Academic Radiology最新文献

{"title":"What Program Directors Think About Resident Recruitment: Results of the 2023 Spring Survey of the Association of Program Directors in Radiology (APDR) Part I","authors":"Hillary W. Garner MD ,&nbsp;Priscilla J. Slanetz MD, MPH ,&nbsp;Jonathan O. Swanson MD, MBA ,&nbsp;Brent D. Griffith MD ,&nbsp;Carolynn M. DeBenedectis MD ,&nbsp;Jennifer E. Gould MD ,&nbsp;Tara L. Holm MD ,&nbsp;Michele Retrouvey MD ,&nbsp;Angelisa M. Paladin MD ,&nbsp;Anna Rozenshtein MD, MPH","doi":"10.1016/j.acra.2024.08.045","DOIUrl":"10.1016/j.acra.2024.08.045","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The Association of Program Directors in Radiology (APDR) administers an annual survey to assess issues and experiences related to residency program management and education. Our purpose is to provide the response data from the 2023 survey and discuss its insights on the impact of COVID-19 on resident recruitment (Part I) and education (Part II), which can be used to facilitate planning and resource allocation for the evolving needs of programs and their leadership. In Part I, we consider the effects of ERAS preference signaling, the virtual interview format, and the potential of a universal interview release date.</div></div><div><h3>Materials and Methods</h3><div>An observational, cross-sectional study of the APDR membership was performed using a web-based survey consisting of 45 questions, 23 of which pertain to virtual recruitment and are discussed in Part I of a two-part survey analysis. All active APDR members (n = 393) were invited to participate in the survey.</div></div><div><h3>Results</h3><div>The response rate was 32% (124 of 393). 83% reported that signaling increased the likelihood of an interview offer. 96% reported only offering virtual interviews; however, 59% intended to offer virtual-only interviews in the future. 53% would adhere to a universal interview release date but an additional 44% would do so depending on the agreed date, Results were tallied using Qualtrics software and qualitative responses were tabulated or summarized as comments.</div></div><div><h3>Conclusions</h3><div>Virtual recruitment is expected to continue for many programs and most respondents would accept a universal interview release date. Preference signaling and geographic signaling are considered positive additions to the application process.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5324-5330"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331858","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":"Risk of Lung Cancer in Peripheral Pulmonary Nodules","authors":"Mark M. Hammer MD,&nbsp;Andetta R. Hunsaker MD","doi":"10.1016/j.acra.2024.06.021","DOIUrl":"10.1016/j.acra.2024.06.021","url":null,"abstract":"<div><h3>Purpose</h3><div>To determine the risk of lung cancer and inter-observer agreement for small pulmonary nodules<span> either touching or near the pleura.</span></div></div><div><h3>Methods</h3><div>Nodules were derived from two cohorts: patients from the National Lung Screening Trial<span> with a solid nodule measuring 6–9.5 mm; and patients with incidental pulmonary nodules in our healthcare system with a solid nodule measuring 1–8 mm. Only the dominant nodule was evaluated for each patient. All malignant nodules as well as a random sample of 200 benign nodules from each cohort were included. Two fellowship-trained thoracic radiologists independently reviewed each case to record nodule morphology (compatible with lymph node or not) and nodule location (pleural-based, septal connection to the pleura, or neither). One radiologist measured the distance to the pleura.</span></div></div><div><h3>Results</h3><div>After exclusion criteria were applied, a total of 434 nodules were included, of which 45 were lung cancers. Considering all pleural-based nodules with lymph node morphology as benign, 0–7% of cancers were misclassified as benign, specificity 33%, and κ = 0.69. Considering subpleural nodules and those with septal connection to the pleura, 7–11% of cancers were misclassified (p = 0.16–0.25 versus pleural-based), specificity 40–52% (p &lt; .0001), and κ = 0.60. Considering nodules with lymph node morphology ≤ 2 mm from the pleura, 2–7% of cancers were misclassified (p = 1 versus pleural-based), specificity 41–36% (p &lt; .0001), and κ = 0.78.</div></div><div><h3>Conclusion</h3><div>Considering nodules with lymph node morphology with septal connection, or those ≤ 2 mm from the pleura, as benign does not lead to significant misclassification of lung cancers as benign.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5261-5268"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472242","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":"Prediction of Anastomotic Leakage in Esophageal Cancer Surgery: A Multimodal Machine Learning Model Integrating Imaging and Clinical Data","authors":"Michail E. Klontzas ,&nbsp;Motonari Ri ,&nbsp;Emmanouil Koltsakis ,&nbsp;Erik Stenqvist ,&nbsp;Georgios Kalarakis ,&nbsp;Erik Boström ,&nbsp;Aristotelis Kechagias ,&nbsp;Dimitrios Schizas ,&nbsp;Ioannis Rouvelas ,&nbsp;Antonios Tzortzakakis","doi":"10.1016/j.acra.2024.06.026","DOIUrl":"10.1016/j.acra.2024.06.026","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive techniques, esophagectomy carries significant morbidity and mortality. One of the most common and feared complications of esophagectomy is anastomotic leakage (AL). Our work aimed to develop a multimodal machine-learning model combining CT-derived and clinical data for predicting AL following esophagectomy for esophageal cancer.