Insights into Imaging最新文献

{"title":"A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study.","authors":"Liwen Zhu, Ben Zhao, Tianyi Xia, Di Chang, Cong Xia, Mengqiu Liu, Ridong Li, Buyue Cao, Yue Qiu, Yaoyao Yu, Shuwei Zhou, Huayu Chen, Wu Cai, Zhimin Ding, Chunqiang Lu, Tianyu Tang, Yang Song, Yuancheng Wang, Jing Ye, Ying Liu, Shenghong Ju","doi":"10.1186/s13244-025-02025-2","DOIUrl":"10.1186/s13244-025-02025-2","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management.</p><p><strong>Methods: </strong>Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (n = 192), validation (n = 82), testing (n = 100), and clinical utilization (n = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan-Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model.</p><p><strong>Results: </strong>A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all p < 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (p < 0.001). Kaplan-Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all p < 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (p = 0.002).</p><p><strong>Conclusion: </strong>The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC.</p><p><strong>Critical relevance statement: </strong>This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection.</p><p><strong>Key points: </strong>Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC). The radiomics model can accurately predict lymph node status and stratify patients' prognosis. This radiomics model enhances the clinical management of PDAC.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"141"},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511884","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":"A multi-view CNN model to predict resolving of new lung nodules on follow-up low-dose chest CT.","authors":"Jingxuan Wang, Xiaowen Zhang, Wei Tang, Marcel van Tuinen, Rozemarijn Vliegenthart, Peter van Ooijen","doi":"10.1186/s13244-025-02000-x","DOIUrl":"10.1186/s13244-025-02000-x","url":null,"abstract":"<p><strong>Objective: </strong>New, intermediate-sized nodules in lung cancer screening undergo follow-up CT, but some of these will resolve. We evaluated the performance of a multi-view convolutional neural network (CNN) in distinguishing resolving and non-resolving new, intermediate-sized lung nodules.</p><p><strong>Materials and methods: </strong>This retrospective study utilized data on 344 intermediate-sized nodules (50-500 mm³) in 250 participants from the NELSON (Dutch-Belgian Randomized Lung Cancer Screening) trial. We implemented four-fold cross-validation for model training and testing. A multi-view CNN model was developed by combining three two-dimensional (2D) CNN models and one three-dimensional (3D) CNN model. We used 2D, 2.5D, and 3D models for comparison. The models' performance was evaluated using sensitivity, specificity, and area under the ROC curve (AUC). Specificity, indicating what percentage of non-resolving nodules requiring follow-up can be correctly predicted, was maximized.</p><p><strong>Results: </strong>Among all nodules, 18.3% (63) were resolving. The multi-view CNN model achieved an AUC of 0.81, with a mean sensitivity of 0.63 (SD, 0.15) and a mean specificity of 0.93 (SD, 0.02). The model significantly improved performance compared to 2D, 2.5D, or 3D models (p < 0.05). Under the premise of specificity greater than 90% (meaning < 10% of non-resolving nodules are incorrectly identified as resolving), follow-up CT in 14% of individuals could be prevented.</p><p><strong>Conclusion: </strong>The multi-view CNN model achieved high specificity in discriminating new intermediate nodules that would need follow-up CT by identifying non-resolving nodules. After further validation and optimization, this model may assist with decision-making when new intermediate nodules are found in lung cancer screening.</p><p><strong>Critical relevance statement: </strong>The multi-view CNN-based model has the potential to reduce unnecessary follow-up scans when new nodules are detected, aiding radiologists in making earlier, more informed decisions.</p><p><strong>Key points: </strong>Predicting the resolution of new intermediate lung nodules in lung cancer screening CT is a challenge. Our multi-view CNN model showed an AUC of 0.81, a specificity of 0.93, and a sensitivity of 0.63 at the nodule level. The multi-view model demonstrated a significant improvement in AUC compared to the three 2D models, one 2.5D model, and one 3D model.