Felix J Dorfner, Liv Jürgensen, Leonhard Donle, Fares Al Mohamad, Tobias R Bodenmann, Mason C Cleveland, Felix Busch, Lisa C Adams, James Sato, Thomas Schultz, Albert E Kim, Jameson Merkow, Keno K Bressem, Christopher P Bridge
Merel Huisman, Felipe Kitamura, Tessa S Cook, Keith D Hentel, Jonathan Elias, George Shih, Linda Moy
{"title":"Pearls and Pitfalls for LLMs 2.0.","authors":"Merel Huisman, Felipe Kitamura, Tessa S Cook, Keith D Hentel, Jonathan Elias, George Shih, Linda Moy","doi":"10.1148/radiol.242512","DOIUrl":"10.1148/radiol.242512","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":12.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142522827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehran Baboli, Fuyixue Wang, Zijing Dong, Jorg Dietrich, Erik J Uhlmann, Tracy T Batchelor, Daniel P Cahill, Ovidiu C Andronesi
Background: MR spectroscopic imaging (MRSI) can be used to quantify an extended brain metabolic profile but is confounded by changes in tissue water levels due to disease.
Purpose: To develop a fast absolute quantification method for metabolite concentrations combining whole-brain MRSI with echo-planar time-resolved imaging (EPTI) relaxometry in individuals with glioma and healthy individuals.
Materials and methods: In this prospective study performed from August 2022 to August 2023, using internal water as concentration reference, the MRSI-EPTI quantification method was compared with the conventional method using population-average literature relaxation values. Healthy participants and participants with mutant IDH1 gliomas underwent imaging at 3 T with a 32-channel coil. Real-time navigated adiabatic spiral three-dimensional MRSI scans were acquired in approximately 8 minutes and reconstructed with a super-resolution pipeline to obtain brain metabolic images at 2.4-mm isotropic resolution. High-spatial-resolution multiparametric EPTI was performed in 3 minutes, with 1-mm isotropic resolution, to correct the relaxation and proton density of the water reference signal. Bland-Altman analysis and the Wilcoxon signed rank test were used to compare absolute quantifications from the proposed and conventional methods.
Results: Six healthy participants (four male; mean age, 37 years ± 11 [SD]) and nine participants with glioma (six male; mean age, 41 years ± 15; one with wild-type IDH1 and eight with mutant IDH1) were included. In healthy participants, there was good agreement (+4% bias) between metabolic concentrations derived using the two methods, with a CI of plus or minus 26%. In participants with glioma, there was large disagreement between the two methods (+39% bias) and a CI of plus or minus 55%. The proposed quantification method improved tumor contrast-to-noise ratio (median values) for total N-acetyl-aspartate (EPTI: 0.541 [95% CI: 0.217, 0.910]; conventional: 0.484 [95% CI: 0.199, 0.823]), total choline (EPTI: 1.053 [95% CI: 0.681, 1.713]; conventional: 0.940 [95% CI: 0.617, 1.295]), and total creatine (EPTI: 0.745 [95% CI: 0.628, 0.909]; conventional: 0.553 [95% CI: 0.444, 0.828]) (P = .03 for all).
{"title":"Transforming Lung Cancer Screening with AI: Comprehensive Evaluation and Personalized Medicine Prospects.","authors":"Masahiro Yanagawa,Akinori Hata","doi":"10.1148/radiol.242118","DOIUrl":"https://doi.org/10.1148/radiol.242118","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":19.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Arterial Tortuosity Syndrome.","authors":"Brecht Van Berkel, Vincent Sneyers","doi":"10.1148/radiol.240181","DOIUrl":"10.1148/radiol.240181","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":12.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142294078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Vesical Imaging Reporting and Data System for Contrast-enhanced US.","authors":"Glen R Morrell","doi":"10.1148/radiol.241666","DOIUrl":"https://doi.org/10.1148/radiol.241666","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":19.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lennart R Koetzier,Jie Wu,Domenico Mastrodicasa,Aline Lutz,Matthew Chung,W Adam Koszek,Jayanth Pratap,Akshay S Chaudhari,Pranav Rajpurkar,Matthew P Lungren,Martin J Willemink
Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development.
{"title":"Generating Synthetic Data for Medical Imaging.","authors":"Lennart R Koetzier,Jie Wu,Domenico Mastrodicasa,Aline Lutz,Matthew Chung,W Adam Koszek,Jayanth Pratap,Akshay S Chaudhari,Pranav Rajpurkar,Matthew P Lungren,Martin J Willemink","doi":"10.1148/radiol.232471","DOIUrl":"https://doi.org/10.1148/radiol.232471","url":null,"abstract":"Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development.","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":19.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Biomarkers for Personalized Neoadjuvant Therapy in Triple-Negative Breast Cancer: Moving Forward.","authors":"Gaiane M Rauch","doi":"10.1148/radiol.242011","DOIUrl":"10.1148/radiol.242011","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":12.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multimodal Models Are Still a Novice at Radiology Vision.","authors":"Francis Deng","doi":"10.1148/radiol.242286","DOIUrl":"10.1148/radiol.242286","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":null,"pages":null},"PeriodicalIF":12.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}