The detection of pancreatic cystic lesions (PCLs) has increased markedly with the widespread use of cross-sectional imaging. Although the true proportion of intraductal papillary mucinous neoplasms (IPMNs) among PCLs remains uncertain, IPMN is considered to account for most PCLs and is a representative precursor lesion of pancreatic ductal adenocarcinoma (PDAC). However, not all PCLs should be assumed to represent IPMN, because this assumption may lead to unnecessary surveillance or overtreatment. Therefore, radiologists should first determine whether a PCL truly represents IPMN, particularly by differentiating it from lesions without malignant potential, such as serous cystic neoplasm. Once IPMN is diagnosed, appropriate risk stratification is required to identify IPMN-derived cancer, defined in this review as IPMN with high-grade dysplasia or associated invasive carcinoma. High-risk stigmata and worrisome features proposed in current guidelines are central to this assessment. However, the risk associated with each factor is not uniform; therefore, risk assessment and management decisions should consider both the number of risk factors present and the relative weight of each factor. Concomitant PDAC may also arise separately from the PCL, possibly reflecting a field defect of the entire pancreas. Unlike IPMN-derived cancer, concomitant PDAC may develop independently of cyst size or growth and is often detected at an advanced stage. Therefore, surveillance of patients with IPMN should include not only evaluation of the PCL itself and assessment for IPMN-derived cancer, but also systematic assessment of the entire pancreas for concomitant PDAC. This review discusses the differential diagnosis of PCLs, imaging assessment of IPMN-derived cancer, the concept and imaging clues of concomitant PDAC, and practical considerations for surveillance strategies.
To determine whether intensity-modulated radiation therapy (IMRT) plan quality under a proposed facility standard of one full-time radiation oncologist supported by structured remote or part-time peer review and mandatory planning assistance is comparable to Japan’s current standard requiring two full-time radiation oncologists. In this multicenter, pair-matched, prospective trial, 41 institutional pairs were enrolled and 36 completed per protocol. Within each pair, one institution had two full-time radiation oncologists (Group A) and the other had one full-time radiation oncologist (Group B), with mandated remote or part-time peer review and planning-assistant involvement at the single-oncologist site. Common cases (prostate cancer; nasopharyngeal carcinoma [NPC] with whole-neck [WNI] or local boost [LBI] irradiation) and consensus reference contours were provided. Primary endpoints were target delineation accuracy versus the reference contour (dice similarity coefficient, DSC) and plan quality (plan quality metric, PQM; 0–100). Differences in DSC (Group B − A) were 0.00 (95
Dual-Energy Computed Tomography (DECT) represents a technological breakthrough in diagnostic imaging, offering the unique ability to characterize and differentiate tissues. This review analyzes the expanding role of DECT within emergency radiology, highlighting how its added value over conventional CT improves decision-making in acute clinical settings. In emergency neuroradiology, DECT allows for a critical distinction between acute intracranial hemorrhage and iodinated contrast extravasation. In vascular emergencies, it optimizes the evaluation of the arterial tree and is crucial for the timely diagnosis of acute pulmonary embolism through iodine perfusion maps that identify subtle filling defects. In acute abdominal imaging, DECT improves the characterization of traumatic or inflammatory lesions and enables precise chemical mapping of renal calculi, differentiating uric acid from calcium oxalate to guide immediate therapy. Finally, in emergency musculoskeletal imaging, calcium suppression algorithms accurately visualize bone marrow edema, helping to detect occult fractures and acute traumatic injuries. In conclusion, DECT has transitioned from a research tool to an essential asset in emergency radiology. By significantly enhancing diagnostic accuracy and streamlining workflows, DECT optimizes patient triage and management in time-critical acute scenarios across multiple anatomical regions.