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"138"},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511883","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":"A two-step automatic identification of contrast phases for abdominal CT images based on residual networks.","authors":"Qianhe Liu, Jiahui Jiang, Kewei Wu, Yan Zhang, Nan Sun, Jiawen Luo, Te Ba, Aiqing Lv, Chuane Liu, Yiyu Yin, Zhenghan Yang, Hui Xu","doi":"10.1186/s13244-025-01995-7","DOIUrl":"10.1186/s13244-025-01995-7","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a deep learning model based on Residual Networks (ResNet) for the automated and accurate identification of contrast phases in abdominal CT images.</p><p><strong>Methods: </strong>A dataset of 1175 abdominal contrast-enhanced CT scans was retrospectively collected for the model development, and another independent dataset of 215 scans from five hospitals was collected for external testing. Each contrast phase was independently annotated by two radiologists. A ResNet-based model was developed to automatically classify phases into the early arterial phase (EAP) or late arterial phase (LAP), portal venous phase (PVP), and delayed phase (DP). Strategy A identified EAP or LAP, PVP, and DP in one step. Strategy B used a two-step approach: first classifying images as arterial phase (AP), PVP, and DP, then further classifying AP images into EAP or LAP. Model performance and strategy comparison were evaluated.</p><p><strong>Results: </strong>In the internal test set, the overall accuracy of the two-step strategy was 98.3% (283/288; p < 0.001), significantly higher than that of the one-step strategy (91.7%, 264/288; p < 0.001). In the external test set, the two-step model achieved an overall accuracy of 99.1% (639/645), with sensitivities of 95.1% (EAP), 99.4% (LAP), 99.5% (PVP), and 99.5% (DP).</p><p><strong>Conclusion: </strong>The proposed two-step ResNet-based model provides highly accurate and robust identification of contrast phases in abdominal CT images, outperforming the conventional one-step strategy.</p><p><strong>Critical relevance statement: </strong>Automated and accurate identification of contrast phases in abdominal CT images provides a robust tool for improving image quality control and establishes a strong foundation for AI-driven applications, particularly those leveraging contrast-enhanced abdominal imaging data.</p><p><strong>Key points: </strong>Accurate identification of contrast phases is crucial in abdominal CT imaging. The two-step ResNet-based model achieved superior accuracy across internal and external datasets. Automated phase classification strengthens imaging quality control and supports precision AI applications.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"139"},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511885","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":"Washed up: the end of an era for adrenal incidentaloma CT.","authors":"James H Seow, Damien L Stella, Christopher J Welman, Arjuna J Somasundaram, Jan F Gerstenmaier","doi":"10.1186/s13244-025-02015-4","DOIUrl":"10.1186/s13244-025-02015-4","url":null,"abstract":"<p><p>For over 20 years, the two key tenets of adrenal incidentaloma (AI) evaluation have been the upper threshold of 10 Hounsfield units (HU) on noncontrast CT (ncCT) to delineate benignity, and the utilisation of adrenal washout CT (AWCT) to evaluate those above this cutoff. In light of growing recent evidence that challenges these two traditional principles, as well as re-evaluation of the data that led to their acceptance, we conclude that neither of these mainstays of adrenal CT remains relevant in modern AI diagnostic workup. With an appropriate definition of an incidentaloma and endocrine assessment for the majority of adrenal lesions, our analysis establishes that the use of AWCT should be ceased in the assessment of AIs, and that a 20 HU attenuation threshold for lesions < 4 cm should replace the traditional 10 HU threshold to exclude malignancy in this patient population. We therefore propose new recommendations for the management of AIs based primarily on CT attenuation and lesion size on ncCT. CRITICAL RELEVANCE STATEMENT: Increasing the CT attenuation threshold to 20 HU for lesions < 4 cm and eliminating washout CT for true adrenal incidentalomas, together with recommendations for endocrine assessment, will significantly decrease the over-investigation of overwhelmingly benign adrenal lesions, whilst confidently excluding malignancy. KEY POINTS: True incidentalomas exclude current or prior extra-adrenal malignancy and clinically suspected adrenal disease. Adrenal washout CT was never proven in the malignancy-sparse true incidentaloma population. Hormonal correlation in parallel with < 20 HU and < 4  cm thresholds of homogeneous lesions on noncontrast CT excludes malignancy.