Academic Radiology最新文献

{"title":"Multimodal Carotid Ultrasound-based Artificial Intelligence Model for Predicting Acute Ischemic Stroke Risk in Patients with Type 2 Diabetes Mellitus.","authors":"Yaning Sun, Bojian Feng, Yanming Zhang, Di Ou, Dangin Pan, Saisai Wang, Fengqin Shen, Xinchen Li, Dong Xu","doi":"10.1016/j.acra.2026.09.036","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.036","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Patients with type 2 diabetes mellitus (T2DM) are at high risk of acute ischemic stroke (AIS), yet accurate risk stratification remains challenging. This study aimed to develop and validate a multimodal model based on carotid ultrasound for improved AIS risk prediction.</p><p><strong>Materials and methods: </strong>In this multicenter retrospective study, a total of 480 patients with T2DM who underwent carotid ultrasound were recruited from two centers, with one center (n=394) used for model development and the other (n = 86) for independent external testing. A dual-scale deep learning framework based on a Swin Transformer was developed to capture both local plaque features and surrounding vascular context. Deep learning features were integrated with radiomic and clinical features to construct a multimodal model. Model performance was evaluated using the area under the curve (AUC), along with calibration and decision curve analysis.</p><p><strong>Results: </strong>The multimodal model achieved AUCs of 0.952 (95% CI: 0.910-0.984) in the internal cohort and 0.939 (95% CI: 0.838-0.996) in the external cohort, showing improved performance compared to single-modality models. In the reader study, AI-assisted interpretation improved diagnostic performance across all readers, with AUC increases ranging from 0.760 to 0.857 and from 0.868 to 0.929 for senior readers, and from 0.450 to 0.700 and from 0.548 to 0.659 for junior readers. Generalized estimating equation analyses showed significant improvements in diagnostic correctness among both senior and junior readers, with P values of 0.00235 and 0.000343, respectively.</p><p><strong>Conclusion: </strong>A multimodal framework integrating carotid ultrasound with clinical, radiomic, and deep learning features enables improved AIS risk stratification in patients with T2DM.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric Complementarity: A Subtype-Specific Strategy via Visual Assessment of DCE-MRI and DWI for Evaluating NAT Efficacy in Breast Cancer.","authors":"Xiuyun Ge, Bin Zheng, Zhen Zhang, Jianle Liang, Wenjing Yuan, Jiarui Xie, Guanxun Cheng, Peng Sun, Rong Huang","doi":"10.1016/j.acra.2026.09.026","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.026","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), based on clinical visual assessment for predicting pathological complete response (pCR) and measuring residual tumor size after neoadjuvant therapy (NAT) across breast cancer subtypes.</p><p><strong>Materials and methods: </strong>This retrospective study included 248 patients. Two radiologists, blinded to pathological results, visually assessed DCE-MRI and DWI (including ADC maps) for radiological complete response (rCR) and measured residual tumor size. Diagnostic performance (vs. pCR) and size agreement (intraclass correlation coefficient, ICC) were compared between sequences and across three molecular subtypes: HR+/HER2-, HER2+, and triple-negative breast cancer (TNBC). Bonferroni correction was applied.</p><p><strong>Results: </strong>DCE-MRI showed higher specificity, positive predictive value (PPV), and accuracy, DWI showed higher sensitivity (all P < 0.05). Subtype analysis: HER2+ exhibited a trend toward symmetric complementarity (DWI-high NPV, DCE-MRI-high PPV; sensitivity difference not significant after correction, adjusted P = 0.066); HR+/HER2- showed DCE-MRI dominance (DWI very low PPV/specificity); TNBC showed no significant differences(n = 30, preliminary). Interobserver κ: 0.77 (DCE-MRI), 0.85 (DWI). Interobserver ICC for size:0.96 (both). DCE-MRI demonstrated superior size concordance with pathology (ICC: 0.91 vs. 0.51, P < 0.001).</p><p><strong>Conclusion: </strong>DCE-MRI and DWI exhibit subtype-dependent asymmetric complementarity. For HER2+, both are complementary (DWI high NPV, DCE-MRI high PPV). For HR+/HER2-, DCE-MRI is the primary anchor (DWI limited). For TNBC, findings are preliminary (larger studies needed). DCE-MRI is more reliable for size measurement. This framework provides a molecular subtype-specific visual interpretation guide for clinical practice.