Japanese Journal of Radiology最新文献

{"title":"Heart volume on health checkup CT scans inversely correlates with pulse rate: data-driven analysis using deep-learning segmentation.","authors":"Kanato Masayoshi, Masahiro Hashimoto, Naoki Toda, Hirozumi Mori, Goh Kobayashi, Hasnine Haque, Kohei Furuya, Takahiro Watanabe, Masahiro Jinzaki","doi":"10.1007/s11604-025-01772-y","DOIUrl":"10.1007/s11604-025-01772-y","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to elucidate correlation between heart volume on computed tomography (CT) and various health checkup examination data in the general population. Furthermore, this study aims to examine the utility of a deep-learning segmentation tool in the data-driven analysis of CT big data.</p><p><strong>Materials and methods: </strong>Health checkup examination data and CT images acquired in 2013 and 2018 were retrospectively analyzed. We first quantified heart volume using a public deep-learning model, TotalSegmentator. The accuracy of segmentation was evaluated using Dice score on 30 randomly chosen images and annotation by a radiologist. Then, Spearman's partial correlation was calculated for 58 numerical items, and the analysis of covariance was performed for 13 categorical items, adjusting for the effect of gender, medication, height, weight, abdominal circumference, and age. The variables found to be significant proceeded to longitudinal analysis.</p><p><strong>Results: </strong>In the dataset, 7993 records were eligible for cross-sectional analysis and 1306 individuals were eligible for longitudinal analysis. Pulse rate was most strongly inversely correlated with the heart volume (Spearman's correlation coefficients ranging from - 0.29 to - 0.33). A 10 bpm increase in pulse rate was correlated with roughly a 0.5 percentage point decrease in the cardiothoracic ratio. Hemoglobin, hematocrit, total protein, albumin, and cholinesterase also showed weak inverse correlation. Five-year longitudinal analysis corroborated these findings.</p><p><strong>Conclusions: </strong>We found that pulse rate was the strongest covariate of the heart volume on CT, rather than other cardiovascular-related variables such as blood pressure. The study also demonstrated the feasibility and utility of the artificial intelligence-assisted data-driven research on CT big data.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1295-1302"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging in paraneoplastic neurological syndromes: a comprehensive review.","authors":"Ebinesh Arulnathan, Sathyashree, Praislin Gideon, M Lokesh, K Rajiv Gandhi","doi":"10.1007/s11604-025-01783-9","DOIUrl":"10.1007/s11604-025-01783-9","url":null,"abstract":"<p><p>Paraneoplastic neurological syndromes (PNS) are a complex spectrum of clinical syndromes that are associated with underlying malignancies. Majorly being immune-mediated, these syndromes are characterized by the presence of specific paraneoplastic antibodies that are either directed against intracellular (onconeuronal) or extracellular (surface or junctional) antigens. Syndromes associated with the occurrence of onconeuronal antibodies have poor prognoses. The spectrum of neurological manifestations includes limbic encephalitis, paraneoplastic cerebellar degeneration, rhombencephalitis, myelitis, optic neuropathy, cranial neuropathies, plexopathy and polyneuropathy. This review presents a comprehensive overview of these manifestations including their imaging features, associated malignancies and antibodies, differential diagnoses and recommended further evaluation.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1269-1285"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the wall-invasion pattern of advanced gallbladder carcinoma using extracellular volume fraction.","authors":"Yukihisa Takayama, Takehiko Koga, Yoshihiro Hamada, Shinji Tanaka, Keisuke Sato, Ryo Murayama, Yusuke Ishida, Masatoshi Kajiwara, Kengo Yoshimitsu","doi":"10.1007/s11604-025-01768-8","DOIUrl":"10.1007/s11604-025-01768-8","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the utility of extracellular volume (ECV) fraction for predicting wall-invasion patterns in advanced gallbladder carcinoma (GBCA).</p><p><strong>Materials and methods: </strong>Patients who had surgically resected GBCA at a single institution were retrospectively evaluated. All patients underwent computed tomography (CT) before the surgery. Based on pathological examinations, the wall-invasion pattern of GBCA was classified into two groups: infiltrative growth (IG, n = 19) and destructive growth (DG, n = 11). ECV map was generated by inputting the patients' hematocrit values and subtraction algorithms using pre-contrast and equilibrium phase images. CT parameters were evaluated by two radiologists (Rad1 and Rad2). The Mann-Whitney U test was performed to identify significant CT parameters for differentiating between the two groups. The diagnostic ability was measured using receiver operating characteristic (ROC) curve analysis. Recurrence-free survival (RFS) was estimated using the Kaplan-Meier method, and differences between the two groups were compared using the log-rank test.