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"136"},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511890","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":"A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumors.","authors":"WenJie Xie, Zhen Zhang, Zhao Sun, XiaoChen Wan, JieHan Li, JianWu Jiang, Qi Liu, Ge Yang, Yang Fu","doi":"10.1186/s13244-025-02011-8","DOIUrl":"10.1186/s13244-025-02011-8","url":null,"abstract":"<p><strong>Objectives: </strong>Post-surgical prediction of recurrence or metastasis for primary gastrointestinal stromal tumors (GISTs) remains challenging. We aim to develop individualized clinical follow-up strategies for primary GIST patients, such as shortening follow-up time or extending drug administration based on the clinical deep learning radiomics model (CDLRM).</p><p><strong>Methods: </strong>The clinical information on primary GISTs was collected from two independent centers. Postoperative recurrence or metastasis in GIST patients was defined as the endpoint of the study. A total of nine machine learning models were established based on the selected features. The performance of the models was assessed by calculating the area under the curve (AUC). The CDLRM with the best predictive performance was constructed. Decision curve analysis (DCA) and calibration curves were analyzed separately. Ultimately, our model was applied to the high-potential malignant group vs the low-malignant-potential group. The optimal clinical application scenarios of the model were further explored by comparing the DCA performance of the two subgroups.</p><p><strong>Results: </strong>A total of 526 patients, 260 men and 266 women, with a mean age of 62 years, were enrolled in the study. CDLRM performed excellently with AUC values of 0.999, 0.963, and 0.995 for the training, external validation, and aggregated sets, respectively. The calibration curve indicated that CDLRM was in good agreement between predicted and observed probabilities in the validation cohort. The results of DCA's performance in different subgroups show that it was more clinically valuable in populations with high malignant potential.</p><p><strong>Conclusion: </strong>CDLRM could help the development of personalized treatment and improved follow-up of patients with a high probability of recurrence or metastasis in the future.</p><p><strong>Critical relevance statement: </strong>This model utilizes imaging features extracted from CT scans (including radiomic features and deep features) and clinical data to accurately predict postoperative recurrence and metastasis in patients with primary GISTs, which has a certain auxiliary role in clinical decision-making.</p><p><strong>Key points: </strong>We developed and validated a model to predict recurrence or metastasis in patients taking oral imatinib after GIST. We demonstrate that CT image features were associated with recurrence or metastases. The model had good predictive performance and clinical benefit.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"135"},"PeriodicalIF":4.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505605","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":"Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases.","authors":"Zhenyu Cao, Gang Xu, Yuan Gao, Jianying Xu, Fengjuan Tian, Hengfeng Shi, Dengfa Yang, Zongyu Xie, Jian Wang","doi":"10.1186/s13244-025-02020-7","DOIUrl":"10.1186/s13244-025-02020-7","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a high-performance machine learning model for predicting and interpreting features of pulmonary diseases.</p><p><strong>Patients and methods: </strong>This retrospective study analyzed clinical and imaging data from patients with non-small cell lung cancer (NSCLC), granulomatous inflammation, and benign tumors, collected across multiple centers from January 2015 to October 2023. Data from two hospitals in Anhui Province were split into a development set (n = 1696) and a test set (n = 424) in an 8:2 ratio, with an external validation set (n = 909) from Zhejiang Province. Features with p < 0.05 from univariate analyses were selected using the Boruta algorithm for input into Random Forest (RF) and XGBoost models. Model efficacy was assessed using receiver operating characteristic (ROC) analysis.