</div></div><div><h3>Material and Methods</h3><div>A total of 471 patients were prospectively included (Jan 2010–Dec 2022). Preoperative computed tomography (CT) was used to evaluate celia trunk stenosis and vessel calcification. Clinical variables, including demographics, disease stage, operation details, postoperative CRP, and stage, were combined with CT data to build a model for AL prediction. Data was split into 80%:20% for training and testing, and an XGBoost model was developed with 10-fold cross-validation and early stopping. ROC curves and respective areas under the curve (AUC), sensitivity, specificity, PPV, NPV, and F1-scores were calculated.</div></div><div><h3>Results</h3><div>A total of 117 patients (24.8%) exhibited post-operative AL. The XGboost model achieved an AUC of 79.2% (95%CI 69%–89.4%) with a specificity of 77.46%, a sensitivity of 65.22%, PPV of 48.39%, NPV of 87.3%, and F1-score of 56%. Shapley Additive exPlanation analysis showed the effect of individual variables on the result of the model. Decision curve analysis showed that the model was particularly beneficial for threshold probabilities between 15% and 48%.</div></div><div><h3>Conclusion</h3><div>A clinically relevant multimodal model can predict AL, which is especially valuable in cases with low clinical probability of AL.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4878-4885"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494176","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":"Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising","authors":"Stephanie A. Soderlund ,&nbsp;Abdullah S. Bdaiwi ,&nbsp;Joseph W. Plummer ,&nbsp;Jason C. Woods ,&nbsp;Laura L. Walkup ,&nbsp;Zackary I. Cleveland","doi":"10.1016/j.acra.2024.06.029","DOIUrl":"10.1016/j.acra.2024.06.029","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div><span>Hyperpolarized xenon (</span>¹²⁹<span>Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient<span> (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease<span>. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.</span></span></span></div></div><div><h3>Materials and Methods</h3><div><span>GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted </span>¹²⁹<span><span>Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis<span> (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed </span></span>diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.</span></div></div><div><h3>Results</h3><div>Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p &lt; 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p &gt; 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>When applied to diffusion-weighted ¹²⁹<span><span>Xe images, GLHOSVD improved image quality and allowed </span>airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.</span></div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5289-5299"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499621","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":"Nomogram for Predicting Emergent Conversion to General Anaesthesia in Stroke Patients During Thrombectomy","authors":"Fei Zhong MD ,&nbsp;Jian-yu Liu MD ,&nbsp;Yue Shi MD ,&nbsp;Da-zhong Zhang MD ,&nbsp;Song Ji MD","doi":"10.1016/j.acra.2024.06.030","DOIUrl":"10.1016/j.acra.2024.06.030","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The aim of this study was to develop and validate a nomogram<span><span> for predicting emergent conversion to general anaesthesia (GA) in stroke patients during </span>thrombectomy.</span></div></div><div><h3>Methods</h3><div>In this retrospective study, 458 patients (320 and 138 were randomised into the training and validation cohorts) were enroled. Univariable and multivariable logistic regression analyses were employed to identify risk factors for emergent conversion to GA. Subsequently, a nomogram was constructed based on the identified risk factors. The discriminative ability, calibration, and clinical utility of the nomogram were assessed in both the training and validation cohorts using receiver operating characteristic (ROC) curve analysis, Hosmer–Lemeshow test, and decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>The emergent conversion to GA occurred in 56 cases (12.2%). In the training cohort, four independent predictors of emergent conversion to GA were identified and incorporated into the nomogram: core infarct volume &gt; 70 mL, severe aphasia, severe cerebral vessel tortuosity, and vertebrobasilar occlusion. The ROC curves illustrated area under curve values of 0.931 (95% CI: 0.863–0.998) and 0.893 (95% CI: 0.852–0.935) for the training and validation cohorts, respectively. Hosmer–Lemeshow testing resulted in average absolute errors of 0.028 and 0.031 for the two cohorts. DCA demonstrated the nomogram’s exceptional utility and accuracy across a majority of threshold probabilities.