Academic Radiology最新文献

{"title":"Creating and Publishing Systematic Reviews, Meta-analyses, and Scoping Reviews: A 10-step Guide for Students and Trainees","authors":"Ishwarya Sivakumar BS ,&nbsp;Katie Lobner MLIS ,&nbsp;Rachel L. Walden MS ,&nbsp;Clifford R. Weiss MD, FSIR, FCIRSE","doi":"10.1016/j.acra.2025.01.020","DOIUrl":"10.1016/j.acra.2025.01.020","url":null,"abstract":"<div><div>This article serves as a step-by-step guide for students and trainees who wish to conduct a systematic review, meta-analysis, or scoping review. As the field of evidence synthesis progresses, more students and trainees are attempting to conduct reviews, and it can be unclear how to approach such a project. In 10 organized steps, we describe different types of reviews, explain how to choose the most appropriate one, detail the steps involved in conducting a review, and list resources that are available to support authors of reviews. We describe the steps involved in 1) forming an appropriate research team; 2) developing a compelling research question; 3) writing a review; and 4) reporting the findings with clarity.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2357-2363"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426633","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":"A Generalizable Framework for Kidney Stone Composition Characterization Using Dual-Energy CT","authors":"Picha Shunhavanich PhD ,&nbsp;Andrea Ferrero PhD ,&nbsp;Cynthia H. McCollough PhD ,&nbsp;Scott S. Hsieh PhD","doi":"10.1016/j.acra.2024.10.025","DOIUrl":"10.1016/j.acra.2024.10.025","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Classification of non-uric acid (NUA) renal stones in dual-energy CT (DECT) is difficult due to their similar CT number ratios (CTRs) and because the CTRs change with patient size and acquisition protocol. In this work, we developed a generalizable framework to estimate correct CTR threshold for different stone types, protocols, and patient sizes and validated the results on two DECT scanners.</div></div><div><h3>Materials and Methods</h3><div>Our framework assumes generic x-ray spectra, estimates the added filtration to match half-value-layer (HVL) measurements, and predicts the CTR of each stone type from the chemical composition and patient size. The framework was validated for four calcium or iodine inserts in two solid water phantom sizes on two DECT scanners, and on 45 human urinary stones of five types (uric acid, cystine, calcium oxalate monohydrate, brushite, and hydroxyapatite) in three different water phantom sizes on a dual-source DECT. All scans were performed at high dose, using routine acquisition parameters. The predicted CTR was compared with the measured CTR.</div></div><div><h3>Results</h3><div>The predicted CTRs for different stone types were consistent with experimentally measured values, with average absolute errors of 2.8% (range 1.3–4.3%), 1.8% (range 0.7–4.4%), and 1.8% (range 0.8–2.4%) for the 30, 40, and 50 cm phantom sizes. The predicted CTR errors of the four inserts were within 6.4%.</div></div><div><h3>Conclusion</h3><div>The developed framework uses easily obtained HVL measurements to predict renal stone CTRs of different compositions for varied patient sizes. With further refinement, it may help classify NUA subtypes in clinical scans.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2064-2072"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584933","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":"Contrast-Enhanced Computed Tomography Radiomics Predicts Colony-Stimulating Factor 3 Expression and Clinical Prognosis in Ovarian Cancer","authors":"Xiaofeng Lv ,&nbsp;Xiaoxue Zhang ,&nbsp;Ruyue Gong ,&nbsp;Changyu Wang ,&nbsp;Lili Guo","doi":"10.1016/j.acra.2024.11.023","DOIUrl":"10.1016/j.acra.2024.11.023","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To develop a radiomics model for non-invasive prediction of colony-stimulating factor 3 (CSF3) expression in ovarian cancer (OC) and evaluate its prognostic value.</div></div><div><h3>Materials and Methods</h3><div>We acquired clinical data, genetic information, and corresponding computed tomography (CT) scans of OC from The Cancer Genome Atlas and The Cancer Imaging Archive repositories. We assessed the prognostic significance of CSF3 and its association with clinical features through the utilization of Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, along with subgroup analysis. To explore the potential molecular mechanisms associated with CSF3 expression, we utilized gene set enrichment analysis and conducted an analysis on immune-cell infiltration. The max-relevance and min-redundancy and recursive feature elimination (RFE) algorithms were used for feature screening. The CT-based radiomics prediction model was built using support vector machine (SVM) and logistic regression (LR).</div></div><div><h3>Results</h3><div>The expression of CSF3 was found to be decreased in OC, and high expression of CSF3 was associated with poor overall survival. Moreover, it was noted that the expression of CSF3 exhibited a positive correlation with programmed death ligand 1 (PD-L1) and sialic acid-binding Ig-like lectin 15 (SIGLEC15). Patients with high CSF3 expression exhibited a decrease in tumor necrosis factor receptor superfamily member 7 (CD27) expression. The infiltration of neutrophils increased and CD8 +T cells decreased in CSF3 high expression group.