</p><p><strong>Results: </strong>A total of 3030 patients were included: 2269 with NSCLC, 529 with granulomatous inflammation, and 232 with benign tumors. The Obuchowski indices for RF and XGBoost in the test set were 0.7193 (95% CI: 0.6567-0.7812) and 0.8282 (95% CI: 0.7883-0.8650), respectively. In the external validation set, indices were 0.7932 (95% CI: 0.7572-0.8250) for RF and 0.8074 (95% CI: 0.7740-0.8387) for XGBoost. XGBoost achieved better accuracy in both the test (0.81) and external validation (0.79) sets. Calibration Curve and Decision Curve Analysis (DCA) showed XGBoost offered higher net clinical benefit.</p><p><strong>Conclusion: </strong>The XGBoost model outperforms RF in the three-class classification of lung diseases.</p><p><strong>Critical relevance statement: </strong>XGBoost surpasses Random Forest in accurately classifying NSCLC, granulomatous inflammation, and benign tumors, offering superior clinical utility via multicenter data.</p><p><strong>Key points: </strong>Lung cancer classification model has broad clinical applicability. XGBoost outperforms random forests using CT imaging data. XGBoost model can be deployed on a website for clinicians.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"133"},"PeriodicalIF":4.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202249/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505607","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":"Feedback in radiology: Essential tool for improving user experience and providing value-based care.","authors":"Andrea Rockall, Jacob J Visser, Cristina Garcia-Villar, Naama Lev-Cohain, Patrick Omoumi, Marie-Pierre Revel, Ruth Mary Strudwick","doi":"10.1186/s13244-025-02002-9","DOIUrl":"10.1186/s13244-025-02002-9","url":null,"abstract":"<p><p>Measuring the value that radiology brings to patient care can be challenging. A positive patient experience is consistently associated with patient safety, clinical effectiveness, and outcome measures and is therefore a tool for measuring value-based care. Monitoring the experience of users of radiology services is an indispensable component of quality improvement programmes for radiology departments. The integration of comprehensive feedback mechanisms brings numerous benefits, including enhanced care, strengthened trust, and greater engagement with our stakeholders and service users. Feedback should be collected from a variety of stakeholders through a 360-degree approach, combining both systematically performed structured methods, such as formal surveys, and unstructured methods, such as informal and opportunistic information gathering during multidisciplinary rounds. To maximise the impact of feedback, it should be frequent and diverse, ensuring that all perspectives are considered. Leaders in radiology must prioritise embedding a culture of feedback within their institutions, recognising its crucial role in continuous improvement. It is essential to ensure that our departments consistently provide value to our most important stakeholders-the patients-but also to our referrers and trainees. In this article, we consider methods for collecting feedback and provide some of the key findings from the literature. By fostering an environment that values and acts upon feedback, we can achieve significant advancements in patient care and overall service quality in radiology. CRITICAL RELEVANCE STATEMENT: Regular feedback from patients, peers, radiographers, referrers, trainees and other users of imaging services is an essential tool for continuous quality improvement, patient safety and value-based care, enhancing trust and greater engagement with our stakeholders and service users. KEY POINTS: Feedback from patients and referrers, radiographers and radiology trainees, helps radiology departments to identify weaknesses and strengths, and should be fully incorporated into daily practice. Many methods are available for collecting user and stakeholder experience, and these should be implemented as a priority. Acting on stakeholder feedback can improve patient safety and patient experience ratings, leading to a culture of continuous improvement in value-based care.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"132"},"PeriodicalIF":4.