Academic Radiology最新文献

{"title":"Moral Distress and the Necessity of Purpose.","authors":"Benjamin R Gray, J Mark Mutz, Richard B Gunderman","doi":"10.1016/j.acra.2024.11.057","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.057","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807714","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":"Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.","authors":"Na Yeon Han, Keewon Shin, Min Ju Kim, Beom Jin Park, Ki Choon Sim, Yeo Eun Han, Deuk Jae Sung, Jae Woong Choi, Suk Keu Yeom","doi":"10.1016/j.acra.2024.10.050","DOIUrl":"https://doi.org/10.1016/j.acra.2024.10.050","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.</p><p><strong>Materials and methods: </strong>This study included 205 patients, each with two consecutive radiological reports. We designed a prompt comprising a three-step task to analyze report findings using LLMs. To establish a ground truth, two radiologists reached a consensus on a six-level categorization, comprising tumor findings (categorized as improved, stable, or aggravated), \"benign\", \"no tumor description,\" and \"other malignancy.\" The performance of GPT-4 and Gemini was then compared based on their ability to match corresponding findings between two radiological reports and accurately reflect these categories.</p><p><strong>Results: </strong>In terms of accuracy in matching findings between serial reports, the proportion of correctly matched findings was significantly higher for GPT-4 (96.2%) than for Gemini (91.7%) (P < 0.01). For oncological issue identification, the precision for tumor-related finding determinations, recall, and F1-scores were 0.68 and 0.63 (P = 0.006), 0.91 and 0.80 (P < 0.001), and 0.78 and 0.70 for GPT-4 and Gemini, respectively. GPT-4 was more accurate than Gemini in determining the correct tumor status for tumor-related findings (P < 0.001).</p><p><strong>Conclusion: </strong>This study demonstrated the potential of LLM-assisted analysis of serial radiology reports in enhancing oncological surveillance, using a carefully engineered prompt. GPT-4 showed superior performance compared to Gemini in matching corresponding findings, identifying tumor-related findings, and accurately determining tumor status.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807346","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":"Predicting Futile Recanalization by Cerebral Collateral Recycle Status in Patients with Endovascular Stroke Treatment: The CHANOA Score.","authors":"Chen Gong, Shuyu Jiang, Liping Huang, Zhiyuan Wang, Yankun Chen, Ziyang Huang, Jin Liu, Jinxian Yuan, You Wang, Siyin Gong, Shengli Chen, Yangmei Chen, Tao Xu","doi":"10.1016/j.acra.2024.11.032","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.032","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The correlation between collateral circulation and futile recanalization (FR) is still controversial, and few studies have explored the influence of comprehensive cerebral collateral circulation on FR after endovascular stroke treatment. Therefore, based on cerebral collateral recycle (CCR) status, we aimed to establish an effective scoring system to identify the probability of FR.</p><p><strong>Methods: </strong>This was a multicenter retrospective cohort study. FR was defined as a 90-day modified Rankin Scale (mRS) score of 3-6, despite having successful recanalization (modified Thrombolysis in Cerebral Infarction score of 2b-3). The discrimination and calibration of this score were assessed using the area under the receiver operator characteristic curve, calibration curve, and decision curve analysis.</p><p><strong>Results: </strong>Out of 860 patients receiving endovascular stroke treatment, 478 were enrolled in this study after strict screening. In multivariate regression analysis, the CCR status (poor CCR, adjusted OR[aOR] 9.99, 95%CI 5.11 to 17.06, P < 0.001; moderate CCR, aOR 2.94, 95%CI 1.71 -5.06, P < 0.001), age ≥ 80 years (aOR 3.77, P < 0.001), baseline NIHSS ≥ 15 (aOR 1.81, P = 0.018), baseline ASPECTS ≤ 6 (aOR 1.95, P = 0.006), the time from stroke onset to revascularization (OTR) ≥ 600 min (aOR 2.02, P = 0.007) and any intracranial hemorrhage within 48 h (aOR 3.54, P < 0.001) were significantly associated with FR. These factors make up the CCR-hemorrhage-age-NIHSS-OTR-ASPECTS (CHANOA) score. The CHANOA score demonstrated good discrimination and calibration in this cohort, as well as the fivefold cross validation.</p><p><strong>Conclusion: </strong>The CHANOA score reliably predicted FR in patients with endovascular stroke treatment, based on comprehensive cerebral collateral and clinical features.