</div></div><div><h3>Conclusion</h3><div>The radiomics model, which utilized both LR and SVM methods, demonstrated significant clinical applicability. The expression level of CSF3 was related to the prognosis of OC. Radiomics based on CT can serve as a novel tool for forecasting prognosis.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2053-2063"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752228","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 Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma","authors":"Yu Mao,&nbsp;Xin Kong,&nbsp;Yuqi Luo,&nbsp;Fengjun Xi,&nbsp;Yan Li,&nbsp;Jun Ma","doi":"10.1016/j.acra.2024.11.044","DOIUrl":"10.1016/j.acra.2024.11.044","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sellar region.</div></div><div><h3>Methods</h3><div>This study included 348 patients with histologically confirmed PA (n = 139) and ACP (n = 209). Data were randomly divided into training and testing cohorts in a 7:3 ratio. Pre-trained ResNet50 network was utilized to extract DTL features from T1WI, T2WI, and CET1, while radiomics features (Rad) were extracted from manually delineated images of the same modalities. The fusion feature set (DLR) was constructed by integrating these features. Semantic features were used to develop clinical models. Pearson rank correlation and The least absolute shrinkage and selection operator regression were used for feature selection, and K-nearest neighbor algorithm was applied to establish the model. The performance of the model was evaluated using receiver operating characteristic curve. DeLong's test was performed to assess differences between models, and decision curve analysis was conducted to evaluate the clinical utility of the models.</div></div><div><h3>Results</h3><div>The DLR model achieved AUC values of 0.945 (95% CI, 0.9149–0.9760) in the training cohort and 0.929 (95% CI, 0.8824–0.9762) in the testing cohort, significantly higher than those of models using DTL features, Rad features, or clinical features alone.</div></div><div><h3>Conclusion</h3><div>The fusion model based on MRI deep transfer learning and radiomics (DLR) demonstrated high accuracy and clinical utility in discriminating between PA and ACP, providing an effective tool for the non-invasive diagnosis of these two diseases.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2197-2208"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848357","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":"Radiomics of Multimodal Ultrasound for Early Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer","authors":"Cai-feng Wan PHD ,&nbsp;Zhuo-yun Jiang PHD ,&nbsp;Yu-qun Wang MD ,&nbsp;Lin Wang MD ,&nbsp;Hua Fang MD ,&nbsp;Ye Jin MD ,&nbsp;Qi Dong MD ,&nbsp;Xue-qing Zhang PHD ,&nbsp;Li-xin Jiang PHD","doi":"10.1016/j.acra.2024.11.012","DOIUrl":"10.1016/j.acra.2024.11.012","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To construct and validate a clinical-radiomics model based on radiomics features extracted from two-stage multimodal ultrasound and clinicopathologic information for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients treated with NAC.</div></div><div><h3>Materials and Methods</h3><div>Consecutive women with biopsy-proven breast cancer undergoing multimodal US pretreatment and after two cycles of NAC and followed by surgery between January 2014 and November 2023 were retrospectively collected for clinical-radiomics model construction (n = 274) and retrospective test (n = 134). The predictive performance of it was further tested in a subsequent prospective internal test set recruited between January 2024 to July 2024 (n = 76). Finally, a total of 484 patients were enrolled. The clinical-radiomics model predictive performance was compared with radiomics model, clinical model and radiologists’ visual assessment by area under the receiver operating characteristic curve (AUC) analysis and DeLong test.</div></div><div><h3>Results</h3><div>The proposed clinical-radiomics model obtained the AUC values of 0.92 (95%CI: 0.88, 0.94) and 0.85 (95%CI: 0.79, 0.89) in retrospective and prospective test sets, respectively, which were significantly higher than that those of the radiomics model (AUCs: 0.75–0.85), clinical model (AUCs: 0.68–0.72) and radiologists’ visual assessments (AUCs:0.59–0.68) (all <em>p</em> &lt; 0.05). In addition, the predictive efficacy of the radiologists was improved under the assistance of the clinical-radiomics model significantly.</div></div><div><h3>Conclusion</h3><div>The clinical-radiomics model developed in this study, which integrated clinicopathologic information and two-stage multimodal ultrasound features, was able to early predict pCR to NAC in breast cancer patients with favorable predictive effectiveness.