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Idea to Publication: A Step-by-Step Guide to Original Research in Radiology with AI-Integrated Workflow.","authors":"Isabela Hernandez, Daniel Souza, Cinthia Del Toro, Kristin Parker, Adriana Falcon Vazquez, Gary H Danton, Ty Subhawong, Francesco Alessandrino","doi":"10.1016/j.acra.2026.09.042","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.042","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Over the past decade, according to the National Resident Matching Program Charting Outcomes data, scholarly output of matched United States Medical Doctor radiology applicants has more than doubled, with applicants reporting an average of 12.0 published scholarly works in 2024 compared to 5.7 in 2014. Despite this shift, structured guidance on how to produce peer-reviewed scholarly work remains limited for radiology trainees, and most existing guides predate the widespread availability of artificial intelligence (AI) tools.</p><p><strong>Materials and methods: </strong>This guide provides a five-step framework, covering choosing a topic of interest, methodology development, presentation of results, manuscript writing, and revision and submission, with explicit integration of AI tools at each stage.</p><p><strong>Results: </strong>A framework for responsible AI use and disclosure is provided.</p><p><strong>Conclusion: </strong>Scientific writing is a teachable skill. AI tools, when used with transparency and integrity, lower the threshold for entry for radiology trainees and should be integrated into scientific writing education. This guide provides a reproducible, AI-integrated framework applicable to trainees at any stage of training and seeking to publish peer-reviewed scholarly works.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of an Interpretable Machine Learning Model Based on Gd-EOB-DTPA-enhanced MRI for Evaluating Small HCC (≤2 cm): A Multicenter Cohort Study.","authors":"Longshan Yang, Xiaojun Zeng, Zhelin Lv, Pengbo Wu, Haitao Wen, Gengyun Lin, Yuxin Li, Lina Huang, Shihan Chen, Yuying Zhang, Ming Gong, Jinyu Lin, Hongbo Liang, Weichao Hao, Haisu Tao, Jian Yang","doi":"10.1016/j.acra.2026.09.031","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.031","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aims to develop and externally validate an interpretable machine-learning model that integrates gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid magnetic resonance imaging (MRI) radiomics, qualitative MRI features, and clinical data to accurately distinguish small (≤2 cm) hepatocellular carcinomas from benign hepatic lesions.</p><p><strong>Materials and methods: </strong>This retrospective, multicenter cohort included 296 lesions (Center A: 209 [146 training, 63 internal test]; Centers B + C: 87 external validation). Three feature categories were extracted and integrated. A multistep feature selection and model tuning pipeline was performed exclusively within the training set using nested cross-validation to prevent data leakage. Seven machine learning (ML) algorithms were compared. In the external validation set, a reader study was performed to compare the diagnostic performance of the fusion model against that of radiologists. SHapley Additive exPlanations (SHAP) was employed to interpret the optimal model.</p><p><strong>Results: </strong>The fusion model combining three feature categories achieved the best diagnostic performance. In the internal test set, the fusion model yielded an area under the receiver operating characteristic curve (AUC) of 0.913, compared to 0.791 for the radiomics-only model. Its discriminative ability remained robust in an independent external validation cohort (AUC = 0.824), and remained stable after mitigating interscanner variability with ComBat harmonization (AUC = 0.806). The fusion model showed numerically higher performance than junior radiologists and performed comparably to senior radiologists. SHAP analysis identified hepatitis B virus status, alpha-fetoprotein, and specific hepatobiliary phase-derived radiomics features as key predictors.</p><p><strong>Conclusion: </strong>An interpretable ML fusion model demonstrates robust discriminative ability for evaluating small HCCs. Although its numerical advantages over the radiomics-only model and junior radiologists did not reach statistical significance because of the limited external sample size, this noninvasive tool holds significant potential as a reliable diagnostic adjunct with future dynamic threshold optimization.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leadership Development at All Career Levels: A Survey Of Academic Medical Centers.","authors":"Tara Catanzano, N Reed Dunnick, Alison Chetlen, Stephane Desouches, Ashish P Wasnik, Gloria Guzman, Sagar Bajpai, Atul Agarwal, Bahar Mansoori, Christina Sumner, David Mankoff, Tiffany Chan, Jessica Leschied, Jane Clayton, Priscilla Slanetz, Janine Katzen","doi":"10.1016/j.acra.2026.08.098","DOIUrl":"https://doi.org/10.1016/j.acra.2026.08.098","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>A Radiology Research Alliance taskforce was convened to assess the importance and impact of leadership development at each career stage with an intent of demonstrating the importance of leadership training irrespective of career stage. A survey-based tool was used to gather information on what types of leadership training academic radiologists were currently exposed to and what the desired level of training was at each career stage. The resulting information was used to provide a guide to readers on the need for formal leadership development as well as currently available resources and programs which can provide the needed training.