José Guilherme de Almeida, Nuno M Rodrigues, Ana Sofia Castro Verde, Ana Mascarenhas Gaivão, Carlos Bilreiro, Inês Santiago, Joana Ip, Sara Belião, Celso Matos, Sara Silva, Manolis Tsiknakis, Kostantinos Marias, Daniele Regge, Nikolaos Papanikolaou
{"title":"Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning-assisted Prostate Cancer Classification Using Multiparametric MRI.","authors":"José Guilherme de Almeida, Nuno M Rodrigues, Ana Sofia Castro Verde, Ana Mascarenhas Gaivão, Carlos Bilreiro, Inês Santiago, Joana Ip, Sara Belião, Celso Matos, Sara Silva, Manolis Tsiknakis, Kostantinos Marias, Daniele Regge, Nikolaos Papanikolaou","doi":"10.1148/ryai.230555","DOIUrl":"10.1148/ryai.230555","url":null,"abstract":"<p><p>Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC, and the full dataset)-affects model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC). The effect of clinical features (age, prostate-specific antigen level, Prostate Imaging Reporting and Data System score) on model performance was also evaluated. Results DL models were trained on 4328 bpMRI cases, and the best model achieved an AUC of 0.73 when trained and tested using data from all manufacturers. Held-out test set performance was higher when models trained with data from a manufacturer were tested on the same manufacturer (within- and between-manufacturer AUC differences of 0.05 on average, <i>P</i> < .001). The addition of clinical features did not improve performance (<i>P</i> = .24). Learning curve analyses showed that performance remained stable as training data increased. Analysis of DL features showed that scanner manufacturer and scanning protocol heavily influenced feature distributions. Conclusion In automated classification of PCa aggressiveness using bpMRI data, scanner manufacturer and ERC use had a major effect on DL model performance and features. <b>Keywords:</b> Convolutional Neural Network (CNN), Computer-aided Diagnosis (CAD), Computer Applications-General (Informatics), Oncology <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Suri and Hsu in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230555"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Natural Language Processing for Everyone.","authors":"Quirin D Strotzer","doi":"10.1148/ryai.250218","DOIUrl":"10.1148/ryai.250218","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250218"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.","authors":"Zhao Shi, Bin Hu, Mengjie Lu, Manting Zhang, Haiting Yang, Bo He, Jiyao Ma, Chunfeng Hu, Li Lu, Sheng Li, Shiyu Ren, Yonggao Zhang, Jun Li, Mayidili Nijiati, Jiake Dong, Hao Wang, Zhen Zhou, Fandong Zhang, Chengwei Pan, Yizhou Yu, Zijian Chen, Chang Sheng Zhou, Yongyue Wei, Junlin Zhou, Long Jiang Zhang","doi":"10.1148/ryai.240140","DOIUrl":"10.1148/ryai.240140","url":null,"abstract":"<p><p>Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was conducted from November 2022 to March 2023. A sham-AI model with near-zero sensitivity and similar specificity to a standard AI model was developed using 16 422 CT angiography examinations. Digital subtraction angiography-verified CT angiographic examinations from four hospitals were collected, half of which were processed by standard AI and the others by sham AI to generate sequence A; sequence B was generated in the reverse order. Twenty-eight radiologists from seven hospitals were randomly assigned to either sequence and then assigned to the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with standard-AI assistance, and radiologists with sham-AI assistance were compared using sensitivity and specificity, and radiologists' susceptibility to sham AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61.0 years [IQR, 52.0-67.0]; 199 male), 50 of whom had aneurysms. Standard AI and sham AI performed as expected (sensitivity, 96.0% vs 0.0%; specificity, 82.0% vs 76.0%). The differences in sensitivity and specificity between standard AI-assisted and sham AI-assisted readings were 20.7% (95% CI: 15.8, 25.5 [superiority]) and 0.0% (95% CI: -2.0, 2.0 [noninferiority]), respectively. The difference between sham AI-assisted readings and radiologists alone was -2.6% (95% CI: -3.8, -1.4 [noninferiority]) for both sensitivity and specificity. After sham-AI suggestions, 5.3% (44 of 823) of true-positive and 1.2% (seven of 577) of false-negative results of radiologists alone were changed. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed sham-AI model compared with their unassisted performance. <b>Keywords:</b> CT Angiography, Vascular, Intracranial Aneurysm, Sham AI <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Mayfield and Romero in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240140"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial Intelligence Is Brittle: We Need to Do Better.","authors":"Abhinav Suri, William Hsu","doi":"10.1148/ryai.250081","DOIUrl":"10.1148/ryai.250081","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250081"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marit A Martiniussen, Marthe Larsen, Tone Hovda, Merete U Kristiansen, Fredrik A Dahl, Line Eikvil, Olav Brautaset, Atle Bjørnerud, Vessela Kristensen, Marie B Bergan, Solveig Hofvind
{"title":"One System to Rule Them All? Task- and Data-specific Considerations for Automated Data Extraction.","authors":"Ali S Tejani, Andreas M Rauschecker","doi":"10.1148/ryai.250175","DOIUrl":"10.1148/ryai.250175","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250175"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eduardo Moreno Júdice de Mattos Farina, Paulo Eduardo de Aguiar Kuriki
{"title":"Seeing the Unseen: How Unsupervised Learning Can Predict Genetic Mutations from Radiologic Images.","authors":"Eduardo Moreno Júdice de Mattos Farina, Paulo Eduardo de Aguiar Kuriki","doi":"10.1148/ryai.250243","DOIUrl":"10.1148/ryai.250243","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 3","pages":"e250243"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}