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1861-1873"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848372","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":"Differentiating Malignant From Benign Subpleural Lung Lesions Using Perfluorobutane-Enhanced Ultrasound: A Very New Technology for an Old Problem","authors":"Paul Patrick Cronin","doi":"10.1016/j.acra.2025.02.026","DOIUrl":"10.1016/j.acra.2025.02.026","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2281-2283"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476993","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":"Factors Influencing Choosing Diagnostic Radiology As a Specialty Among Medical Students","authors":"Afnan Fahad Almuhanna ,&nbsp;Deem Hamad Alsultan ,&nbsp;Danyah Saleh Almohsen ,&nbsp;Deemah Salem AlHuraish ,&nbsp;Farah Nedal AlRatrout ,&nbsp;Rabab Hussain Alzanadi ,&nbsp;Nersyan Talaat Sharbini","doi":"10.1016/j.acra.2024.12.015","DOIUrl":"10.1016/j.acra.2024.12.015","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Radiology plays a crucial role in modern healthcare as it facilitates the diagnosis and treatment of various medical conditions in different specialties. Therefore, this study aimed to gain insights into the factors that contribute to medical students choosing radiology as a future career.</div></div><div><h3>Materials and Methods</h3><div>This cross-sectional study used an online, self-administered questionnaire. Data were collected exclusively from medical students at Imam Abdulrahman bin Faisal University in Saudi Arabia from August to September 2023. The chi-square test was used to assess the factors associated with medical students’ choices as a future specialty.</div></div><div><h3>Results</h3><div>A total of 431 eligible respondents completed the survey; 267 (61.9%) were female, and their ages ranged from 18–36 years. When asked about their specialty of choice, 209 (48.5%) were interested in surgery and internal medicine, whereas only 81 (18.8%) chose radiology. Regarding the factors influencing the choice of radiology, the majority (85.6%) reported the importance of lifestyle in their choice, followed by the impact on patient care (83.5%), work environment (82.1%), intellectual challenge (79.8%), presence of procedures (76.6%), degree of patient contact (76.1%), and pre-existing experience of radiology (75.9%).</div></div><div><h3>Conclusion</h3><div>Many factors influence medical students’ choices of radiology as a future career. Predominantly, the working environment, current exposure, knowledge of the specialty, extent of patient contact, and work–life balance were chosen as the main factors affecting medical students’ choices when considering radiology as a future specialty.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2371-2377"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973052","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":"Optimizing Radiology Reporting and the Peer Review Process","authors":"Eniola T. Oluyemi MD, MPH ,&nbsp;Jeffrey P. Guenette MD, MPH ,&nbsp;Tessa S. Cook MD, PhD ,&nbsp;Jeffrey G. Jarvik MD, MPH","doi":"10.1016/j.acra.2025.02.036","DOIUrl":"10.1016/j.acra.2025.02.036","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1783-1784"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537611","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 in Medical Education","authors":"Raufay Abbasi BA in Psychology ,&nbsp;Omer A. Awan MD, MPH, CIIP","doi":"10.1016/j.acra.2024.06.018","DOIUrl":"10.1016/j.acra.2024.06.018","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2380-2383"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728252","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":"Natural Killer Cell-Associated Radiogenomics Model for Hepatocellular Carcinoma: Integrating CD2 and Enhanced CT-Derived Radiomics Signatures","authors":"Yan-zhu Chen ,&nbsp;Zhi-shang Meng ,&nbsp;Yan-nan Zhang ,&nbsp;Zuo-lin Xiang","doi":"10.1016/j.acra.2024.10.043","DOIUrl":"10.1016/j.acra.2024.10.043","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality. Natural Killer (NK) cells play a crucial role in immune defense against HCC, but their activity is often impaired by the tumor microenvironment (TME). This study aims to integrate radiomics and transcriptomics to develop a prognostic model linking NK cell characteristics to clinical outcomes in HCC.</div></div><div><h3>Methods</h3><div>Transcriptomic data from five cohorts (734 HCC patients) from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using the Microenvironment Cell Populations-counter algorithm. NK cell-related prognostic biomarkers were identified via weighted gene co-expression network analysis and LASSO-Cox regression. Radiomics models were established using CT imaging features from 239 patients in three datasets from The Cancer Imaging Archive and Shanghai East Hospital. HCC radiogenomic subtypes were proposed by integrating genetic biomarkers and radiomics models.</div></div><div><h3>Results</h3><div><em>CD2</em> expression was identified as an independent NK cell-related prognostic biomarker, with a positive impact on prognosis and a strong correlation with NK cell-associated biological processes in HCC. A robust radiomics model was constructed, and the integration of CD2 expression with radioscore identified potential radiogenomic subtypes of HCC.</div></div><div><h3>Conclusion</h3><div>Radiomics has potential to link TME immune phenotypes with HCC prognosis. <em>CD2</em> is a key biomarker connecting NK cells with radiomic features, offering a new classification of HCC into radiogenomic subtypes. This approach supports the use of radiogenomics in personalized HCC treatment.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1981-1992"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631735","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}