusions: Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Sarcopenia, defined as skeletal muscle loss, is thought to be a hallmark of cancer cachexia. It has an impact on mortality, especially in cancer patients. There are also opposing views regarding the relationship between definitive concurrent chemoradiotherapy (CRT) and sarcopenia in locally advanced lung cancer. Our aim was to investigate the prognostic effect of sarcopenia in our patients with locally advanced stage III non-small cell lung cancer (NSCLC) who received definitive concurrent CRT by using many markers, and to determine the overall survival (OS). The study was designed as a retrospective cohort. 54 patients with stage III NSCLC who received definitive concurrent CRT at the Radiation Oncology Unit of Health Sciences University Izmir Dr Suat Seren Chest Diseases and Surgery Training Hospital, between January 1, 2018 and December 31, 2019, were included in the study.92% of our patients were sarcopenic with international L3-skeletal muscle index (SMI) and Psoas muscle index (PMI) threshold values. The mean OS time was 32.4 months, and the 4-year survival rate was 38.9%. While the new threshold values specific to our patient group were 26.21 for SMI and 2.94 for PMI, SMI and PMI did not indicate OS with these values. Even with the new values, most proposed criteria for sarcopenia did not indicate OS. However, low BMI (≤21.30), low serum albumin (≤4.24 mg/dl) and low visceral fat tissue area (≤37) in univariate analysis, and low visceral fat tissue area (≤37) in multivariate analysis indicated OS. OS was poor in patients with low fat tissue area. In patients with stage III NSCLC who received definitive concurrent CRT, low visceral fat tissue area (≤37) indicated OS, rather than SMI, PMI and other sarcopenia indices.

Purpose: To evaluate the efficacy of MRI findings for differentiating between ovarian metastasis from stomach cancer (OMSC) and colorectal cancer (OMCC).

Methods: Twenty-six patients with histopathologically proven ovarian metastasis (n = 8 with 12 OMSCs and n = 18 with 25 OMCCs) were enrolled in the study. All patients had undergone pelvic MRI before surgery. We retrospectively reviewed MRI findings and compared them between the two pathologies. The black scrunchie sign was defined as a thick (> 5 mm) and lobulated hypointense rim (> 180°) with central hyperintense areas on T2-weighted images.

Results: Predominantly solid lesions (100% vs. 20%, p < 0.01), black scrunchie sign (33% vs. 0%, p < 0.01), and flow void (67% vs. 20%, p < 0.01) were frequently observed in OMSCs than in OMCCs. The signal intensity ratio of solid components on T2-weighted images (3.30 ± 0.70 vs. 2.52 ± 0.77, p < 0.01) and gadolinium-enhanced T1-weighted images (2.21 ± 0.57 vs. 1.43 ± 0.32, p < 0.01) were significantly higher in OMSCs than in OMCCs. Furthermore, hyperintense areas within cystic components on T1-weighted images (71% vs. 18%, p < 0.01) and stained-glass appearance (44% vs. 0%, p < 0.01) were frequently observed in OMCCs than in OMSCs.

Conclusion: The black scrunchie sign was only observed in OMSCs. OMSCs always exhibited predominantly solid lesions and had higher signal intensity of solid components on T2- and gadolinium-enhanced T1-weighted images. OMCCs usually presented as cystic lesions, usually accompanied by hyperintense areas within the cystic components on T1-weighted images.

Purpose: To evaluate the risk factors of non-diagnostic results based on cause of error in liver tumor biopsy.

Materials and methods: This single-institution, retrospective study included 843 patients [445 men, 398 women; median age, 67 years] who underwent a total of 938 liver tumor biopsies between April 2018 and September 2022. An 18-G cutting biopsy needle with a 17-G introducer needle was used. Ultrasound was used as the first choice for image guidance, and computed tomography was alternatively or complementarily used only for tumors with poor ultrasound visibility. Non-diagnostic biopsies were divided into two groups depending on the cause of error, either technical or targeting error. Biopsies in which the biopsy needle did not hit the target tumor were classified as technical error. Biopsies in which insufficient tissue was obtained due to necrosis or degeneration despite the biopsy needle hitting the target tumor were classified as targeting error. This classification was based on pre-procedural enhanced-imaging, intro-procedural imaging, and pathological findings. Statistical analysis was performed using binary logistic regression.