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144496141","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":"Dental magnetic resonance imaging for bone loss assessment and disease activity classification in severe periodontitis.","authors":"Arne Lauer, Luisa Schulte, Artid Skenderi, Nouha Tekiki, Alexander Juerchott, Meysam Sohani, Maurice Ruetters, Franz Sebastian Schwindling, Peter Rammelsberg, Mathias Nittka, Sabine Heiland, Martin Bendszus, Tim Hilgenfeld","doi":"10.1186/s13244-025-02004-7","DOIUrl":"10.1186/s13244-025-02004-7","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the reliability and accuracy of dental MRI (dMRI) for volumetric infrabony and furcation bone loss compared to cone-beam computed tomography (CBCT) and to correlate to clinical signs of inflammation in patients with severe periodontitis.</p><p><strong>Methods: </strong>In this cross-sectional study nineteen patients with severe periodontitis underwent standardized clinical examination as well as pre-treatment CBCT and 3T-dMRI. Bone lesion volumetry was performed in CBCT, contrast-enhanced-T1-weighting (T1W + C) and T2-weighting (T2W) dMRI. Lesions whose T2W signal significantly exceeded T1W/CBCT margins (indicating excessive edema) were classified as T2W-mismatch. Volumetric data were compared to clinical findings.</p><p><strong>Results: </strong>Ten female and nine male patients with 253 bony lesions were examined. Reliability for bone lesions was highest in CBCT (ICC [95% CI] T1W + C/T2W/CBCT: 0.78 [0.74-0.83]/0.82 [0.77-0.85]/0.87 [0.94-0.89]). Overall, T1W + C and T2W dMRI strongly correlated with CBCT (r_s = 0.86 [95% CI: 0.82-0.89], p < 0.001 and r_s = 0.91 [95% CI: 0.88-0.93], p < 0.001 respectively) but volume was significantly overestimated by dMRI (median percentage error of T1W + C-T2W: 19-55%). A T2W-mismatch was found in 44.1% and correlated with bleeding (85.8% vs. 70.9%, p = 0.005), giving 47.5% sensitivity and 71.2% specificity.</p><p><strong>Conclusions: </strong>While dMRI offers good reliability, T2W- and to a lesser extent T1W + C imaging overestimate infrabony and interradicular periodontal bone lesion volumetry compared to CBCT. While this could increase the risk of overtreatment, dMRI detects periodontal inflammation beyond areas of bone loss, and T2W-mismatch is closely related but not identical to signs of active inflammation in clinical examination. This may provide additional diagnostic information and could serve as a supplemental tool for higher-risk patients.</p><p><strong>Critical relevance statement: </strong>Dental MRI excels in detecting inflammation beyond bone loss, identifying high-risk tissue. This study assesses reliability in evaluating periodontitis-related bone loss, highlighting its tendency to overestimate lesion volume. A novel \"mismatch lesion pattern\" was observed, potentially linked to disease activity.</p><p><strong>Key points: </strong>Dental MRI (dMRI) reliably assesses bone loss in periodontitis but overestimates volume vs. cone-beam computed tomography (CBCT). dMRI detects excess bone marrow edema, indicating inflammation beyond visible bone loss. dMRI could aid periodontal diagnosis and guide targeted therapeutic interventions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"134"},"PeriodicalIF":4.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505606","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":"Association between ultrasound-based biliary and parenchymal intrahepatic mass-forming cholangiocarcinoma subtypes and clinicopathological features and survival.","authors":"Cong-Jian Wen, Hui Liu, Li-Ping Sun, Chong-Ke Zhao, Hao-Hao Yin, Li-Fan Wang, Ming-Rui Zhu, Yi-Kang Sun, Ya-Qin Zhang, Zi-Tong Chen, Xi Wang, Han-Sheng Xia, Hong Han, Hui-Xiong Xu, Bo-Yang Zhou","doi":"10.1186/s13244-025-02019-0","DOIUrl":"10.1186/s13244-025-02019-0","url":null,"abstract":"<p><strong>Objective: </strong>Mass-forming intrahepatic cholangiocarcinomas (MF-ICCs) can be classified into ductal and parenchymal types using magnetic resonance imaging (MRI). We aimed to subclassify MF-ICC into biliary and parenchymal types based on ultrasound (US) findings and to investigate the differences in their contrast-enhanced ultrasound (CEUS) patterns, clinicopathologic features, and prognosis.</p><p><strong>Methods: </strong>In this study, 141 patients who underwent US with pathologically proven MF-ICC from two hospitals were retrospectively enrolled. MF-ICCs were divided into biliary (bMF-ICCs) and parenchymal MF-ICC (pMF-ICCs) based on the signs of bile duct dilation in US images. Clinicopathological, imaging, and short-term survival data were collected from medical records and compared.