Prostate-specific membrane antigen positron emission tomography (PSMA-PET) has become pivotal in prostate cancer (PCa) management, offering superior sensitivity over conventional imaging for detecting tumors, metastases, and biochemical recurrence. However, interpretive subjectivity, workflow inefficiencies, and heterogeneous PSMA expression remain significant limitations. Artificial intelligence (AI), particularly radiomics and deep learning, addresses these challenges by enabling automated lesion analysis and image enhancement. This review examines the impact of AI across the PSMA-PET workflow, covering optimized image acquisition (e.g., low-dose protocols, motion correction), enhanced interpretation (e.g., lesion characterization, prognostic stratification), and personalized theranostics (e.g., treatment response forecasting, radioligand therapy dosimetry). Despite promising multicenter validation, challenges remain in annotation standardization, data heterogeneity, model generalizability, interpretability, regulatory integration, and ethics. We further discuss emerging frontiers, including multimodal multi-omic integration, generative AI, and AI-driven clinical decision support systems. Notably, we highlight the evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertise. These advances position AI-integrated PSMA-PET to drive precision oncology, with key pathways outlined for clinical translation and future innovation in PCa care.
The efficacy of single-fraction 8 Gy radiotherapy (RT) for achieving hemostasis remains unclear. In this study, we assessed (1) hemostatic efficacy, (2) rebleeding, and (3) factors related to hemostatic efficacy and rebleeding associated with single-fraction 8 Gy RT. Patients with tumor bleeding who received single-fraction 8 Gy RT between January 2020 and December 2024 were retrospectively examined. Factors associated with hemostasis and rebleeding (recurrence of bleeding signs/symptoms, need for red blood cell [RBC] transfusion or salvage treatment after confirmation of hemostasis or decreasing hemoglobin levels) were assessed. A total of 90 lesions in 82 patients were examined. The median follow-up time after the administration of RT treatment was 80 days (interquartile range [IQR], 35–206 days; range, 7–776 days). The overall hemostatic ratio of lesions within 30 days (= 30-day hemostatic response rate) was 68.9
Metabolic syndrome (MetS) is screened for or assessed by manual waist circumference (WC) measurements or adipose tissue evaluation using supine multidetector CT (MDCT). Currently, upright MDCT was clinically established and tested for different clinical aims. The purpose of this study was to directly compare the utility of metabolic disorder assessments and the diagnosis of MetS among WC, upright MDCT, and supine MDCT in a Japanese health checkup test cohort. This study cohort consisted of 47 consecutive subjects who underwent WC measurements, blood tests, upright MDCT, and supine MDCT on the same day. In each subject, the total fat area (TFA), subcutaneous fat area (SFA), visceral fat area (VFA), and visceral-to-subcutaneous fat ratio (VSR) were semi-automatically assessed using upright MDCT and supine MDCT data. To compare each index on both CTs, as well as manual WC measurements between the MetS and non-MetS groups, Student’s t-test was performed. Univariate regression analyses were performed to evaluate the relationship between each index and blood test results. Subsequently, receiver operating characteristic (ROC)-based positive tests were performed. Finally, the sensitivity, specificity, and accuracy were compared among all indexes from both CTs, and a manual WC measurement evaluation was conducted using McNemar’s test. Some indices showed significant differences between two groups (p < 0.05). All indices shows significant correlations with BMI, TG, HDL-cholesterol, uric acid, glucose, HbA1c, insulin, HOMA-IR, GOT (or AST), GPT (or ALT), or creatinine (manual WC measurement: − 0.58 ≤ r ≤ 0.87, p < 0.05; supine MDCT: − 0.61 ≤ r ≤ 0.84, p < 0.05; upright MDCT: − 0.64 ≤ r ≤ 0.91, p < 0.05). TFA and VFA on upright MDCT had significantly higher sensitivities than WCs determined from manual and supine MDCT as well as the VSR (p < 0.05). Upright MDCT-derived adipose tissue indices showed preliminary potential for assessing metabolic syndrome, but larger prospective studies are needed to validate their clinical utility.