</p><p><strong>Materials and methods: </strong>An internally validated survey tool was developed by the taskforce members with a specific focus on appraising the following critical dimensions of leadership: skills, mentors/sponsors/coaches, available roles at local, regional and national levels and potential or self-crafted roles. The survey was distributed via email invitation to members of the Radiology Research Alliance and the Alliance of Clinician Educators in Radiology through listservs. A separate invitation was sent to members of the radiology department of a taskforce member to increase participation rates.</p><p><strong>Results: </strong>An estimated 350 invitations for survey participation were issued with a response rate of 25.4% (89/350). Of the respondents, 86% (77/89) were practicing radiologists and 14% (12/89) were trainees. The majority were affiliated with academic institutions (89%; 79/89). A total of 37% (32/89) expressed strong interest in holding leadership roles, 31% (27/89) moderate interest and 10% (9/89) no interest. However, 81% (73/89) expressed interest in developing leadership skills as part of career growth. The most preferred skills were \"partnership and collaboration\" (69%; 61/89), \"management/providing feedback\" (64%; 57/89), \"conflict management/resolution\" (61%; 54/89), and \"negotiation skills\" (60%; 53/89). Patient engagement was valued less at 16% (14/85 responses). A total of 54% (47/87) had participated in external development courses, 48% (43 /89) in institutional courses, 32% ( 28 /89) in departmental courses and 25% (22/89) in external medical leadership courses.</p><p><strong>Conclusions: </strong>Regardless of career stage, leadership development and mentorship are essential for continued career growth. The survey results indicate that there is a gap at the institutional level for the provision of leadership development training which could be addressed to improve accessibility for all faculty to engage in leadership development training.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Prediction of Insufficient Response to Ursodeoxycholic Acid: A Multimodal Model Based on Radiomics and Clinical Indicators.","authors":"Yanan Sun, Yichen Gu, Zixian Wang, Shuhui Xie, Binbin Wu, Jing Wu","doi":"10.1016/j.acra.2026.09.023","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.023","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to integrate two-dimensional ultrasound (2D-US) radiomics and clinical indicators to develop a prediction model for early identification of primary biliary cholangitis (PBC) patients with insufficient response to ursodeoxycholic acid (UDCA) at 6 months.</p><p><strong>Materials and methods: </strong>136 PBC patients were enrolled. Least absolute shrinkage and selection operator regression was applied to select key features and construct a radiomics model. Six machine learning algorithms were compared using 5-fold nested cross-validation. Univariate and multivariate logistic regression were performed to identify independent clinical predictors and construct a clinical model. By combining the radiomics score and significant clinical factors using multivariate logistic regression analysis, a combined model was subsequently developed. Model performance was evaluated by AUC, calibration curves, and decision curve analysis.</p><p><strong>Results: </strong>The support vector machine (SVM) algorithm achieved the highest mean AUC (0.696 ± 0.028) and the lowest coefficient of variation (4.055%) in nested cross-validation. Alkaline phosphatase (ALP) and platelet count (PLT) were independent predictors of insufficient response to UDCA. The combined model achieved an AUC of 0.865 in the test set, higher than those of the clinical model (0.778) and the radiomics model (0.833). The response status at 6 months was highly consistent with that at 12 months (kappa = 0.809). The model showed generally consistent predictive performance across different UDCA response criteria and histological fibrosis stages.</p><p><strong>Conclusions: </strong>A noninvasive multimodal model based on pretreatment 2D-US radiomics features, ALP, and PLT can accurately predict the insufficient response to UDCA in PBC patients at an early stage.