Radiology最新文献

{"title":"Interventional Oncology Meets Immuno-oncology: Combination Therapies for Hepatocellular Carcinoma.","authors":"Ryan Bitar, Riad Salem, Richard Finn, Tim F Greten, S Nahum Goldberg, Julius Chapiro","doi":"10.1148/radiol.232875","DOIUrl":"10.1148/radiol.232875","url":null,"abstract":"<p><p>The management of hepatocellular carcinoma (HCC) is undergoing transformational changes due to the emergence of various novel immunotherapies and their combination with image-guided locoregional therapies. In this setting, immunotherapy is expected to become one of the standards of care in both neoadjuvant and adjuvant settings across all disease stages of HCC. Currently, more than 50 ongoing prospective clinical trials are investigating various end points for the combination of immunotherapy with both percutaneous and catheter-directed therapies. This review will outline essential tumor microenvironment mechanisms responsible for disease evolution and therapy resistance, discuss the rationale for combining locoregional therapy with immunotherapy, summarize ongoing clinical trials, and report on developing imaging end points and novel biomarkers that are relevant to both diagnostic and interventional radiologists participating in the management of HCC.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232875"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666697","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}

{"title":"2024 Manuscript Reviewers: A Note of Thanks.","authors":"Curtis P Langlotz, Linda Moy","doi":"10.1148/radiol.243207","DOIUrl":"https://doi.org/10.1148/radiol.243207","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e243207"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716984","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":"Flow versus Form: A New Approach to Assessing Dialysis Fistulas.","authors":"Martin R Prince, Thomas A Sos, Joshua L Weintraub","doi":"10.1148/radiol.242551","DOIUrl":"https://doi.org/10.1148/radiol.242551","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e242551"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716990","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":"Life Cycle Analysis and Sustainability Opportunities in Radiology.","authors":"James H Thrall","doi":"10.1148/radiol.243029","DOIUrl":"https://doi.org/10.1148/radiol.243029","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e243029"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716991","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":"Liquid Biopsy and Imaging Are Complementary for Assessing Tumor Burden.","authors":"Dow-Mu Koh","doi":"10.1148/radiol.242525","DOIUrl":"https://doi.org/10.1148/radiol.242525","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e242525"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716992","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":"Diagnostic Accuracy of Dual-Energy CT for Bone Stress Injury of the Lower Limb.","authors":"Giovanni Foti, Lorenza Sanfilippo, Chiara Longo, Eugenio Oliboni, Nicoletta De Santis, Venanzio Iacono, Gerardo Serra, Massimo Guerriero, Roberto Filippini","doi":"10.1148/radiol.232415","DOIUrl":"https://doi.org/10.1148/radiol.232415","url":null,"abstract":"<p><p>Background Because of its ability to help assess the presence of subtle morphologic changes in bone and bone marrow edema, dual-energy CT (DECT) could be an alternative to MRI in the diagnosis of bone stress injury that includes a stress fracture (SF) and stress reaction (SR). Purpose To determine the diagnostic accuracy of DECT in identifying bone stress injury of the lower limb using MRI as the reference standard. Materials and Methods This prospective study, conducted between June 2021 and January 2024, included consecutive patients clinically suspected of having stress injury (SF or SR) of the lower limb (leg, ankle, or foot). Imaging diagnosis was based on the absence or presence of cortical or periosteal changes, bone marrow edema, or a fracture line. The diagnostic performance of four blinded independent readers was determined for the entire cohort and for the subset of participants without fracture lines. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was evaluated with Kendall coefficient of concordance <i>(W)</i>. Results A total of 184 patients (mean age, 58 years ± 17 [SD]; 97 male) were enrolled. At MRI, 107 of 184 participants (58%) had positive diagnoses, including 70 with SF and 37 with SR. The mean overall sensitivity and specificity were 91% (390 of 428; 95% CI: 0.85, 0.95) and 93% (287 of 308; 95% CI: 0.87, 0.97), respectively, with an AUC of 0.94 (95% CI: 0.91, 0.97). Among patients without fracture lines (<i>n</i> = 114), the mean overall sensitivity and specificity of DECT were 79% (117 of 148; 95% CI: 0.65, 0.88) and 93% (287 of 308; 95% CI: 0.87, 0.97), respectively, with an AUC of 0.87 (95% CI: 0.81, 0.94). The interobserver agreement was very good for diagnosis of SF and SR combined (Kendall <i>W</i> = 0.90) and SR alone (Kendall <i>W</i> = 0.84). Conclusion DECT helped to accurately diagnose bone stress injury of the lower limb by identifying fracture lines and osseous stress reactions. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Breighner in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232415"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626434","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":"Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases.","authors":"Lama Dawi, Younes Belkouchi, Littisha Lawrance, Othilie Gautier, Samy Ammari, Damien Vasseur, Felix Wirth, Joya Hadchiti, Salome Morer, Clemence David, François Bidault, Corinne Balleyguier, Michèle Kind, Arnaud Bayle, Laila Belcaid, Mihaela Aldea, Claudio Nicotra, Arthur Geraud, Madona Sakkal, Felix Blanc-Durand, Sophie Moog, Maria Fernanda Mosele, Marco Tagliamento, Alice Bernard-Tessier, Benjamin Verret, Cristina Smolenschi, Nathalie Auger, Anas Gazzah, Jean-Baptiste Micol, Olivier Caron, Antoine Hollebecque, Yohann Loriot, Benjamin Besse, Ludovic Lacroix, Etienne Rouleau, Santiago Ponce, Fabrice André, Jean-Charles Soria, Fabrice Barlesi, Serge Muller, Paul-Henry Cournede, Hugues Talbot, Antoine Italiano, Nathalie Lassau","doi":"10.1148/radiol.232674","DOIUrl":"https://doi.org/10.1148/radiol.232674","url":null,"abstract":"<p><p>Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung (<i>n</i> = 20 334), lymph nodes (<i>n</i> = 11 651), and liver (<i>n</i> = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model (<i>R</i>² = 0.17; ρ = 0.41; <i>P</i> < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm³ for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm³. Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Koh in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232674"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716993","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":"Diagnostic Accuracy of CT for the Detection of Hepatic Steatosis: A Systematic Review and Meta-Analysis.","authors":"Maryam Haghshomar, Dominic Antonacci, Andrew D Smith, Sarang Thaker, Frank H Miller, Amir A Borhani","doi":"10.1148/radiol.241171","DOIUrl":"10.1148/radiol.241171","url":null,"abstract":"<p><p>Background CT plays an important role in the opportunistic identification of hepatic steatosis. CT performance for steatosis detection has been inconsistent across various studies, and no clear guidelines on optimum thresholds have been established. Purpose To conduct a systematic review and meta-analysis to assess CT diagnostic accuracy in hepatic steatosis detection and to determine reliable cutoffs for the commonly mentioned measures in the literature. Materials and Methods A systematic search of the PubMed, Embase, and Scopus databases (English-language studies published from September 1977 to January 2024) was performed. Studies evaluating the diagnostic accuracy of noncontrast CT (NCCT), contrast-enhanced (CECT), and dual-energy CT (DECT) for hepatic steatosis detection were included. Reference standards included biopsy, MRI proton density fat fraction (PDFF), or NCCT. In several CECT and DECT studies, NCCT was used as the reference standard, necessitating subgroup analysis. Statistical analysis included a random-effects meta-analysis, assessment of heterogeneity with use of the <i>I</i>² statistic, and meta-regression to explore potential sources of heterogeneity. When available, mean liver attenuation, liver-spleen attenuation difference, liver to spleen attenuation ratio, and the DECT-derived fat fraction for hepatic steatosis diagnosis were assessed. Results Forty-two studies (14 186 participants) were included. NCCT had a sensitivity and specificity of 72% and 88%, respectively, for steatosis (>5% fat at biopsy) detection and 82% and 94% for at least moderate steatosis (over 20%-33% fat at biopsy) detection. CECT had a sensitivity and specificity of 66% and 90% for steatosis detection and 68% and 93% for at least moderate steatosis detection. DECT had a sensitivity and specificity of 85% and 88% for steatosis detection. In the subgroup analysis, the sensitivity and specificity for detecting steatosis were 80% and 99% for CECT and 84% and 93% for DECT. There was heterogeneity among studies focusing on CECT and DECT. Liver attenuation less than 40-45 HU, liver-spleen attenuation difference less than -5 to 0 HU, and liver to spleen attenuation ratio less than 0.9-1 achieved high specificity for detection of at least moderate steatosis. Conclusion NCCT showed high performance for detection of at least moderate steatosis. © RSNA, 2024 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e241171"},"PeriodicalIF":2.