Noriyuki Tomiyama

Purpose: To investigate the association between AI-derived probability of interstitial lung abnormalities (ILA), including interstitial lung disease (ILD), and prognosis in an esophageal cancer cohort re-evaluated according to the 2025 American Thoracic Society (ATS) Clinical Statement. Materials and methods: This retrospective study included patients with esophageal cancer who underwent pretreatment chest CT between January 2011 and December 2015. Chest CT images were visually assessed and classified as non-ILA, indeterminate for ILA, ILA, or ILD according to the 2025 ATS Clinical Statement. Twelve previously developed artificial intelligence (AI) models generated patient-level ILA probability scores ranging from 0 to 1. Associations between AI-derived ILA/ILD probability and five-year overall survival (OS) were evaluated using multivariable Cox proportional hazards models adjusted for age, sex, body mass index, smoking history, and histology, stratified by clinical stage. Associations between visual ILA/ILD classification and OS were also assessed. Results: A total of 452 patients (median age, 68 years [interquartile range: 61–73 years]; 61 women) were included. Among them, 251 (56%) were classified as non-ILA, 118 (26%) as indeterminate for ILA, 75 (17%) as ILA, and 8 (2%) as ILD. All 12 AI models showed significant associations with shorter OS (all p < 0.05). The best-fit model yielded a hazard ratio (HR) for mortality of 7.18 (95% confidence interval [CI]: 2.70–19.1; p < 0.001). Visual ILA and ILD were associated with mortality (ILA: HR = 1.60, 95% CI: 1.02–2.51, p = 0.039; ILD: HR = 3.01, 95% CI: 1.07–8.44, p = 0.037). AI-derived probability scores differed significantly among visual categories (all p < 0.01). Conclusion: AI-derived ILA/ILD probability was associated with five-year OS in patients with esophageal cancer and may provide a reproducible quantitative marker for opportunistic risk stratification using chest CT.

Purpose To investigate the prevalence of calyceal diverticulum (CD) among patients undergoing Gd-EOB-DTPA-enhanced liver MR imaging, and to report their CT features including size changes on follow-up examinations. Methods We conducted a retrospective review of 1,713 consecutive adult patients who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced liver MR imaging for various indications. Hepatobiliary phase images were screened for the presence of CD by observing the renal parenchymal accumulation of Gd-EOB-DTPA. Two radiologists evaluated the CT features of the identified CDs and measured the changes in size of the CDs and associated calcifications between the initial and follow-up CT examinations. Results Seven patients had CD, resulting in a prevalence of 0.4%. The shape was round or oval in six CDs and lobulated in one. All of the CDs had a well-defined margin, and none had thickened walls or septa. All of the CDs had homogeneous internal density, with low density in six CDs and intermediate in one. Five CDs were in proximity to the renal sinus. Five calcifications were observed in four CDs: four of which were located on the posterior wall and one on the anterior wall. Follow-up examinations revealed a 28% decrease in the maximum diameter of one CD and a 14% decrease in another. The maximum diameter of the calcifications increased in all cases, with a mean increase of 24%. Conclusion CDs were found in 0.4% of patients who underwent Gd-EOB-DTPA-enhanced liver MR imaging. Typical CT features of CDs were round- or oval-shaped, well-defined, homogeneous, low-density masses that abutted the renal sinus. When calcification was present, it was usually located on the posterior wall and was round or oval in shape. The size of CD may occasionally decrease over time, while associated calcifications tend to increase in size over time.