To compare the lesion detection rates of fibroblast activation protein inhibitor (FAPI) PET/CT and 18F-FDG PET/CT in hepatocellular carcinoma (HCC) using currently available head-to-head evidence. Databases including PubMed, Embase, Cochrane Library, Scopus, and Web of Science were searched for head-to-head studies comparing FAPI and FDG for HCC lesion detection up to March 2026. Two researchers independently performed literature screening and data extraction, and assessed the risk of bias using the QUADAS-2 tool. Meta-analysis was conducted using R 4.5.3 software. The primary outcome was lesion detection-rate difference, expressed as risk difference (RD) with 95
The salivary glands give rise to a diverse range of histologic tumor types. The World Health Organization (WHO) Classification of Head and Neck Tumors has been revised from the fourth edition (2017) to the fifth edition; the online version was released in March 2022, followed by the publication of the WHO Blue Book in March 2024. In the current classification of salivary gland tumors (SGTs), 15 benign and 21 malignant epithelial tumor types are categorized. Furthermore, four new benign entities were added: intercalated duct adenoma/hyperplasia, striated duct adenoma, sclerosing polycystic adenoma, and keratocystoma. Imaging features of prevalent benign tumors, such as pleomorphic adenoma, Warthin tumor, basal cell adenoma, salivary gland myoepithelioma, and oncocytoma, are thoroughly documented in the radiologic literature. Conversely, infrequently encountered and newly categorized benign tumors often remain unrecognized by many radiologists. In this Part I of a two-part series, we evaluate infrequently encountered benign SGTs within the current WHO classification, including cystadenoma of the salivary glands, canalicular adenoma, sialadenoma papilliferum, ductal papillomas, sebaceous adenoma, and lymphadenoma. The discussion focuses on diagnostic criteria with an emphasis on radiologic–pathologic correlations.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with early and progressive hippocampal pathology serving as a hallmark across the AD continuum, including subjective cognitive decline, mild cognitive impairment, and AD dementia. Single-modal MRI fails to fully capture multilevel hippocampal neuropathology, hindering accurate early diagnosis and prognostic prediction of AD. This narrative review summarizes recent advances in structural MRI, diffusion tensor imaging, magnetic resonance spectroscopy, quantitative susceptibility mapping, arterial spin labeling, and functional MRI for evaluating hippocampal and medial temporal lobe abnormalities in AD. These techniques provide a multifaceted characterization of AD-associated hippocampal alterations, including macroscopic atrophy, microstructural degeneration, metabolic dysregulation, aberrant iron deposition, hemodynamic dysfunction, and abnormal neuronal activity. Hippocampal damage in AD exhibits distinct subfield specificity, hemispheric asymmetry, and stage-dependent progression, modulated by Aβ/tau pathology, neuroinflammation, and apolipoprotein E ε4 genotype. Notably, multimodal imaging fusion integrated with machine learning outperforms single-modal biomarkers, significantly improving the accuracy of AD early screening, differential diagnosis, and prognostic prediction. Nevertheless, existing studies are hampered by inadequate pathological validation, limited sample sizes, unsatisfactory reproducibility, and multicenter technical heterogeneity. Multimodal MRI offers robust non-invasive evidence for exploring AD hippocampal pathophysiology. Future large-scale multicenter longitudinal studies combining artificial intelligence will advance precision diagnosis and targeted therapy for AD by clarifying the interplay among AD pathology, genetics, and heterogeneous hippocampal injury.
To evaluate how two fixed computer-aided detection (CAD) operating modes—sensitivity-optimized (SE-CAD) and specificity-optimized (SP-CAD)—affect radiologists’ diagnostic performance in chest radiography (CXR). Previous studies have focused on the performance of standalone CAD at different threshold settings. However, in clinical practice, CAD is commonly used as a decision-support tool for radiologists. This study aimed to evaluate the impact of different CAD threshold settings on radiologists’ diagnostic performance. This retrospective single-center diagnostic accuracy study evaluated posteroanterior or anteroposterior CXRs acquired between 2013 and 2023, interpreted with and without AI assistance, using CT as the reference standard. Six radiologists (three residents / three board-certified) read all cases four times over two sessions: first without CAD, then with either SE_CAD or SP_CAD, and after 1-month washout period, second session using the alternative CAD. 375 patients (mean age, 65 ± 15 years; 170 women) were included. The dataset comprised 139 cases with pulmonary opacities (nodules / masses / consolidations), 119 with pleural effusion, 29 with pneumothorax, and 161 normal cases, including overlaps. SP_CAD-assisted readings showed higher specificity than SE_CAD-assisted for pulmonary opacities (p < 0.001). Sensitivity did not differ significantly between SE_CAD and SP_CAD assistance. For pulmonary opacities, CAD-assisted reading showed higher sensitivity (both CAD: p < 0.001) but lower specificity (SE_CAD: p = 0.006; SP_CAD: p = 0.03) than unassisted reading. For pleural effusion, CAD-assisted improved sensitivity (SE_CAD: p < 0.001; SP_CAD: p = 0.02) and specificity (both CAD: p < 0.001) than unassisted reading. For pneumothorax, SE_CAD assistance did not significantly affect sensitivity but decreased specificity, whereas SP_CAD assistance improved sensitivity without significantly affecting specificity. The specificity-prioritized CAD showed higher specificity than the sensitivity-prioritized CAD, without a significant reduction in sensitivity. These findings suggest that different CAD operating points can influence radiologists’ diagnostic performance. We compared radiologist performance using sensitivity-optimized and specificity-optimized computer-aided detection (CAD). Different predefined CAD operating points influenced radiologist performance, resulting in differences in specificity for nodules/masses/consolidations. Our results show that different preset decision thresholds within the same AI model can influence diagnostic performance in AI-assisted radiograph interpretation.