</div></div><div><h3>Conclusion</h3><div>The constructed nomogram displayed promising predictive accuracy for emergent conversion to GA in stroke patients during thrombectomy, thereby providing potential assistance for clinical decision-making.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5175-5182"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535880","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 Best of Both Worlds: Ultra-high-pitch Pulmonary Angiography with Free-Breathing Technique by Means of Photon-Counting Detector CT for Diagnosis of Acute Pulmonary Embolism","authors":"Pauline Pannenbecker MD ,&nbsp;Julius F. Heidenreich MD ,&nbsp;Henner Huflage MD ,&nbsp;Philipp Gruschwitz MD ,&nbsp;Theresa S. Patzer MD ,&nbsp;Andreas M. Weng ,&nbsp;Jan-Peter Grunz MD ,&nbsp;Andreas S. Kunz MD ,&nbsp;Thorsten A. Bley MD ,&nbsp;Bernhard Petritsch MD","doi":"10.1016/j.acra.2024.06.028","DOIUrl":"10.1016/j.acra.2024.06.028","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To assess image quality and radiation dose of ultra-high-pitch CT pulmonary angiography (CTPA) with free-breathing technique for diagnosis of pulmonary embolism using a photon-counting detector (PCD) CT compared to matched energy-integrating detector (EID)-based single-energy CTPA.</div></div><div><h3>Materials and Methods</h3><div>Fifty-one PCD-CTPAs were prospectively compared to 51 CTPAs on a third-generation dual-source EID-CT. CTPAs were acquired with an ultra-high-pitch protocol with free-breathing technique (40 mL contrast medium, pitch 3.2) at 140 kV (PCD) and 70–100 kV (EID). Iodine maps were reconstructed from spectral PCD-CTPAs. Image quality of CTPAs and iodine maps was assessed independently by three radiologists. Additionally, CT attenuation numbers within pulmonary arteries as well as signal-to-noise and contrast-to-noise ratios (SNR, CNR) were compared. Administered radiation dose was compared.</div></div><div><h3>Results</h3><div>CT attenuation was higher in the PCD-group (all <em>P &lt;</em> 0.05). CNR and SNR were higher in lobar pulmonary arteries in PCD-CTPAs (<em>P &lt;</em> 0.05), whereas no difference was ascertained within the pulmonary trunk (<em>P &gt;</em> 0.05). Image quality of PCD-CTPA was rated best by all readers (excellent/good image quality in 96.1% of PCD-CTPAs vs. 50.9% of EID-CTPAs). PCD-CT produced no non-diagnostic scans vs. three non-diagnostic (5.9%) EID-CTPAs. Radiation dose was lower with PCD-CT than with EID-CT (effective dose 1.33 ± 0.47 vs. 1.80 ± 0.82 mSv; all <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Ultra-high-pitch CTPA with free-breathing technique with PCD-CT allows for superior image quality with significantly reduced radiation dose and full spectral information. With the ultra-high pitch, only PCD-CTPA enables reconstruction of iodine maps containing additional functional information.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5280-5288"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538893","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 and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer","authors":"Yue-yue Zhang ,&nbsp;Hui-min Mao ,&nbsp;Chao-gang Wei ,&nbsp;Tong Chen ,&nbsp;Wen-lu Zhao ,&nbsp;Liang-yan Chen ,&nbsp;Jun-kang Shen ,&nbsp;Wan-liang Guo","doi":"10.1016/j.acra.2024.07.013","DOIUrl":"10.1016/j.acra.2024.07.013","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div><span>Perineural invasion (PNI) is an important prognostic biomarker for </span>prostate cancer<span> (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa.</span></div></div><div><h3>Materials and Methods</h3><div><span>In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by </span>logistic regression<span>. Radiomics and deep learning methods were used to develop different MRI-based radiomics<span> and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision–recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model’s goodness of fit and clinical benefit.</span></span></div></div><div><h3>Results</h3><div>The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts.</div></div><div><h3>Conclusion</h3><div>The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5054-5065"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753288","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":"Special Report on the Consensus QIBA Profile for Objective Analytical Validation of Non-calcified and High-risk Plaque and Other Biomarkers using Computed Tomography Angiography","authors":"Andrew J. Buckler PhD ,&nbsp;Suhny Abbara MD ,&nbsp;Matthew J. Budoff MD ,&nbsp;John Jeffrey Carr MD MSc ,&nbsp;Carlo N. De Cecco MD PhD ,&nbsp;J. Kevin DeMarco MD ,&nbsp;Maros Ferencik MD PhD MCR ,&nbsp;Gemma A. Figtree MBBS(Hons), DPhil (Oxon) ,&nbsp;Ichiro Ikuta MD MMSc ,&nbsp;Márton Kolossváry MD PhD ,&nbsp;Mathis Konrad MSc ,&nbsp;Brajesh K. Lal MD ,&nbsp;Hugo Marques MD PhD ,&nbsp;Alastair J. Moss MD, PhD ,&nbsp;Nancy A. Obuchowski PhD ,&nbsp;Edwin J.R. van Beek MD PhD ,&nbsp;Renu Virmani MD ,&nbsp;Michelle C. Williams ,&nbsp;Luca Saba MD ,&nbsp;U. Joseph Schoepf MD","doi":"10.1016/j.acra.2024.07.014","DOIUrl":"10.1016/j.acra.2024.07.014","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available. Vendors use differing definitions, assessment metrics, and validation data sets to describe their offerings without clinician users having the capability to make objective assessments of accuracy and precision and how this affects diagnostic confidence.