Intermuscular adipose tissue (IMAT) refers to adipose tissue located between muscle groups or beneath the muscle fascia, and its accumulation is closely linked to impaired muscle quality, reduced contractile function, and adverse metabolic profiles, establishing it as a core component of myosteatosis. Unlike subcutaneous and visceral adipose tissue, which reside outside the muscle architecture, IMAT expands within the muscle environment, where local metabolic and mechanical interactions may affect muscle integrity and performance. Elevated IMAT has been associated with diminished strength, impaired mobility, frailty, insulin resistance, and adverse cardiometabolic profiles, underscoring its clinical relevance beyond a passive fat depot. However, IMAT evidence remains dispersed across imaging techniques, anatomical regions, and disease‐specific literatures, limiting a unified understanding of its clinical utility. This review synthesizes current evidence on IMAT biology, imaging‐based quantification, disease‐specific distribution and clinical implications, with emphasis on its value as a risk‐stratification biomarker. Recent advances in medical imaging, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound and positron emission tomography (PET), have enabled noninvasive and reproducible IMAT quantification. These modalities provide complementary measures of muscle fat infiltration, including CT‐derived area, volume and attenuation; MRI‐based segmentation and proton density fat fraction; ultrasound echo intensity; and PET‐derived metabolic activity. Deep learning–based segmentation methods have improved large‐scale IMAT quantification and reproducibility across anatomical regions. Mechanistically, IMAT is associated with chronic low‐grade inflammation and impaired glucose homeostasis, promoting progressive fat infiltration and muscle degeneration. Human studies indicate that IMAT exhibits an inflammatory secretory profile linked to insulin resistance and metabolic dysfunction. Quantitative imaging shows anatomically heterogeneous IMAT distribution, with frequent involvement of the lower extremities, paraspinal muscles and trunk. Across disease settings, IMAT appears to provide complementary information beyond muscle mass alone by capturing muscle quality and systemic vulnerability. Evidence is strongest in oncology, where elevated IMAT or IMAT‐based ratios have repeatedly been associated with poorer survival, recurrence, treatment toxicity, and reduced treatment tolerance. In metabolic and musculoskeletal disorders, IMAT is consistently related to insulin resistance, reduced strength, impaired mobility, and frailty, whereas cardiovascular evidence suggests prognostic relevance but remains less standardized and more heterogeneous. Clinically, IMAT imaging may help identify patients whose preserved muscle size or body mass index masks poor muscle quality, thereby refining prognostic assessment, perioperative risk evaluation, and selection for nutritional, exercise, metabolic, or treatment strategies. Future efforts should focus on standardized acquisition, automated segmentation, harmonized thresholds, and prospective validation to support integration of IMAT metrics into clinical decision‐making.

Background/Objectives: Accurate preoperative assessment of peritoneal dissemination is essential in ovarian cancer because it influences surgical strategy and the achievement of complete gross resection. However, [¹⁸F]FDG-PET may be limited in detecting lesions with low glycolytic activity and in differentiating malignancy from inflammatory changes. This case series evaluated the clinical relevance of [¹⁸F]FAPI-74 PET/CT for preoperative assessment of peritoneal dissemination in ovarian cancer. Methods: Four patients underwent [¹⁸F]FAPI-74 PET/CT as part of preoperative evaluation, with comparison to [¹⁸F]FDG-PET/CT when available. Imaging findings were correlated with intraoperative observations and histopathological results, including immunohistochemical assessment of fibroblast activation protein and α-smooth muscle actin. Results: FAPI-PET detected peritoneal dissemination not identified by FDG-PET in several cases, including occult metastasis confirmed histologically and additional lesions after neoadjuvant chemotherapy. FAPI-avid lesions showed stromal activation on immunohistochemistry, supporting the biological basis of FAPI uptake. In one case, additional FAPI uptake may have been partly influenced by inflammatory changes associated with bloody ascites. Conclusions: FAPI-PET may provide complementary information by visualizing stromal components of ovarian cancer and may support preoperative mapping of peritoneal dissemination, although interpretation should consider inflammatory conditions.

Ota T, et al. Korean J Radiol. 2026 ;27:e71. https://doi.org/10.3348/kjr.2026.0242

MRI has become an increasingly important modality for evaluating the upper urinary tract. Although CT urography remains the standard imaging modality for assessing hematuria, MRI offers distinct advantages, including superior soft-tissue contrast, functional imaging capability, and a radiation-free evaluation, making it particularly valuable in certain clinical scenarios. This review provides a comprehensive overview of MRI of the upper urinary tract, with a focus on upper tract urothelial carcinoma (UTUC). Optimized MRI techniques, including T2-weighted imaging, MR urography, diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging, are discussed with respect to their complementary roles in tumor detection, characterization, and local staging. MRI findings of UTUC are summarized, emphasizing the role of DWI in improving tumor detection and assessing renal parenchymal invasion. Beyond UTUC, the review addresses MRI features of non-UTUC neoplastic lesions, infectious and inflammatory diseases, and congenital anomalies of the upper urinary tract, underscoring the value of MRI in differential diagnosis. We discuss the potential role of MRI-derived imaging biomarkers, focusing on apparent diffusion coefficient measurements as noninvasive indicators of tumor aggressiveness and invasiveness. We also introduce emerging radiomics approaches as promising, albeit investigational, tools. As technical innovations continue and clinical evidence grows, the role of MRI in the personalized evaluation and management of upper urinary tract diseases is expected to expand.