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584105","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":"Deep Learning Algorithms for Breast Cancer Detection in a UK Screening Cohort: As Stand-alone Readers and Combined with Human Readers.","authors":"Sarah E Hickman, Nicholas R Payne, Richard T Black, Yuan Huang, Andrew N Priest, Sue Hudson, Bahman Kasmai, Arne Juette, Muzna Nanaa, Fiona J Gilbert","doi":"10.1148/radiol.233147","DOIUrl":"10.1148/radiol.233147","url":null,"abstract":"<p><p>Background Deep learning (DL) algorithms have shown promising results in mammographic screening either compared to a single reader or, when deployed in conjunction with a human reader, compared with double reading. Purpose To externally validate the performance of three DL algorithms as mammographic screen readers in an independent UK data set. Materials and Methods Three commercial DL algorithms (DL-1, DL-2, and DL-3) were retrospectively investigated from January 2022 to June 2022 using consecutive full-field digital mammograms collected at two UK sites during 1 year (2017). Normal cases with 3-year follow-up and histopathologically proven cancer cases detected either at screening (that round or next) or within the 3-year interval were included. A preset specificity threshold equivalent to a single reader was applied. Performance was evaluated for stand-alone DL reading compared with single human reading, and for DL reading combined with human reading compared with double reading, using sensitivity and specificity as the primary metrics. <i>P</i> < .025 was considered to indicate statistical significance for noninferiority testing. Results A total of 26 722 cases (median patient age, 59.0 years [IQR, 54.0-63.0 years]) with mammograms acquired using machines from two vendors were included. Cases included 332 screen-detected, 174 interval, and 254 next-round cancers. Two of three stand-alone DL algorithms achieved noninferior sensitivity (DL-1: 64.8%, <i>P</i> < .001; DL-2: 56.7%, <i>P</i> = .03; DL-3: 58.9%, <i>P</i> < .001) compared with the single first reader (62.8%), and specificity was noninferior for DL-1 (92.8%; <i>P</i> < .001) and DL-2 (96.8%; <i>P</i> < .001) and superior for DL-3 (97.9%; <i>P</i> < .001) compared with the single first reader (96.5%). Combining the DL algorithms with human readers achieved noninferior sensitivity (67.0%, 65.6%, and 65.4% for DL-1, DL-2, and DL-3, respectively; <i>P</i> < .001 for all) compared with double reading (67.4%), and superior specificity (97.4%, 97.6%, and 97.6%; <i>P</i> < .001 for all) compared with double reading (97.1%). Conclusion Use of stand-alone DL algorithms in combination with a human reader could maintain screening accuracy while reducing workload. Published under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e233147"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669065","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":"Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.","authors":"Drew Prinster, Amama Mahmood, Suchi Saria, Jean Jeudy, Cheng Ting Lin, Paul H Yi, Chien-Ming Huang","doi":"10.1148/radiol.233261","DOIUrl":"10.1148/radiol.233261","url":null,"abstract":"<p><p>Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic diagnostic decision-making. Purpose To test whether the type of AI explanation and the correctness and confidence level of AI advice impact physician diagnostic performance, perception of AI advice usefulness, and trust in AI advice for chest radiograph diagnosis. Materials and Methods A multicenter, prospective randomized study was conducted from April 2022 to September 2022. Two types of AI explanations prevalent in medical imaging-local (feature-based) explanations and global (prototype-based) explanations-were a between-participant factor, while AI correctness and confidence were within-participant factors. Radiologists (task experts) and internal or emergency medicine physicians (task nonexperts) received a chest radiograph to read; then, simulated AI advice was presented. Generalized linear mixed-effects models were used to analyze the effects of the experimental variables on diagnostic accuracy, efficiency, physician perception of AI usefulness, and \"simple trust\" (ie, speed of alignment with or divergence from AI advice); the control variables included knowledge of AI, demographic characteristics, and task expertise. Holm-Sidak corrections were used to adjust for multiple comparisons. Results Data from 220 physicians (median age, 30 years [IQR, 28-32.75 years]; 146 male participants) were analyzed. Compared with global AI explanations, local AI explanations yielded better physician diagnostic accuracy when the AI advice was correct (β = 0.86; <i>P</i> value adjusted for multiple comparisons [<i>P</i>_adj] < .001) and increased diagnostic efficiency overall by reducing the time spent considering AI advice (β = -0.19; <i>P</i>_adj = .01). While there were interaction effects of explanation type, AI confidence level, and physician task expertise on diagnostic accuracy (β = -1.05; <i>P</i>_adj = .04), there was no evidence that AI explanation type or AI confidence level significantly affected subjective measures (physician diagnostic confidence and perception of AI usefulness). Finally, radiologists and nonradiologists placed greater simple trust in local AI explanations than in global explanations, regardless of the correctness of the AI advice (β = 1.32; <i>P</i>_adj = .048). Conclusion The type of AI explanation impacted physician diagnostic performance and trust in AI, even when physicians themselves were not aware of such effects. © RSNA, 2024 <i>Supplemental material is available for this article</i>.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e233261"},"PeriodicalIF":12.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669064","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}