Academic Radiology最新文献

{"title":"From Distress to Growth: Promoting Moral Resilience in Radiology.","authors":"Jose Nicolas Duarte, Gonzalo Andrés Montaño Rozo, David Fernando Torres Cortes, Alejandra Duarte","doi":"10.1016/j.acra.2025.02.037","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.037","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722577","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":"Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.","authors":"Ping Wang, Jianing Cui, Haoyuan Du, Zhanhua Qian, Huili Zhan, Heng Zhang, Wei Ye, Wei Meng, Rongjie Bai","doi":"10.1016/j.acra.2025.03.005","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.005","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.</p><p><strong>Objective: </strong>To develop a machine learning (ML) based model to predict STAS using preoperative CT imaging features and clinicopathological data, while enhancing interpretability through shapley additive explanations (SHAP) analysis.</p><p><strong>Materials and methods: </strong>This multicenter retrospective study included 1237 patients with pathologically confirmed primary LUAD from three hospitals. Patients from Center 1 (n=932) were divided into a training set (n=652) and an internal test set (n=280). Patients from Centers 2 (n=165) and 3 (n=140) formed external validation sets. CT imaging features and clinical variables were selected using Boruta and least absolute shrinkage and selection operator regression. Seven ML models were developed and evaluated using five-fold cross-validation. Performance was assessed using F1 score, recall, precision, specificity, sensitivity, and area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>The Extreme Gradient Boosting (XGB) model achieved AUCs of 0.973 (training set), 0.862 (internal test set), and 0.842/0.810 (external validation sets). SHAP analysis identified nodule type, carcinoembryonic antigen, maximum nodule diameter, and lobulated sign as key features for predicting STAS. Logistic regression analysis confirmed these as independent risk factors.</p><p><strong>Conclusion: </strong>The XGB model demonstrated high predictive accuracy and interpretability for STAS. By integrating widely available clinical and imaging features, this model offers a practical and effective tool for preoperative risk stratification, supporting personalized surgical planning in primary LUAD management.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722580","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":"The CHANOA Score: Will Collateral Circulation Help Determine Candidacy for Endovascular Stroke Treatment?","authors":"Franklin G Moser","doi":"10.1016/j.acra.2025.03.021","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.021","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712008","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":"Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.","authors":"Haonan Mei, Hui Chen, Qingyuan Zheng, Rui Yang, Nanxi Wang, Panpan Jiao, Xiao Wang, Zhiyuan Chen, Xiuheng Liu","doi":"10.1016/j.acra.2025.03.002","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.002","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.</p><p><strong>Materials and methods: </strong>This retrospective study analyzed a total of 596 patients from three cohorts, utilizing corticomedullary phase computed tomography urography (CTU) images. The nnUNet v2 network combined with numerical computation was employed to segment the perirenal fat region. Pyradiomics algorithms and a computed tomography foundation model were used to extract features from CTU images separately, creating single-modality predictive models for identifying perirenal fat adhesion. By concatenating the Pyradiomics and foundation model features, an early fusion multimodal predictive signature was developed. The prognostic performance of the single-modality and multimodality models was further validated in two independent cohorts.</p><p><strong>Results: </strong>The nnUNet v2 segmentation model accurately segmented both kidneys. The neural network and thresholding approach effectively delineated the perirenal fat region. Single-modality models based on radiomic and computed tomography foundation features demonstrated a certain degree of accuracy in diagnosing and identifying perirenal fat adhesion, while the early feature fusion diagnostic model outperformed the single-modality models. Also, the perirenal fat adhesion score showed a positive correlation with surgical time and intraoperative blood loss.</p><p><strong>Conclusion: </strong>AI-based radiomics and foundation models can accurately identify the degree of perirenal fat adhesion and have the potential to be used for surgical risk assessment.