Approximately 5% and 1% of global greenhouse gas emissions originate from the healthcare sector and medical imaging respectively. These are not insignificant. Despite this, the medical field has been slow to adopt sustainable practices. Green radiology is a sustainable, innovative, and responsible approach in radiological practice that focuses on minimising the negative environmental impact of our technologies and procedures. The Asian Oceanian Society of Radiology (AOSR) conducted a survey across Asia-Oceania to better understand current operations and identify opportunities for meaningful progress toward more sustainable radiological practices. From the 123 institutions that responded, it was found that collectively the carbon footprint of Asia-Oceania’s radiological services are high in some of the countries/regions because of the high density of high energy consumption scanners (such as computed tomography and magnetic resonance imaging scanners). Our survey showed that less than half of the 15 societies that responded had specific academic or research activities related to sustainable radiology and less than a third reported that sustainability had been incorporated in the training curriculum. The AOSR is committed to facilitating the sharing of best practices in green radiology and recommends that Asia-Oceania use an “As Low As Reasonably Achievable” principle, traditionally used in radiation safety and extend this in the approach to the issues of environmental impact of radiological services. The AOSR as a cross-regional professional society, has the mandate to partner with our counterpart societies and industry stakeholders, to emphasise the importance of sustainability as a critical agenda, through our various activities. We foresee that influencing radiology leaders in the region to urgently formulate strategies, implement policies towards adopting eco-friendly approaches including reducing inappropriate imaging and encouraging academic efforts will help to reduce the environmental impact in each country/region. A checklist has been included to help kickstart the process.

Background: To investigate the performance of radiologists in characterizing and diagnosing hepatic lesions with and without the assistance of deep learning-based artificial intelligence (AI). Methods: This retrospective study included 83 nodules/masses from 69 patients who underwent dynamic contrast-enhanced CT of the liver. Image assessments were conducted by 20 radiologists. grouped according to their level of experience (10 senior and 10 junior). Each radiologist determined the probability of eight characteristics based on enhancement patterns and the diagnosis with and without AI attached to the SYNAPSE SAI viewer (FUJIFILM Corporation, Minato-ku, Japan). The reference standard for comparison was established as follows: final diagnoses were based on pathology for 39 lesions and expert imaging consensus for the remainder, while image characteristics for all lesions were determined by expert imaging consensus. Areas under the receiver operating characteristic curves (AUCs) were analyzed using the multireader multicase method. Results: Using AI significantly improved the overall AUCs for both the characterization and the diagnosis of liver lesions. Improvement was suggested for specific items, including the characterization of enhancement, nonperipheral washout, and delayed enhancement, and the diagnosis of hepatocellular carcinoma. The utilization of AI system also suggested potential improvements in the AUCs for image characterization in both the senior and junior groups. Conclusions: Using AI improved the radiologists’ performance in characterizing and diagnosing hepatic lesions. In terms of their capacity to assess imaging characteristics, improvements were observed regardless of their level of experience.

Purpose This study aimed to develop and validate a prognostic model for hepatocellular carcinoma (HCC) patients undergoing liver resection, using the functional liver imaging score (FLIS) derived from hepatobiliary-specific contrast-enhanced magnetic resonance imaging (MRI). Material and methods A total of 694 pathologically confirmed HCC patients who underwent hepatobiliary-specific MRI with either gadoxetic acid or gadobenate dimeglumine and subsequent liver resection were included. FLIS was calculated by assigning 0–2 points to three hepatobiliary-phase MRI features: hepatic enhancement, biliary excretion, and portal vein signal intensity. Multivariable Cox regression identified AFP level, tumor size, and extent of resection as independent predictors of overall survival (OS). Results FLIS ≤ 2, alpha-fetoprotein (AFP) > 400 ng/mL, tumor size > 5 cm, and major resection were identified as independent predictors of worse OS. A predictive model combining these factors demonstrated excellent prognostic performance, with Harrell’s concordance indices of 0.91 in the training cohort and 0.96 internal validation cohort, and 0.94 in external validation cohort. The FLIS-based model significantly outperformed FLIS alone and conventional clinical models (p < 0.05). Kaplan–Meier survival analysis showed that low-risk patients had significantly better OS and recurrence-free survival (RFS) compared to high-risk patients across all cohorts (p < 0.05). Conclusion FLIS is a simple, non-invasive imaging biomarker for evaluating liver function and predicting outcomes in HCC patients. When integrated with key clinical variables, the FLIS-based model demonstrates excellent discrimination and calibration for OS and RFS, providing accurate postoperative prognostic stratification and showing great potential for guiding surveillance and improving long-term survival outcomes in future clinical applications.