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807727","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":"Triple Rule Out CT in the Emergency Department: Clinical Risk and Outcomes (Triple Rule Out in the Emergency Department).","authors":"Philip A Araoz, Srikanth Gadam, Aditi K Bhanushali, Palak Sharma, Mansunderbir Singh, Aidan F Mullan, Jeremy D Collins, Phillip M Young, Stephen Kopecky, Casey M Clements","doi":"10.1016/j.acra.2024.10.051","DOIUrl":"https://doi.org/10.1016/j.acra.2024.10.051","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Triple rule out CT protocols (TRO-CT) have been advocated as a single test to simultaneously evaluate major causes of acute chest pain, in particular acute myocardial infarction (MI), acute pulmonary embolism (PE), and acute aortic syndrome. However, it is unclear what patient populations would benefit from a such comprehensive exam and current guidelines recommend tailoring CT protocols to the most likely diagnosis.</p><p><strong>Methods: </strong>We retrospectively reviewed TRO-CT scans performed from the Emergency Department (ED) at our institution from April 2021 to April 2022. Charts were reviewed to calculate clinical risk of MI, PE, and acute aortic syndrome using conventional clinical scoring systems (HEART score, PERC score, ADD-RS). TRO-CT findings and 30-day clinical outcomes were recorded from chart review.</p><p><strong>Results: </strong>1279 patients ED patients scanned with TRO-CT were included in the analysis. 831 patients (65.0%) were at-risk for two or more clinical risk scores. At TRO-CT, 381 (29.8%) patients had obstructive CAD. 91 (7.1%) had acute PE. 7 (0.5%) had acute aortic syndrome. At 30-day clinical follow up, 28 patients (2.2%) had the diagnosis of acute MI (95% CI: 1.5-3.2%). 90 patients (7.0%) had the diagnosis of acute PE (95% CI: 5.7-8.6%). 7 patients (0.5%) had the diagnosis acute aortic syndrome (95% CI: 0.2-1.2%). A low-risk HEART score was associated with a 0.3% 30-day clinical diagnosis of acute MI (95% CI: 0.0-1.6%). Low-risk-PERC was associated with a 2.9% 30-day clinical diagnosis of acute PE (95% CI: 0.7-8.7%). Low-risk ADD-RS was associated with a 0.3% 30-day clinical diagnosis of acute aortic syndrome (95% CI: 0.0-1.8%).</p><p><strong>Conclusions: </strong>We found a high clinical overlap in the presentation of acute MI, acute PE, and acute aortic syndrome based on clinical risk scores. Further studies will be needed to compare a TRO-CT algorithm to a standard-of-care algorithm in patients presenting to the ED.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808061","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":"Navigating Well-being in Radiology: Strategies, Challenges, and Opportunities Across Career Transitions.","authors":"Carlos Zamora","doi":"10.1016/j.acra.2024.11.066","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.066","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796510","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 Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with ⁶⁸Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.","authors":"Rongqin Fan, Xueqin Long, Xiaoliang Chen, Yanmei Wang, Demei Chen, Rui Zhou","doi":"10.1016/j.acra.2024.11.034","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.034","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to develop a radiomics model characterized by ⁶⁸Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). This study also investigated the impact of varying thresholds for maximum standardized uptake value (SUV_max) in semi-automatic delineation methods on the predictions of the model.</p><p><strong>Methods: </strong>This retrospective study included 84 HCC patients who underwent ⁶⁸Ga-FAPI PET and their MVI results were confirmed by histopathological examination. Volumes of interest (VOIs) for lesions were semi-automatically delineated with four thresholds of 30%, 40%, 50%, and 60% for SUV_max. Extracted shape features, first-, second- and higher-order features. Eight PET radiomics models for predicting MVI were constructed and tested.</p><p><strong>Results: </strong>In the testing set, the logistic regression (LR) model achieved the highest AUC values for three groups of 30%, 50%, and 60%, with values of 0.785, 0.896, and 0.859, respectively, while the random forest (RF) model in 40% group obtained the highest AUC value of 0.815. The LR model in 50% group and the extreme gradient boosting (XGBoost) model in 60% group achieved the highest accuracy, each at 87.5%. The highest sensitivity was observed in the support vector machine (SVM) model in 30% group, at 100%.