</p><p><strong>Results: </strong>Thirty patients (mean age, 75.5 years; 20 men) were evaluated. Mean ECV fraction of the DG-type (Rad1, 34.5%; Rad2, 34.1%) was significantly higher than that of the IG-type (Rad1, 28.5%; Rad2, 28.8%) (p < 0.05). The ECV values of the two radiologists indicated that the areas under the ROC curves for differentiation between the two groups were Rad1, 0.91 and Rad2, 0.84 (p < 0.05). Medium RFS of the DG-type (970 days) was significantly shorter than that of the IG-type (2200 days) (p < 0.05).</p><p><strong>Conclusion: </strong>ECV fraction demonstrates potential as the most valuable predictor of the DG type of GBCA, which has a higher recurrence rate compared with the IG type. However, further large-scale multi-center studies are required to validate these findings.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1323-1334"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.","authors":"Hao Wu, XiaoLi Wu, ShouLiang Miao, GuoQuan Cao, Huang Su, Jie Pan, YiLun Xu, JianWei Zhou","doi":"10.1007/s11604-025-01780-y","DOIUrl":"10.1007/s11604-025-01780-y","url":null,"abstract":"<p><strong>Background: </strong>Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improving patient outcomes. This study leverages the power of deep learning, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze arterial phase enhanced CT images and predict LNM in ESCC patients.</p><p><strong>Methods: </strong>A retrospective study included 441 ESCC patients who underwent radical esophagectomy and regional lymphadenectomy. CT imaging was performed using contrast-enhanced CT scanners. Tumor region segmentation was conducted to determine the region of interest (ROI), where local tumor 3D volumes were extracted as input for the model. The novel deep learning model, LymphoReso-Net, combined CNN and LSTM networks to process and learn from medical imaging data. The model outputs a binary prediction for LNM. GRAD-CAM was integrated to enhance model interpretability. Performance was evaluated using fivefold cross-validation with metrics including accuracy, sensitivity, specificity, and AUC-ROC. The gold standard for LNM confirmation was pathologically confirmed LNM shortly after the CT.</p><p><strong>Results: </strong>LymphoReso-Net demonstrated promising performance with an average accuracy of 0.789, an AUC of 0.836, a sensitivity of 0.784, and a specificity of 0.797. GRAD-CAM provided visual explanations of the model's decision-making, aiding in identifying critical regions associated with LNM prediction.</p><p><strong>Conclusion: </strong>This study introduces a novel deep learning framework, LymphoReso-Net, for predicting LNM in ESCC patients. The model's accuracy and interpretability offer valuable insights into lymphatic spread patterns, enabling more informed therapeutic decisions.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1347-1356"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based delta-radiomics signature of visceral adipose tissue for prediction of disease progression in ileal stricturing Crohn's disease.","authors":"Jingwen Zhang, Shanyu Qin, Haixing Jiang","doi":"10.1007/s11604-025-01779-5","DOIUrl":"10.1007/s11604-025-01779-5","url":null,"abstract":"<p><strong>Objectives: </strong>To establish and validate a model based on CT imaging during follow-ups for predicting the disease progression in ileal stricturing Crohn's disease (CD).</p><p><strong>Methods: </strong>Between January 2014 and February 2024, a retrospective review was conducted on 71 patients (training, n = 49; test, n = 22) who were initially diagnosed with ileal stricturing CD. Disease progression referred to the development of penetrating diseases, the requirement for CD-related hospitalization or surgery during follow-up. Radiomics features were extracted from visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) on baseline and follow-up CT scans, respectively. Integrating clinical characteristics and body composition features, a novel CT-based delta-radiomics nomogram was established according to multivariate Cox stepwise regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance.</p><p><strong>Results: </strong>The delta-VAT radiomics model (RM) exhibited satisfactory performance in training cohort (the area under the ROC curve [AUC] = 0.792, 95% confidence Interval [CI] 0.666-0.917) and in test cohort (AUC = 0.640, 95% CI 0.411-0.870). The AUCs of the delta-SAT RM were 0.777 (95% CI 0.648-0.907) in training cohort and 0.612 (95% CI 0.377-0.846) in test cohort. The combined nomogram model showed good discrimination for predicting disease progression, with a C-index of 0.808 and 0.702 in the training and test cohorts, respectively.</p><p><strong>Conclusions: </strong>We first constructed a comprehensive model incorporating delta-adipose radiomics, baseline neutrophil-to-lymphocyte ratio (NLR) level and the application of biological therapy to predict progression in ileal stricturing CD, which aids in the timely adjustment of therapeutic strategies and enhances patients' quality of life.