Transcatheter arterial embolization is an essential treatment for lower gastrointestinal bleeding; however, embolization of multiple vasa recta carries a substantial risk of bowel infarction. This study aimed to evaluate the safety of quick-soluble gelatin sponge particles (QS) compared with conventional gelatin sponge particles (GS) in a canine model of superior mesenteric artery branch embolization. This study planned to include 10 Beagle dogs. The entire ileocolic artery and its vasa recta were non-selectively embolized to create a stringent ischemic environment. DSA was performed at 15, 30, and 60 min after embolization and on days 1 and 7 post-embolization. Dogs were randomly assigned to receive QS (n = 5) or GS (n = 5). The primary outcome was 7-day survival. Secondary outcomes included angiographic recanalization, as well as macroscopic and histopathological evaluation of intestinal necrosis. Given the first two mortality cases in the GS group, further enrollment in the GS group was halted. All five dogs in the QS group survived the 7-day observation period, demonstrating a significantly higher survival rate than the GS group (100
The purpose of this study was to elucidate the association triple-negative breast cancer (TNBC) with peritumoral fat content identified by using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL), and to compare IDEAL-based fat measurements with findings obtained from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). We analyzed 538 consecutive patients diagnosed with breast cancer who underwent IDEAL MRI prior to surgery or biopsy. After applying the exclusion criteria, 265 patients with 270 lesions were included in this study. Using a 3 T MRI system, we created fat fraction maps and measured the average peritumoral fat fraction value (TFF) in 4 regions of interest (ROIs) surrounding the tumor. We also calculated the fat fraction ratio (pTFR: TFF/HFF) by measuring fat fraction in the contralateral healthy side breast (HFF). Additionally, we assessed apparent diffusion coefficient (ADC) values from DWI, classified peritumoral edema based on T2WI-based grading system and evaluated the presence of rim enhancement on the early phase DCE-MRI images. The TNBC and non-TNBC groups consisted of 38 and 232 lesions, respectively. The TNBC group exhibited significantly lower TFF and pTFR values compared with the non-TNBC group (p < 0.001). A significant association was observed with T2 edema (p = 0.028), whereas no significant difference was found in ADC values (p = 0.946). Among the significantly contributing quantitative parameters (p < 0.05), pTFR demonstrated the highest diagnostic performance (AUC = 0.749, 95
Research on vision-language models (VLMs) in the medical field has recently increased. However, while multifaceted evaluation is necessary to avoid the high risks associated with misdiagnosis, artificial intelligence (AI)-assisted mammogram report generation remains insufficient, with no studies on objective and subjective generation. We aimed to develop an AI system that generates mammogram reports and to verify the impact of differences between objective and subjective evaluation methods on the interpretation of this AI system. We used a public dataset consisting of mammograms and their reports, preparing question prompts and performing low-rank adaptation tuning on Qwen2.5(7B). We analyzed the Breast Imaging Reporting and Data System (BI-RADS) and findings agreement rate, Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and Bilingual Evaluation Understudy (BLEU) for the objective evaluation. A breast clinician performed score-based evaluations of generated reports as subjective assessments. Finally, we analyzed samples of inconsistent objective and subjective results. The BI-RADS agreement rate was 58.1
The World Health Organization (WHO) Classification of Head and Neck Tumors was updated from the fourth edition (published in 2017) to the fifth edition (published in 2024). The current WHO classification lists 15 benign and 21 malignant epithelial salivary gland tumors (SGTs). Among the benign epithelial tumors, four entities are newly listed: intercalated duct adenoma/hyperplasia, striated duct adenoma, sclerosing polycystic adenoma, and keratocystoma. This second article in a two-part review series summarizes the four newly listed benign SGTs, focusing on their classification background, clinicopathologic features, and reported imaging findings. As the published imaging literature remains limited, radiologic-pathologic correlation remains essential for recognizing these rare entities and avoiding overinterpretation of benign lesions. The aims of this review are to clarify the currently recognized imaging features of these rare tumors while emphasizing the need for careful integration of imaging, histopathologic, immunohistochemical, and molecular findings.