</div></div><div><h3>Materials and Methods</h3><div>The QIBA Profile for Atherosclerosis Biomarkers by CTA was created by the Quantitative Imaging Biomarkers Alliance (QIBA) to improve objectivity and decrease the variability of noninvasive plaque phenotyping. The Profile provides claims on the accuracy and precision of plaque measures individually and when combined.</div></div><div><h3>Results</h3><div>Individual plaque morphology measurements are evaluated in terms of bias (accuracy), slope (consistency of the bias across the measurement range, needed for measurements of change), and variability. The multiparametric plaque stability phenotype is evaluated in terms of agreement with expert pathologists. The Profile is intended for a broad audience, including those engaged in discovery science, clinical trials, and patient care.</div></div><div><h3>Conclusion</h3><div>This report provides a rationale and overview of the Profile claims and how to comply with the Profile in research and clinical practice.</div></div><div><h3>Summary Statement</h3><div>This article summarizes objective means to validate the analytical performance of non-calcified plaque (NCP), other emerging plaque morphology measurements, and multiparametric histology-defined high-risk plaque (HRP), as outlined in the QIBA Profile for Atherosclerosis Biomarkers by CTA.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4811-4820"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767968","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 Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging","authors":"Liting Shen ,&nbsp;Hui Xu ,&nbsp;Qian Liao ,&nbsp;Ying Yuan ,&nbsp;Dan Yu ,&nbsp;Jie Wei ,&nbsp;Zhenghan Yang ,&nbsp;Liang Wang","doi":"10.1016/j.acra.2024.06.048","DOIUrl":"10.1016/j.acra.2024.06.048","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS).</div></div><div><h3>Materials and Methods</h3><div>In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WI_PI, T2WI_ACS1 and T2WI_ACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 =<!--> <!--> non-diagnostic to 5 =<!--> <!--> excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P &lt; 0.05.</div></div><div><h3>Results</h3><div>This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P &lt; 0.05), significantly improving the image quality of sagittal and axial T2WI (P &lt; 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5022-5033"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789785","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":"Intersectionality and Faculty Compensation in Academic Radiology in U.S.","authors":"Ajay Malhotra MD, MMM, FACR ,&nbsp;Dheeman Futela MBBS ,&nbsp;Mihir Khunte BS ,&nbsp;Xiao Wu MD ,&nbsp;Seyedmehdi Payabvash MD ,&nbsp;Dheeraj Gandhi MD, FACR ,&nbsp;John E. Jordan MD, MPP, FACR","doi":"10.1016/j.acra.2024.07.021","DOIUrl":"10.1016/j.acra.2024.07.021","url":null,"abstract":"<div><h3>Background</h3><div>The impact of intersectionality on academic radiology physician compensation is not well known.</div></div><div><h3>Purpose</h3><div>The aim of this study was to assess impact of intersectionality on academic radiology financial compensation, based on rank, gender and race/ethnicity in US medical schools.</div></div><div><h3>Methods</h3><div>Data were collected from the AAMC Faculty Salary Survey, which collects information for full-time faculty at U.S. medical schools. Financial compensation data for radiology faculty with MD or equivalent degree in diagnostic radiology (DR) as well as interventional radiology (IR) was collected for 2023, stratified by rank, gender, and race/ethnicity.</div></div><div><h3>Results</h3><div>The AAMC Faculty Salary Survey data for 2023 included responses for 683 IR (138 women, 545 men) and 2431 DR (862 women, 1569 men) faculty. Men had a higher median compensation than women at all ranks, for both IR and DR, except DR instructors. The gender pay gap was greater in IR faculty compared to DR faculty of the same rank. All intersectional groups among IR faculty reported a lower median compensation compared to White men of the same rank. All intersectional groups among DR faculty, except Asian Men, had a lower median compensation than White men of the same rank. Among IR faculty, Asian women assistant professors faced the greatest disparity in median compensation, down to $75 K (15%) lower than White men. Among DR faculty, Black/African American women assistant professors faced the greatest disparity on median compensation, down to $48 K (10.5%) lower than White men.</div></div><div><h3>Conclusion</h3><div>The study results raise important concerns about impact of intersectionality on faculty compensation in radiology which needs further study and should be addressed as part of broader drive to increase diversity, equity, and inclusion in academic radiology.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5228-5231"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789788","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}