</p><p><strong>Results: </strong>Among 141 patients (61.96 ± 10.15 years, 83 men), bMF-ICCs (33/141, 23.4%) showed significantly more CEA ≥ 5 µg/L (42.4% vs 20.2%, p = 0.01), microvascular invasion (54.5% vs 10.2%, p < 0.001), lymph node metastasis (48.5% vs 5.6%, p < 0.001), bile duct invasion (48.5% vs 5.6%, p < 0.001), and high Ki-67 expression (63.6% vs 38.9%, p = 0.01) than pMF-ICCs. Pathologically, bMF-ICCs were more inclined toward the large duct type (78.1% vs 11.7%, p < 0.001). In addition, the bMF-ICCs were usually located in the left lobe of the liver (63.6% vs 41.7%, p = 0.03). pMF-ICCs showed better overall survival than bMF-ICCs (p = 0.04).</p><p><strong>Conclusions: </strong>Subclassification of MF-ICCs into biliary and parenchymal types based on US is useful for discriminating clinicopathological characteristics.</p><p><strong>Critical relevance statement: </strong>The subclassification of mass-forming intrahepatic cholangiocarcinoma (MF-ICC) into biliary (bMF-ICC) and parenchymal (pMF-ICC) subtypes using ultrasound can provide clinicopathological and prognostic information before surgery.</p><p><strong>Key points: </strong>We subclassified mass-forming intrahepatic cholangiocarcinomas into biliary and parenchymal types using ultrasound. Biliary and parenchymal types have different clinicopathological features and postsurgical outcomes. Biliary type above and below 50 mm exhibits different unfavorable clinicopathological characteristics. Our classification has certain similarities with MRI classification in clinicopathological characteristics.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"130"},"PeriodicalIF":4.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144325594","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":"Pelvic nerve endometriosis: MRI features and key findings for surgical decision.","authors":"Justine Bourg, Edouard Ruaux, Pierre Adrien Bolze, Marie Gavrel, Mathilde Charlot, François Golfier, Isabelle Thomassin-Naggara, Pascal Rousset","doi":"10.1186/s13244-025-02005-6","DOIUrl":"10.1186/s13244-025-02005-6","url":null,"abstract":"<p><p>Endometriosis is a prevalent gynecological disorder in women of reproductive age. It is the leading cause of chronic pelvic pain. While the mechanisms underlying this pain remain elusive, rare cases of pelvic nerve involvement can result in severe, debilitating symptoms, adding complexity to the clinical landscape. Nerve involvement typically results from the direct extension of deep infiltrating endometriosis, though it may also occur in isolation. The nerves most commonly affected include the inferior hypogastric and lumbosacral plexuses, as well as the sciatic, pudendal, obturator, and femoral nerves. Early and accurate diagnosis is essential for the effective management of the pain and the prevention of irreversible nerve damage. Given the limitations of transvaginal ultrasonography in visualizing the lateral compartment, MRI is considered the gold standard for detecting and evaluating pelvic nerve involvement. Through the use of optimized protocols to enhance the visualization of nerves and their anatomical landmarks, radiologists play a key role in the identification of endometriotic lesions. A comprehensive and structured radiology report is essential for surgical planning, as nerve involvement often requires precise interventions to alleviate symptoms and restore quality of life. CRITICAL RELEVANCE STATEMENT: Accurate identification and a structured reporting of pelvic nerve endometriosis in the lateral compartment are pivotal to guide surgical decision-making and optimize patient outcomes. KEY POINTS: Pelvic nerve endometriosis is often overlooked, underestimated by clinicians, and underdiagnosed on imaging. Timely nerve involvement diagnosis prevents permanent damage in pelvic pain with neurological symptoms. Deep endometriosis in the lateral compartment may extend to the pelvic nerves. The inferior hypogastric plexus, sacral plexus, sciatic, and pudendal nerves are commonly affected. A dedicated MRI protocol with 3D T2-weighted sequence ensures accurate pelvic nerve assessment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"131"},"PeriodicalIF":4.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333067","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}