A subset of uterine cervical cancers recurs locally after definitive radiotherapy, highlighting the need for establishing biomarkers to predict radioresistance for dose optimisation. However, low local recurrence rate constitutes a barrier to research progress. This study aimed at exploring candidate predictive biomarker of radioresistance of uterine cervical cancer by analyzing multiple omics layers using nested case-control approach. A discovery cohort (n = 8) was created from a series of 192 patients (all with squamous cell carcinoma of the uterine cervix underwent definitive radiotherapy) by 1:1 random case-control matching on pelvic recurrence (PR) for RNA sequencing. A validation cohort (n = 54) was created from the same series by 1:2 propensity score matching on PR for panel-based DNA sequencing and pelvic recurrence-free survival (PRFS) analysis. by using the Kaplan–Meier method and cause-specific proportional hazards models. In the discovery cohort, Gene set enrichment analysis revealed that the genes targeted by MYC were upregulated in patients with PR. In the validation cohort, the prevalence of MYC amplification was significantly greater in patients with PR than in those without (72
Adjacent vessel geometry may affect focal hemodynamics and plaque features in intracranial atherosclerotic disease. To investigate the correlations of upstream (internal carotid artery (ICA) bifurcation) and focal arterial geometry of M1 middle cerebral artery (MCA-M1) with location of MCA-M1 plaques and focal hemodynamics, particularly wall shear stress (WSS) metrics. In patients with symptomatic atherosclerotic stenosis (50–99
Joint space narrowing (JSN) in rheumatoid arthritis (RA) can progress even during clinical remission. Conventional imaging lacks sensitivity for detecting early risks. This study evaluates AI-based analysis of bilateral joint-space asymmetry on hand radiographs to detect future JSN progression in radiographically normal RA. Forty-six radiographically normal RA patients underwent bilateral hand radiography and high-resolution peripheral quantitative computed tomography (HR-pQCT) at baseline, 12, and 24 months. Five joint space parameters were extracted from HR-pQCT. Gaussian mixture modeling (GMM) stratified patients into progressive and non-progressive phenotypes using 24 month data. An AI pipeline was developed to measure bilateral joint space asymmetry from baseline X-rays using deep learning–based segmentation and registration. Predictive models were constructed using logistic regression and evaluated via ROC curves. GMM identified 17 progressive and 29 non-progressive patients. HR-pQCT-derived intra-joint space width inconsistency (JSW.Inc) increased significantly over time in progressors (p = 0.0337). At baseline, HR-pQCT-based JSW.Inc showed fair predictive power (AUC = 0.696). AI-derived asymmetry at PIP3 and MCP3 joints showed stronger baseline prediction (AUCs = 0.739 and 0.714, respectively). By month 12, predictive performance improved across models (AUCs up to 0.836 for HR-pQCT and 0.748 for AI-based metrics). AI-assisted analysis of bilateral joint space asymmetry from hand radiography may provide an accessible method for detecting radiographic JSN progression, with prognostic findings that should be interpreted as exploratory and hypothesis-generating. This method complements conventional imaging tools and may support future investigations into early risk stratification.