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1335-1346"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of a virtual reality-based interventional radiology simulator for medical student education.","authors":"Hidenori Mitani, Yukiko Honda, Keigo Narita, Yuko Nakamura, Shintaro Morishita, Shota Kondo, Shogo Maeda, Haruka Higashibori, Keigo Chosa, Toru Higaki, Ikuo Kawashita, Minoru Hattori, Naoko Hasunuma, Isamu Saeki, Shinya Takahashi, Naoki Mihara, Kazuo Awai","doi":"10.1007/s11604-025-01771-z","DOIUrl":"10.1007/s11604-025-01771-z","url":null,"abstract":"<p><strong>Purpose: </strong>We developed an interventional radiology (IR) simulator using a virtual reality system (the VR-IR simulator) to teach IR procedures to medical students. In this study, we investigated the effectiveness of this teaching method.</p><p><strong>Materials and methods: </strong>All ninety-nine fifth-year medical students attended a conventional classroom lecture. To teach students the actual procedure, they were randomly divided into two groups: One received conventional verbal explanations and educator demonstrations (the conventional group [n = 44]), and the other received VR-IR simulator training (the VR-IR simulator group [n = 55]). Afterward, they underwent a test using an augmented reality- (AR-) IR simulator (the VIST^® G5 image-guided AR-IR simulator, Mentice, Gothenburg, Sweden). The total procedure time, amount of contrast media used, fluoroscopic time, and patient peak skin dose in the simulated patients were compared between groups. A board-certified radiologist evaluated ten aspects of the procedure technique using a 5-point Likert scale (total: 50 points).</p><p><strong>Results: </strong>Two students in the VR-IR simulator group were excluded due to VR sickness and simulator malfunction. There were no significant differences between the VR-IR simulator group and the conventional group regarding total procedure time (median [25-75% interquartile range]: 13.5 [11.8-14.5] vs. 14.3 [12.3-16.8] minutes, p = 0.11), fluoroscopic time (10.1 [8.5-13.0] vs. 11.0 [8.6-13.7] minutes, p = 0.31), and patient peak skin dose (276 [243-373] vs. 303 [239-395] mGy, p = 0.57), respectively. However, the amount of contrast media used was significantly lower (28.0 [21.0-36.2] vs. 40.0 [32.3-50.9] mL, p < 0.01) and the technical achievement scores by the radiologist (36 [34-44] vs. 31 [29-32], p < 0.01) were significantly higher in the VR-IR simulator group.</p><p><strong>Conclusion: </strong>The VR-IR simulator helped reduce the amount of contrast media in interventional procedures and improved technical achievement scores.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1386-1392"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of clinical factors associated with incomplete standard protocol ¹⁷⁷Lu-DOTATATE treatment in neuroendocrine tumor patients.","authors":"Shigeyasu Sugawara, Shozo Okamoto, Hirofumi Go, Shiro Ishii, Hiroshi Ito, Tohru Shiga, Noboru Oriuchi","doi":"10.1007/s11604-025-01769-7","DOIUrl":"10.1007/s11604-025-01769-7","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the clinical features of patients with neuroendocrine tumors (NETs) who could not complete the standard protocol (4 cycles of 7.4 GBq per 8 weeks) ¹⁷⁷Lu-DOTATATE treatment.</p><p><strong>Materials and methods: </strong>A retrospective single-center analysis was conducted on 26 patients who underwent ¹⁷⁷Lu-DOTATATE treatment between December 2021 and August 2024. Therapeutic outcome was compared with clinical features, including location and number of metastatic lesions, interval from diagnosis to the first ¹⁷⁷Lu-DOTATATE treatment, and laboratory data. Statistical analyses were performed to identify clinical features associated with dose reduction or treatment discontinuation.</p><p><strong>Results: </strong>The clinical data of 24 patients with metastatic neuroendocrine tumors (NETs) were analyzed, of whom 16 patients completed the standard protocol ¹⁷⁷Lu-DOTATATE treatment. The most common adverse events were hematologic toxicities. Eight patients did not complete the standard protocol treatment, primarily due to adverse events (6/8). Single variable logistic regression analysis revealed that the presence of somatostatin receptor scintigraphy (SRS) positive bone metastases (OR = 21.0, 95% CI 2.37-186, p = 0.006) and lower hemoglobin levels (OR = 0.479, 95% CI 0.255-0.900, p = 0.022) were significantly associated with incomplete treatment. Notably, 5/8 patients in the incomplete group had extensive bone metastases (> 20 lesions), including 4 with diffuse metastases. Other variables, including age, sex, white blood cell count, platelet count, eGFR, and other metastatic sites, showed no significant association with treatment completion.</p><p><strong>Conclusions: </strong>In this study, the presence of SRS-positive bone metastases and low hemoglobin levels were significant factors associated with the inability to complete ¹⁷⁷Lu-DOTATATE treatment for NET patients. Extensive bone metastases, such as diffuse metastasis or more than 20 bone metastases, may be particularly associated with the inability to administer standard protocol treatment.