Insights into Imaging最新文献

{"title":"CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study.","authors":"Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu","doi":"10.1186/s13244-025-01980-0","DOIUrl":"https://doi.org/10.1186/s13244-025-01980-0","url":null,"abstract":"<p><strong>Purpose: </strong>To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features.</p><p><strong>Results: </strong>The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks.</p><p><strong>Conclusions: </strong>The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC.</p><p><strong>Critical relevance statement: </strong>The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment.</p><p><strong>Key points: </strong>Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"102"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study.","authors":"Liyong Zhuo, Wenjing Chen, Lihong Xing, Xiaomeng Li, Zijun Song, Jinghui Dong, Yanyan Zhang, Hongjun Li, Jingjing Cui, Yuxiao Han, Jiawei Hao, Jianing Wang, Xiaoping Yin, Caiying Li","doi":"10.1186/s13244-025-01985-9","DOIUrl":"https://doi.org/10.1186/s13244-025-01985-9","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC).</p><p><strong>Methods: </strong>Preoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>The final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52-65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25-69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset.</p><p><strong>Conclusion: </strong>The combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading.</p><p><strong>Critical relevance statement: </strong>This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management.</p><p><strong>Key points: </strong>Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"101"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models for efficient whole-organ MRI score-based reports and categorization in knee osteoarthritis.","authors":"Yuxue Xie, Zhonghua Hu, Hongyue Tao, Yiwen Hu, Haoyu Liang, Xinmin Lu, Lei Wang, Xiangwen Li, Shuang Chen","doi":"10.1186/s13244-025-01976-w","DOIUrl":"10.1186/s13244-025-01976-w","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the performance of large language models (LLMs) in automatically generating whole-organ MRI score (WORMS)-based structured MRI reports and predicting osteoarthritis (OA) severity for the knee.</p><p><strong>Methods: </strong>A total of 160 consecutive patients suspected of OA were included. Knee MRI reports were reviewed by three radiologists to establish the WORMS reference standard for 39 key features. GPT-4o and GPT-4o-mini were prompted using in-context knowledge (ICK) and chain-of-thought (COT) to generate WORMS-based structured reports from original reports and to automatically predict the OA severity. Four Orthopedic surgeons reviewed original and LLM-generated reports to conduct pairwise preference and difficulty tests, and their review times were recorded.</p><p><strong>Results: </strong>GPT-4o demonstrated perfect performance in extracting the laterality of the knee (accuracy = 100%). GPT-4o outperformed GPT-4o mini in generating WORMS reports (Accuracy: 93.9% vs 76.2%, respectively). GPT-4o achieved higher recall (87.3% s 46.7%, p < 0.001), while maintaining higher precision compared to GPT-4o mini (94.2% vs 71.2%, p < 0.001). For predicting OA severity, GPT-4o outperformed GPT-4o mini across all prompt strategies (best accuracy: 98.1% vs 68.7%). Surgeons found it easier to extract information and gave more preference to LLM-generated reports over the original reports (both p < 0.001) while spending less time on each report (51.27 ± 9.41 vs 87.42 ± 20.26 s, p < 0.001).</p><p><strong>Conclusion: </strong>GPT-4o generated expert multi-feature, WORMS-based reports from original free-text knee MRI reports. GPT-4o with COT achieved high accuracy in categorizing OA severity. Surgeons reported greater preference and higher efficiency when using LLM-generated reports.</p><p><strong>Critical relevance statement: </strong>The perfect performance of generating WORMS-based reports and the high efficiency and ease of use suggest that integrating LLMs into clinical workflows could greatly enhance productivity and alleviate the documentation burden faced by clinicians in knee OA.</p><p><strong>Key points: </strong>GPT-4o successfully generated WORMS-based knee MRI reports. GPT-4o with COT prompting achieved impressive accuracy in categorizing knee OA severity. Greater preference and higher efficiency were reported for LLM-generated reports.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"100"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based quantification of intratumoral heterogeneity for predicting distant metastasis in retroperitoneal sarcoma.","authors":"Jun Xu, Jian-Guo Miao, Chen-Xi Wang, Yu-Peng Zhu, Ke Liu, Si-Yuan Qin, Hai-Song Chen, Ning Lang","doi":"10.1186/s13244-025-01977-9","DOIUrl":"https://doi.org/10.1186/s13244-025-01977-9","url":null,"abstract":"<p><strong>Objectives: </strong>Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits.</p><p><strong>Results: </strong>The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05).</p><p><strong>Conclusions: </strong>The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies.</p><p><strong>Critical relevance statement: </strong>The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions.</p><p><strong>Key points: </strong>Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"99"},"PeriodicalIF":4.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome.","authors":"Kaiyue Zhi, Yanmei Wang, Lei Yan, Feng Hou, Jie Wu, Shuo Zhang, He Zhu, Lianzi Zhao, Ning Wang, Xia Zhao, Xianjun Li, Yicong Wang, Chengcheng Chen, Nan Wang, Yuchao Xu, Guangjie Yang, Pei Nie","doi":"10.1186/s13244-025-01972-0","DOIUrl":"https://doi.org/10.1186/s13244-025-01972-0","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate an interpretable CT-based vision transformer (ViT) model for preoperative prediction of the stage, size, grade, and necrosis (SSIGN) and outcome in clear cell renal cell carcinoma (ccRCC) patients.</p><p><strong>Methods: </strong>Eight hundred forty-five ccRCC patients from multiple centers were retrospectively enrolled. For each patient, 768 ViT features were extracted in the cortical medullary phase (CMP) and renal parenchymal phase (RPP) images, respectively. The CMP ViT model (CVM), RPP ViT model (RVM), and CMP-RPP combined ViT model (CRVM) were constructed to predict the SSIGN in ccRCC patients. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model. Decision curve analysis (DCA) was used to evaluate the net clinical benefit. The endpoint was the progression-free survival (PFS). Kaplan-Meier survival analysis was used to assess the association between model-predicted SSIGN and PFS. The SHAP approach was applied to determine the prediction process of the CRVM.</p><p><strong>Results: </strong>The CVM, RVM, and CRVM demonstrated good performance in predicting SSIGN, with a high AUC of 0.859, 0.883, and 0.895, respectively, in the test cohort. DCA demonstrated the CRVM performed best in clinical net benefit. In predicting PFS, CRVM achieved a higher Harrell's concordance index (C-index, 0.840) than the CVM (0.719) and RVM (0.773) in the test cohort. The SHAP helped us understand the impact of ViT features on CRVM's SSIGN prediction from a global and individual perspective.</p><p><strong>Conclusion: </strong>The interpretable CT-based CRVM may serve as a non-invasive biomarker in predicting the SSIGN and outcome of ccRCC.</p><p><strong>Critical relevance statement: </strong>Our findings outline the potential of an interpretable CT-based ViT biomarker for predicting the SSIGN score and outcome of ccRCC, which might facilitate patient counseling and assist clinicians in therapy decision-making for individual cases.</p><p><strong>Key points: </strong>Current first-line imaging lacks preoperative prediction of the SSIGN score for ccRCC patients. The ViT model could predict the SSIGN score and outcome of ccRCC patients. This study can facilitate the development of personalized treatment for ccRCC patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"98"},"PeriodicalIF":4.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute arterial mesenteric ischaemia: comparison of partial and complete occlusion of the superior mesenteric artery.","authors":"Raphael Dufay, Lorenzo Garzelli, Iannis Ben Abdallah, Arnaud Tual, Dominique Cazals-Hatem, Olivier Corcos, Valérie Vilgrain, Emmanuel Weiss, Alexandre Nuzzo, Maxime Ronot","doi":"10.1186/s13244-025-01986-8","DOIUrl":"https://doi.org/10.1186/s13244-025-01986-8","url":null,"abstract":"<p><strong>Objectives: </strong>To describe the characteristics and outcomes of patients with an incomplete occlusion of the superior mesenteric artery (SMA) (persistence of contrast-enhanced vessel lumen) and compare them to those with a complete occlusion of the SMA (complete interruption of the contrast-enhanced vessel lumen) in arterial acute mesenteric ischaemia (AMI).</p><p><strong>Material and methods: </strong>Retrospective study of arterial AMI patients (2006-2022). Demographics, laboratory tests, clinical characteristics, CT, treatments and outcomes were compared between patients with complete or incomplete SMA obstruction after adjusting for aetiology (embolic or atherosclerotic). The primary outcome was 30-day mortality, and the secondary outcome was 6-month gastrointestinal disability-free survival (no short bowel syndrome or parenteral nutritional support or permanent stoma).</p><p><strong>Results: </strong>151 patients (65 women, mean age 69) were included, 62 (41%) with incomplete and 89 (59%) with occlusive SMA occlusion. After adjusting for aetiology, chronic kidney failure (p = 0.03) and normal bowel enhancement on CT (p < 0.01) were associated with incomplete SMA occlusion. Patients with incomplete SMA occlusion were more frequently treated by endovascular revascularisation (p < 0.01) and stenting (p < 0.01), while patients with complete SMA occlusion were treated by open revascularisation. The 30-day mortality rate was 13% with no difference between incomplete (11%) and complete SMA occlusion (15%; p = 0.89). Nevertheless, complete SMA occlusion patients had a lower 6-month gastrointestinal disability-free survival rate (p = 0.01), more transmural necrosis (p < 0.01) and a higher risk of gastrointestinal disability (p = 0.02).</p><p><strong>Conclusion: </strong>Incomplete SMA occlusion can cause AMI with a similar 30-day mortality rate to completely occlusive forms. However, it is associated with poorer gastrointestinal outcomes, regardless of aetiology.