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision Transformer for Pretreatment Prediction of Pathologic Complete Response in Multi-center Breast DCE-MRI.","authors":"Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldstein","doi":"10.1016/j.acra.2026.09.024","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.024","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Pretreatment prediction of pathologic complete response (pCR) is clinically important for guiding neoadjuvant chemotherapy (NAC), but robust multi-center benchmarks with standardized evaluation protocols remain limited. We evaluated a Vision Transformer (ViT) approach for pCR prediction from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).</p><p><strong>Materials and methods: </strong>We used BreastDCEDL, a public multi-center dataset comprising 2070 patients from the I-SPY1, I-SPY2, and Duke cohorts with predefined splits. Pre-contrast, early post-contrast, and late post-contrast phases were mapped to RGB channels for transfer learning from ImageNet-pretrained backbones. The held-out test set (n=175) included 32 pCR+ in I-SPY2 (99), 8 in Duke (41), and 12 in I-SPY1 (35). For I-SPY2, ViT scores were combined with clinical variables (age, HR/HER2 status, tumor volume, genomic score, treatment arm) via a Random Forest classifier.</p><p><strong>Results: </strong>The ViT achieved AUC 0.72 (95% CI 0.64-0.80), sensitivity 0.27 (95% CI 0.15-0.40), and specificity 0.96 (95% CI 0.92-0.99). Performance varied across cohorts (AUC: I-SPY2 0.78, 95% CI 0.68-0.86, I-SPY1 0.68, 95% CI 0.46-0.89, Duke 0.54, 95% CI 0.27-0.80). In I-SPY2, clinical variables increased AUC to 0.85 overall (n=99), a difference that was not statistically significant (DeLong P=0.06).</p><p><strong>Conclusion: </strong>Cross-cohort variation highlights challenges in developing generalizable pretreatment response models under real-world heterogeneity. The high specificity supports potential utility for identifying non-responders, enabling earlier transition to alternative strategies. Code, data, and predefined splits are publicly available, establishing a reproducible benchmark.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-based Pressure-threshold Flow-rate Estimation for Contrast Injection via Upper-arm Central Venous Ports.","authors":"Yusuke Sato, Yasuhiro Fukushima, Soma Kumasaka, Tomokazu Takeuchi, Daisuke Ozaki, Yoshiyuki Sakai, Takayuki Suto, Yoshito Tsushima","doi":"10.1016/j.acra.2026.09.033","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.033","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To characterize the association between recorded maximum delivered injection rate and peak pressure during contrast-enhanced computed tomography (CT) through upper-arm central venous (CV) ports and estimate port- and viscosity-specific flow-rate thresholds corresponding to 13 kg/cm².</p><p><strong>Materials and methods: </strong>This retrospective study included 205 CT examinations from 102 patients with 103 implanted upper-arm CV ports. Three port systems and eight iodinated contrast media (ICM) formulations grouped by viscosity were evaluated. A no-intercept linear model estimated separate slopes and 13 kg/cm² thresholds for nine port-viscosity combinations using the recorded maximum delivered rate with port-clustered robust inference. Sensitivity analyses excluded pressure-limit activation and flow-reduction cases and examined the 13 kg/cm² preset subgroup.</p><p><strong>Results: </strong>All subgroup pressure-flow slopes were statistically significant (all p < 0.001), with uncentered R² values ranging from 0.962 to 0.993. Pressure-limit activation and flow reduction occurred in 38 (18.5%) and 26 (12.7%) examinations, respectively. Pressure-flow slopes differed by port and viscosity (both p < 0.001); the port-by-viscosity interaction was not significant in the primary analysis (p = 0.336). Residual formulation-level heterogeneity was detected within viscosity groups (p < 0.001). Thresholds ranged from 2.7 to >5.0 mL/s and changed by at most 5.6% in exclusion analyses. Similar patterns persisted in the 13 kg/cm² preset subgroup despite sparse cells.</p><p><strong>Conclusion: </strong>Under the evaluated clinical conditions, pressure-flow relationships through upper-arm CV ports differed by port system and ICM viscosity group. Model-based descriptive thresholds provide port- and viscosity-specific pressure-flow reference information for the evaluated combinations.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updated Pediatric Sonographic Percentile Curves for Abdominal Organ Size in a Large European Cohort.","authors":"Elena Höhne, Sebastian Fischer, Jennifer Gotta, Philipp Reschke, Vitali Koch, Scherwin Mahmoudi, Ibrahim Yel, Katrin Eichler, Jan-Henning Klusmann, Thomas Vogl, Leon Grünewald","doi":"10.1016/j.acra.2026.09.013","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.013","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate measurement of liver, spleen, and kidney size plays a central role in pediatric diagnostics. Contemporary sonographic percentile data are needed to support interpretation of organ dimensions in clinical practice. This study aimed to generate age-specific clinical reference percentiles for abdominal organ size based on a large real-world pediatric ultrasound cohort.