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712006","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":"Size and Contrast Thresholds for Liver Lesion Detection in Regular and Low-dose CT Examinations: A Reader Study of 2300 Synthetic Lesions Across 100 Patients.","authors":"Li Yan, Ulrich Genske, Yang Peng, Angelo Laudani, Katharina Beller, Thula Walter-Rittel, Moritz Wagner, Bernd Hamm, Paul Jahnke","doi":"10.1016/j.acra.2025.03.001","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.001","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To determine the size and contrast required for liver lesion detection in regular and low-dose computed tomography (CT) examinations.</p><p><strong>Materials and methods: </strong>100 abdominal CT datasets were retrospectively collected, with 50 originating from vendor A and 50 from vendor B. Half the datasets from each scanner were regular-dose oncologic examinations, the other half were acquired using a low-dose kidney stone protocol. Cylindrical liver lesions with 23 different combinations of diameter and contrast to the surrounding liver were digitally inserted. Seven radiologists assessed lesion detectability in a four-alternative forced choice reading experiment, and image noise was measured within the liver.</p><p><strong>Results: </strong>Lesion detection thresholds at regular dose were at -30, -35, and -70 Hounsfield unit (HU) lesion contrast (vendor A) and -25, -35, and -65 HU (vendor B) for lesions with 15-, 10-, and 5-mm diameter, respectively. At low dose, thresholds were -40 and -45 HU (vendor A) and -40 and -50 HU (vendor B) for 15- and 10-mm lesions, while 5-mm lesions did not reach the detection threshold. Noise levels were 21.5±2.3 HU at regular dose vs 22.2±2.0 HU at low dose for vendor A (P=.06) and 25.9±4.9 HU vs 30.9±3.1 HU for vendor B (P<.001).</p><p><strong>Conclusion: </strong>In oncologic CT examinations, liver lesions with diameters of 15-, 10-, and 5-mm require contrasts of -30, -35, and -70 HU, respectively for reliable detection. In low-dose examinations, greater contrasts of -40 and -50 HU are required for lesions measuring 15- and 10-mm, while readers do not reliably detect 5-mm lesions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694314","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":"Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective Study.","authors":"Abhinav Suri, Pritam Mukherjee, Nusrat Rabbee, Perry J Pickhardt, Ronald M Summers","doi":"10.1016/j.acra.2025.02.047","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.047","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Pancreatic imaging biomarkers on CT imaging are known to be associated with diabetes. However, no studies have examined if these imaging biomarkers are resilient to changes in segmentation quality and contrast status. Here, we assess if imaging biomarkers are robust to variations in pancreatic segmentation quality and contrast status, and how these factors affect their ability to predict diabetes.</p><p><strong>Materials and methods: </strong>This retrospective study selected patients with CT scans and corresponding HbA1c tests from two institutions. Patients were classified into two categories: having diabetes at the time or < 4 years after the scan (diabetic/incident) vs not having diabetes within 4 years after the scan (nondiabetic). Pancreatic imaging biomarkers, including average attenuation, intrapancreatic fat fraction, fractal dimension of the pancreatic boundary and volume, were measured using three pancreatic segmentation algorithms (TotalSegmentator, nnU-Net, and DM-UNet). Pairwise comparisons were made between algorithms when computing pancreatic imaging biomarker values for all patient scans. Predictive ability of imaging biomarkers (derived from each algorithm) was assessed for agreement between algorithms using a generalized additive model.</p><p><strong>Results: </strong>A total of 9772 patients (age, 56.1 years ± 9.1 [SD]; 5407 females) were included in this study. Imaging biomarkers based on attenuation measurements showed high algorithm agreement (ICC ≥0.93), with lower agreement on measures not reliant on attenuation. Models trained on imaging biomarkers derived from these algorithms exhibited good predictive agreement (AUC for diabetes overall, 0.84-0.91; contrast scans, 0.73-0.80; noncontrast scans, 0.62-0.80). Algorithms achieved a positive predictive value of 0.79-0.84, and negative predictive value of 0.89-0.94.</p><p><strong>Conclusion: </strong>Attenuation-based imaging biomarkers demonstrated robustness to segmentation algorithm quality and consistent predictive ability across different clinical scenarios. These findings suggest that CT-derived biomarkers could be a reliable tool for diabetes screening across multiple institutions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694310","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 of a PET-CT Based Radiomics Model for Preoperative Prediction of the Novel IASLC Grading and Prognosis in Patients with Clinical Stage I Pure Solid Invasive Lung Adenocarcinoma.","authors":"Junping Lan, Hanzhe Wang, Enhui Xin, Beihui Xue, Kun Tang, Shouliang Miao, Yimin Chen, Zhe Xiao, Jiageng Xie, Linfeng Shao, Shulan Chen, Xiangwu Zheng, Xuan