</p><p><strong>Critical relevance statement: </strong>Acute arterial mesenteric ischaemia caused by incomplete occlusion of the superior mesenteric artery demonstrates similar 30-day mortality to complete occlusion but distinctively better gastrointestinal outcomes, emphasising nuanced imaging evaluation for targeted management strategies in these patients.</p><p><strong>Key points: </strong>Occlusive acute mesenteric ischaemia can be caused by incomplete superior mesenteric artery (SMA) occlusion. Acute mesenteric ischaemia caused by incomplete SMA occlusion has a similar 30-day mortality rate to complete SMA occlusion. A complete occlusion of the SMA is associated with poorer gastrointestinal outcomes.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"97"},"PeriodicalIF":4.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12062469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features.","authors":"Huan-Zhong Su, Zhi-Yong Li, Long-Cheng Hong, Yu-Hui Wu, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang","doi":"10.1186/s13244-025-01974-y","DOIUrl":"https://doi.org/10.1186/s13244-025-01974-y","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate machine learning (ML) models for diagnosing salivary gland adenoid cystic carcinoma (ACC) in the salivary glands based on clinical and ultrasound features.</p><p><strong>Methods: </strong>A total of 365 patients with ACC or non-ACC of the salivary glands treated at two centers were enrolled in training cohort, internal and external validation cohorts. Synthetic minority oversampling technique was used to address the class imbalance. The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were subsequently utilized to construct predictive models employing five ML algorithms. The performance of the models was evaluated across a comprehensive array of learning metrics, prominently the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Through LASSO regression analysis, six key features-sex, pain symptoms, number, cystic areas, rat tail sign, and polar vessel-were identified and subsequently utilized to develop five ML models. Among these models, the support vector machine (SVM) model demonstrated superior performance, achieving the highest AUCs of 0.899 and 0.913, accuracy of 90.54% and 91.53%, and F1 scores of 0.774 and 0.783 in both the internal and external validation cohorts, respectively. Decision curve analysis further revealed that the SVM model offered enhanced clinical utility compared to the other models.</p><p><strong>Conclusions: </strong>The ML model based on clinical and US features provide an accurate and noninvasive method for distinguishing ACC from non-ACC.</p><p><strong>Critical relevance statement: </strong>This machine learning model, constructed based on clinical and ultrasound characteristics, serves as a valuable tool for the identification of salivary gland adenoid cystic carcinoma.</p><p><strong>Key points: </strong>Rat tail sign and polar vessel on US predict adenoid cystic carcinoma (ACC). Machine learning models based on clinical and US features can identify ACC. The support vector machine model performed robustly and accurately.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"96"},"PeriodicalIF":4.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT of acute abdomen in the elderly.","authors":"Juliette Coutureau, Ingrid Millet, Patrice Taourel","doi":"10.1186/s13244-025-01955-1","DOIUrl":"https://doi.org/10.1186/s13244-025-01955-1","url":null,"abstract":"<p><p>Abdominal disorders represent 10 to 15% of all Emergency Department visits in elderly patients. This educational review focuses on acute abdomen pathologies specific to the elderly and on their imaging patterns and proposes a strategy for performing CT scans in this population. Bowel obstruction is the most common cause of emergency surgery in the elderly with a higher proportion of colonic obstructions, in particular obstructive colorectal cancer and sigmoid volvulus. Concerning abdominal inflammatory processes, such as cholecystitis, appendicitis, and diverticulitis, gangrenous cholecystitis and complicated appendicitis are relatively frequently encountered due to delayed diagnoses. Bowel ischemia, which includes acute mesenteric ischemia (AMI) and ischemic colitis (IC), is also much more common after the age of 80. Although ischemic colitis is mainly related to cardiovascular risk factors, it can also result from a persistent distension above a colonic cancer or from fecal impaction. Finally, extra-abdominal pathologies responsible for acute abdominal pain, such as inferior myocardial infarction, should not be overlooked. In clinical practice, when possible thanks to sufficient and appropriate radiological resources, we recommend a scan without injection of contrast and an injection depending on the results of the unenhanced scan, decided by the radiologist present at the CT scan room during the examination. CRITICAL RELEVANCE STATEMENT: CT is critical in the diagnosis and management of patients over 75 years old with an acute abdomen, given the difficulty of clinico-biological diagnosis, the frequency of complicated forms, and the morbidity induced by delayed diagnosis. KEY POINTS: The most common site and cause of bowel obstruction in the elderly is large bowel obstruction due to colon cancer. Discrepancy between a poor clinical examination and complicated forms on imaging, particularly for inflammation and infections, is responsible for late diagnosis and increased morbidity. Ischemia, including of the small bowel, colon, and gallbladder are common cause of acute abdomen in elderly. In patients with upper quadrant pain, consider extra-abdominal causes such as pneumonia or myocardial infarction.