</p><p><strong>Methods and materials: </strong>This retrospective analysis included 27,696 clinically indicated pediatric abdominal ultrasound examinations conducted between 2000 and 2025 at a major urban tertiary-care center in Germany. Standardized organ measurements were obtained by experienced pediatric radiologists. Patients were not excluded on the basis of underlying pathology. Age-specific clinical percentile curves (P3-P97) were generated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to capture non-linear growth patterns.</p><p><strong>Results: </strong>From 27,696 examinations of children aged 0-18, organ-specific measurements comprised 12,254 livers, 12,703 spleens, 16,254 right kidneys, and 16,516 left kidneys. All organs demonstrated non-linear growth trajectories. No overall statistically significant sex differences were observed in this cohort for spleen (p = 0.195), liver (p = 0.517), right kidney (p = 0.712), or left kidney (p = 0.797). Owing to the retrospective design, sex-specific variation within individual age strata cannot be excluded. The left kidney was consistently larger than the right (p<0.0001), with a mean difference of 2-5 mm.</p><p><strong>Conclusion: </strong>This study presents contemporary clinical percentile curves for liver, spleen, and kidney dimensions derived from one of the largest Central European pediatric ultrasound datasets published to date. The curves provide population-specific benchmarks for interpreting pediatric abdominal organ dimensions in real-world clinical practice and aim to support standardized assessment across the full pediatric age range.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.","authors":"Pu Zhou, Li Yang, Ying He, Libao Wang, Rong Zhao, Mei Gu, Pengfei Zhu, Xu Luo, Xiangyun Kong, Pei Du","doi":"10.1016/j.acra.2026.09.038","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.038","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study investigated the clinical utility of a machine learning (ML) model based on ultrasoundomics features (UFs) from primary foci to predict pathological complete response (pCR) in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NAC).</p><p><strong>Materials and methods: </strong>Ultrasound images from 333 patients with BC treated with NAC were retrospectively analyzed. Patients in the internal training cohort and external validation cohort were from two different hospitals, respectively. Tumor regions of interest (ROI) were delineated, and outward segmentation of the peritumor area was performed, using the ROI edge as a baseline and expanding it by 3 mm and 5 mm, respectively. This resulted in three distinct segmentation images: ROI (intratumoral region), ROI (intratumoral region + 3-mm peritumoral region), and ROI (intratumoral region + 5-mm peritumoral region). Three sets of UFs were extracted, normalized using the Z-score method, and further refined through Spearman correlation analysis and LASSO for feature selection. Eight ML models (R0), based on key UFs in ROI (intratumoral region), were compared: support vector machine, K-nearest neighbor, random forest, Naïve Bayes, logistic regression, AdaBoost (AB), multilayer perceptron, and light gradient boosting machine learning. The optimal ML models were evaluated using diagnostic performance metrics, including AUC, accuracy, sensitivity, and specificity. The best-performing algorithm was then applied to construct ML models (R3 and R5) incorporating ROI (intratumoral region + 3-mm peritumoral region) and ROI (intratumoral region + 5-mm peritumoral region). Predictive efficacy and clinical utility were assessed through decision curve analysis (DCA) and Shapley Additive exPlanation (SHAP), which quantified feature importance and facilitated model visualization and interpretation.</p><p><strong>Results: </strong>Among the 333 patients, 119 (35.7%) achieved pCR, while 214 (64.3%) did not. Among the eight ML algorithms, the R0 model based on AB demonstrated the best performance, with an AUC of 0.811 (Internal training cohort) and 0.632 (External validation cohort). The R3 model also showed improved performance, with an AUC of 0.830 (Internal training cohort) and 0.699 (External validation cohort). The R5 model yielded the highest predictive accuracy, with an AUC of 0.871 (Internal training cohort) and 0.779 (External validation cohort). SHAP analysis was conducted for R5, and SHAP waterfall charts enable single-sample predictions to validate the model's clinical utility. DCA revealed that the combination of intratumoral region and 5-mm peritumoral region in R5 offered superior clinical benefit.</p><p><strong>Conclusion: </strong>ML models integrating intratumoral and peritumoral ultrasoundomics offer valuable assistance in assessing NAC efficacy in patients with BC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}