Zheng","doi":"10.1016/j.acra.2025.02.017","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.017","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop and validate a fluorine-18-fludeoxyglucose (¹⁸F-FDG) PET/CT-based radiomics nomogram for preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grading and recurrence-free survival (RFS) in patients with clinical stage I pure-solid invasive lung adenocarcinoma (LADC). MATERIALS AND METHODS: 418 patients with clinical stage I pure-solid invasive LADC who underwent preoperative ¹⁸F-FDG PET/CT examination were retrospectively enrolled. All patients were separated into the low-grade group (grade I and II; n=315) and the high-grade group (grade III; n=103) according to the IASLC grading system, and the cohort was randomly divided into a training set (n=292) and a testing set (n=126) at a ratio of 7:3. Radiomics features were extracted from CT and PET images in regions of the entire tumor. Multivariate analysis identified the independent predictors for IASLC grading and RFS. The Radscore, along with clinical and radiological features were combined to establish a predictive nomogram.</p><p><strong>Results: </strong>The ultimate Radiomics model, achieving AUCs of 0.838 and 0.768 in the training and testing sets. The multivariate logistic regression showed that higher maximum standard uptake value (SUVmax), cavity presence are the independent risk factors for IASLC grading. The integrated nomogram showed superior prediction performance than CT model (p=0.001) and PET model (p=0.028) in the training set. Furthermore, both pathological grade and preoperatively predictive IASLC grade derived by nomogram significantly stratified patients for RFS, with 5-year survival rates showing marked differences between low-grade and high-grade LADC (p<0.001).</p><p><strong>Conclusion: </strong>The preoperative PET/CT-based radiomics nomogram represents a potential biomarker for predicting IASLC grade and RFS in patients with clinical stage I pure-solid invasive LADC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694312","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":"Integrating Omics: A New Paradigm in the Management of Hepatocellular Carcinoma.","authors":"Aymen Bahsoun, Hero K Hussain","doi":"10.1016/j.acra.2025.02.016","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.016","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674786","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":"Ultrasound-Guided Thermal Ablation vs Surgery in T1N0M0 Papillary Thyroid Carcinoma: A Systematic Review and Meta-analysis.","authors":"Yanwei Chen, Jianming Li, Shuangshuang Zhao, Zheng Zhang, Yun Cai, Huajiao Zhao, Xin Zhang, Baoding Chen","doi":"10.1016/j.acra.2025.02.048","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.048","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Ultrasound-guided thermal ablation (TA) offers a minimally invasive alternative to surgery for T1N0M0 papillary thyroid carcinoma (PTC), but its efficacy and safety remain controversial. This meta-analysis aimed to evaluate and compare the outcomes of TA and surgery in treating T1N0M0 PTC, encompassing both T1a and T1b stages.</p><p><strong>Materials and methods: </strong>We conducted a systematic review and meta-analysis including studies comparing TA and surgery for T1N0M0 PTC up to October 23, 2024. Standardized mean differences and odds ratios (OR) with 95% confidence intervals (CI) were calculated for primary and secondary outcomes.</p><p><strong>Results: </strong>Sixteen studies with a total of 5045 patients were analyzed. No significant differences were observed in recurrence (OR=1.464; 95% CI=0.881, 2.433; P=.141), lymph node metastasis (OR=0.817; 95% CI=0.492, 1.356; P=.434), transient hoarseness (OR=0.700; 95% CI=0.339, 1.445; P=.334), hematoma (OR=0.528; 95% CI=0.187, 1.492; P=.228), and infection (OR=0.368; 95% CI=0.060, 2.268; P=.281). Notably, TA significantly reduced permanent hoarseness, hypoparathyroidism, dysphagia, procedure time, hospitalization, cost, estimated blood loss, and surgical incision (all P<.05). The subgroup analyses demonstrated similar primary outcomes within each subgroup, including tumor stage (T1a/T1b), type of TA (microwave/radiofrequency), and follow-up time (short-term/long-term).</p><p><strong>Conclusion: </strong>Ultrasound-guided TA is a safe and effective alternative to surgery for both T1a and T1b stages of T1N0M0 PTC, offering comparable prognostic outcomes with fewer complications, lower costs, and faster recovery.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671643","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":"Advances in Precision Medicine: Improving Breast Cancer Diagnosis Through the Integrative Use of Contrast-Enhanced Mammography, Clinical Descriptors and Radiomics.","authors":"Zhe Zhang, Zhen Zhang, Mengqi Yang, Shuihua Wang, John Moraros","doi":"10.1016/j.acra.2025.03.017","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.017","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671641","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}