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"95"},"PeriodicalIF":4.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143982243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological approach to metatarsalgia in current practice: an educational review.","authors":"Océane Palka, Raphaël Guillin, Romain Lecigne, Damien Combes","doi":"10.1186/s13244-025-01945-3","DOIUrl":"https://doi.org/10.1186/s13244-025-01945-3","url":null,"abstract":"<p><p>Metatarsalgia, characterized by forefoot pain, is frequent and is primarily due to foot static disorders. Initial evaluation with weight-bearing radiographs is essential, allowing precise analysis of the architecture of the foot. Ultrasound is useful for soft tissue and tendon examination and provides the best clinical correlation. Computed Tomography provides detailed bone assessment and is helpful for pre-operative planning. Magnetic Resonance Imaging is the gold standard modality, offering superior soft tissue contrast. The common causes of metatarsalgia include hallux pathologies (hallux valgus, hallux rigidus, and sesamoid issues), bursitis (intermetatarsal and subcapitellar), Morton's neuroma, second ray syndrome, stress fractures, and systemic pathologies affecting the foot. Combining clinical and imaging data is crucial for accurate diagnosis and effective management of metatarsalgia. Post-traumatic causes of metatarsalgia are beyond the scope of this article and will not be described. CRITICAL RELEVANCE STATEMENT: Metatarsalgia, the pain of the forefoot, necessitates accurate imaging for diagnosis and management. This review critically assesses imaging techniques and diagnostic approaches, aiming to enhance radiological practice and support effective therapeutic decision-making. KEY POINTS: Metatarsalgia commonly results from foot static disorders, requiring weight-bearing radiographs for assessment. MRI is often the gold standard examination, but ultrasound is complementary, allowing for a radioclinical approach with dynamic examinations. The radiologist is crucial in diagnosing metatarsalgia, providing essential imaging, and guiding treatment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"94"},"PeriodicalIF":4.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12041408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renal ectopic fat deposition and hemodynamics in type 2 diabetes mellitus assessment with magnetic resonance imaging.","authors":"Jian Liu, Hengzhi Chen, Chong Tian, Liwei Fu, Lisha Nie, Rongpin Wang, Xianchun Zeng","doi":"10.1186/s13244-025-01971-1","DOIUrl":"https://doi.org/10.1186/s13244-025-01971-1","url":null,"abstract":"<p><strong>Objectives: </strong>To assess renal perfusion and ectopic fat deposition in patients with type 2 diabetes mellitus (T2DM), and to evaluate the effects of ectopic fat deposition on renal hemodynamics.</p><p><strong>Methods: </strong>All participants underwent quantitative magnetic resonance imaging (MRI) to measure the cortical and medullary renal blood flow (RBF) and proton density fat fraction (PDFF). Patients with T2DM were classified into three groups according to the estimated glomerular filtration rate (mL/min/1.73 m²). One-way analysis of variance was used to assess differences among groups. Pearson's correlation coefficient was used to analyze correlations. Additionally, a receiver operating characteristic (ROC) curve was constructed to assess diagnostic performance.</p><p><strong>Results: </strong>Renal PDFF values of the renal cortex and medulla, as well as perirenal fat thickness, were significantly different among the four groups: healthy control < T2DM < diabetic kidney disease (DKD) I-II < DKD III-IV. Additionally, significant differences in cortical and medullary RBF values were observed among the four groups: healthy control > T2DM > DKD I-II > DKD III-IV. A significant negative correlation was observed between renal PDFF and RBF values. Medullary RBF values demonstrated the best performance in discriminating T2DM from DKD with the largest area under the ROC curve (AUC) of 0.971. The cortical PDFF achieved the largest AUC (0.961) for distinguishing DKD I-II from DKD III-IV.</p><p><strong>Conclusions: </strong>Quantitative MRI effectively evaluates renal perfusion and ectopic fat deposition in T2DM patients, aiding in assessing kidney function and disease progression. Additionally, renal ectopic fat deposition may be an important risk factor for renal hemodynamic injury.</p><p><strong>Critical relevance statement: </strong>Quantitative MRI could serve as a radiation-free imaging modality for assessing renal perfusion and ectopic fat deposition, which may be an important risk factor for DKD progression.</p><p><strong>Key points: </strong>Quantitative MRI can be used to assess kidney function and monitor disease progression in patients with T2DM. In patients with T2DM, decreased renal perfusion, increased renal ectopic fat deposition, and kidney damage were significantly correlated. Renal ectopic fat deposition may be an important risk factor for renal hemodynamic injury.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"93"},"PeriodicalIF":4.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}