</p><p><strong>Conclusion: </strong>The ⁶⁸Ga-FAPI PET radiomics model has high efficacy in predicting MVI in HCC, which is important for the development of HCC treatment plan and post-treatment evaluation. Different thresholds of SUV_max in semi-automatic delineation methods exert a degree of influence on performance of the radiomics model.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796527","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 for differentiating pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis.","authors":"Longjia Zhang, Boyu Diao, Zhiyao Fan, Hanxiang Zhan","doi":"10.1016/j.acra.2024.11.047","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.047","url":null,"abstract":"<p><strong>Background: </strong>As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross-sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN).</p><p><strong>Methods: </strong>This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis.</p><p><strong>Results: </strong>A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82-0.87) and 0.82(0.79-0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09-0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier.</p><p><strong>Conclusion: </strong>This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796514","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":"Treatment of Small Renal Masses: Should We Cut, Burn, or Freeze?","authors":"Mark E Lockhart","doi":"10.1016/j.acra.2024.11.070","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.070","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792770","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 in Gastric Cancer: Another Step Toward Personalized Medicine.","authors":"Shudong Hu, Wenzheng Lu","doi":"10.1016/j.acra.2024.11.069","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.069","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792766","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":"Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.","authors":"Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm, Pritam Mukherjee, Jianfei Liu, Ronald M Summers","doi":"10.1016/j.acra.2024.11.046","DOIUrl":"10.1016/j.acra.2024.11.046","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descriptions present in the DICOM headers arise due to myriad MRI scanners from various manufacturers used for imaging, wide variations in imaging practices across institutions, and technologist preferences. These conflicts affect the hanging protocol, which dictates the arrangement of sequences for the reading radiologist. At present, clinician supervision is necessary to ensure that the correct sequence is being read and used for diagnosis. This pilot work seeks to classify five different series in mpMRI studies acquired at the levels of the chest, abdomen, and pelvis.</p><p><strong>Materials and methods: </strong>First, 2D and 3D classification networks were compared using data acquired by Siemens scanners and the optimal network was identified. Then, its performance was analyzed when trained with different training data quantities. The out-of-distribution (OOD) robustness on data acquired by a Philips scanner was also measured. In addition, the effect of data augmentation on model training was studied. The model was also tested with smaller input volumes through downsampling or cropping. Finally, the model was trained on combined data from both Siemens and Philips scanners to bridge the performance gap between different scanners.</p><p><strong>Results: </strong>Among 2D and 3D networks of ResNet-50, ResNet-101, DenseNet- 121, and EfficientNet-BN0, the 3D DenseNet-121 ensemble achieved an F₁ score of 99.5% when tested on data from the Siemens scanners. The model performed well on OOD data from the Philips scanner and achieved an F₁ score of 86.5%. There was no statistically significant difference between the models trained with and without data augmentation, and between the models trained with original-sized input and with smaller-sized input. When training the model with combined data, the F₁ score improved to 98.8% for the Philips test set and 99.3% for the Siemens test set respectively.</p><p><strong>Conclusion: </strong>Our pilot work is useful for the classification of MRI sequences in studies acquired at the level of the chest, abdomen, and pelvis. It has the potential for robust automation of hanging protocols and the creation of large-scale data cohorts for pre-clinical research.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792758","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}