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1372-1379"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.","authors":"So Ode, Atsuko Fujikawa, Atsushi Hiroishi, Yuki Saito, Takao Tanuma, Daigo Suzuki, Yuichi Sasaki, Hidefumi Mimura","doi":"10.1007/s11604-025-01775-9","DOIUrl":"10.1007/s11604-025-01775-9","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.</p><p><strong>Materials and methods: </strong>Forty-six consecutive cases of pediatric and neonatal chest X-rays were identified for the quality evaluation. The images underwent AI-based noise reduction processing (Intelligent NR, Canon Inc.). All the images were randomized, and were evaluated from 1 to 4 for image quality by three board-certified radiologists in consensus. A score of \"1\" indicated the desired anatomy or features were not seen, \"2\" indicated quality between one and three, \"3\" indicated adequate quality, and \"4\" indicated higher than required image quality. A Wilcoxon signed rank test was used to assess the significant difference between images from conventional noise reduction versus those from the AI-based noise reduction.</p><p><strong>Results: </strong>The images processed with the INR(Intelligent NR) noise reduction had a higher image quality than the conventionally processed images, with a significant difference between the two groups (p < 0.05).</p><p><strong>Conclusion: </strong>The AI-based noise reduction technique improved the image quality of pediatric and neonatal chest and abdominal radiography significantly.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1365-1371"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143795613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical brain MRI guidelines for anti-Aβ antibody treatment in early symptomatic Alzheimer's disease.","authors":"Shingo Kakeda, Yukio Miki, Kohsuke Kudo, Harushi Mori, Aya M Tokumaru, Osamu Abe, Shigeki Aoki","doi":"10.1007/s11604-025-01773-x","DOIUrl":"10.1007/s11604-025-01773-x","url":null,"abstract":"<p><strong>Purpose: </strong>These guidelines aim to support magnetic resonance imaging (MRI) diagnosis in patients receiving anti-amyloid β (Aβ) antibody treatment without restricting treatment eligibility.</p><p><strong>Materials and methods: </strong>These guidelines were collaboratively established by Japan Radiological Society, The Japanese Society of Neuroradiology, and Japanese Society for Magnetic Resonance in Medicine by reviewing existing literature and the results of clinical trials.</p><p><strong>Results: </strong>Facility standards should comply with the \"Optimal Use Promotion Guidelines\" of Japan, and physicians should possess comprehensive knowledge of amyloid-related imaging abnormalities (ARIA) and expertise in brain MRI interpretation. The acquisition of knowledge regarding amyloid-related imaging abnormalities, brain MRI, anti-Aβ antibody introduction, and post-treatment diagnosis are also recommended.</p><p><strong>Conclusion: </strong>These guidelines facilitate the accurate diagnosis and effective management of ARIA; ensure the safe administration of anti-Aβ drugs; and provide a framework for MRI facilities, includes staffing requirements and the use of MRI management systems.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1231-1238"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rare pancreatic ductal adenocarcinoma variants and other malignant epithelial tumors: a comprehensive clinical and radiologic review.","authors":"Yukihisa Takayama, Ryo Murayama, Shinji Tanaka, Keisuke Sato, Kazuki Goto, Gaku Honda, Kengo Yoshimitsu","doi":"10.1007/s11604-025-01777-7","DOIUrl":"10.1007/s11604-025-01777-7","url":null,"abstract":"<p><p>Over 95% of pancreatic carcinomas are classified as conventional pancreatic ductal adenocarcinoma (cPDAC), while less than 5% consist of rare histological subtypes. Some of these rare histological subtypes, such as colloid carcinoma, medullary carcinoma, and undifferentiated carcinoma with osteoclast-like giant cells, are associated with a relatively better prognosis compared to cPDAC, whereas others, including signet ring cell carcinoma/poorly cohesive carcinoma, adenosquamous carcinoma, large cell carcinoma with rhabdoid phenotype, and undifferentiated carcinoma, have a worse prognosis. Other malignant pancreatic epithelial tumors (MPET) include acinar cell carcinoma, pancreatoblastoma, and solid-pseudopapillary neoplasm that should also be differentiate from PDACs. Accurate differentiation among PDAC subtypes and other MPETs is essential for precise survival predictions and effective therapeutic planning. However, cPDAC, rare histological subtypes of PDAC and MPETs often exhibit similar imaging findings, making it challenging to establish a diagnosis based solely on imaging. Thus, needle biopsy or surgical resection is generally required for the final diagnosis. We herein present a review article based on case studies and literature reviews of rare histological subtypes of PDAC and other MPET, with particular focus on their imaging characteristics, referencing the 5th edition of the World Health Organization classification.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1239-1260"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}