Results: The non-diagnostic rate was 4.6%. Twenty-six and seventeen biopsies were classified as technical and targeting errors, respectively. In the technical error group, tumor size ≤ 17 mm and computed tomography-assisted biopsy due to poor ultrasound visibility were identified as risk factors (p < 0.001 and p = 0.021, respectively), and the tumors with both factors had a significantly high risk of technical error compared to those without both factors (non-diagnostic rate: 17.2 vs 1.1%, p < 0.001). In the targeting error group, tumor size ≥ 42 mm was identified as a risk factor (p = 0.003).

Conclusion: Tumor size ≤ 17 mm and computed tomography-assisted biopsy due to poor ultrasound visibility were risk factors for technical error, and tumor size ≥ 42 mm was a risk factor for targeting error in liver tumor biopsies.

Purpose: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms.

Materials and methods: This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5 mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale.

Results: SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR (P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers (P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures (P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures (P < .05).

Conclusion: SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.

Purpose: The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE).

Methods: This retrospective study included 54 patients with focal hepatic lesions who underwent dynamic contrast-enhanced MRI. Both standard HBP images using CS with AiCE and high-precision HBP images using modified Fast 3D mode wheel and PIQE were obtained. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated using the Wilcoxon signed-rank test. p values of < 0.05 were considered to be statistically significant.

Results: Scores for image noise, conspicuity of liver contours and intrahepatic structures, and overall image quality in high-precision HBP imaging using modified Fast 3D mode wheel and PIQE were significantly higher than those in HBP imaging using CS and AiCE (all p < 0.001). There was no significant difference in the presence of artifact and motion-related blurring. There were no significant differences between the sequences in SNR (p = 0.341) or CNR (p = 0.077). The detection rate of focal hepatic lesions was 71.4-85.3% in CS with AiCE, and 82.2-95.8% in modified Fast 3D mode wheel and PIQE.

Conclusion: A high-precision MR sequence using a modified Fast 3D mode wheel and PIQE is applicable for the HBP of BH 3D T1-weighted imaging.

Purpose: Placenta previa complicated by placenta accrete spectrum (PAS) is a life-threatening obstetrical condition; therefore, preoperative diagnosis of PAS is important to determine adequate management. Although several MRI features that suggest PAS has been reported, the diagnostic importance, as well as optimal use of each feature has not been fully evaluated.

Materials and methods: The occurrence of 11 PAS-related MRI features was investigated in MR images of 145 patients with placenta previa. The correlation between each MRI feature and pathological diagnosis of PAS was evaluated using univariate analysis. A decision tree model was constructed according to a random forest machine learning model of variable selection.

Results: Eight MRI features showed a significant correlation with PAS in univariate analysis. Among these features, placental/uterine bulge and myometrial thinning showed high odds ratios: 138.2 (95% CI: 12.7-1425.6) and 66.0 (95% CI: 18.01-237.1), respectively. A decision tree was constructed based on five selected MRI features: myometrial thinning, placental bulge, serosal hypervascularity, placental ischemic infarction/recess, and intraplacental T2 dark bands. The decision tree predicted the presence of PAS in the randomly assigned validation cohort with significance (p < 0.001). The sensitivity and the specificity of the decision tree for detecting PAS were 90.0% (95%CI: 53.2-98.9) and 95.5% (95%CI: 89.9-96.8), respectively.

Conclusion: Among PAS-related MRI features, placental/uterine bulge and myometrial thinning showed high diagnostic values. In addition, the present decision tree model was shown to be effective in predicting the presence of PAS in cases with placenta previa.