Radiology. Imaging cancer最新文献

{"title":"Mathematical 3D Liver Model for Surgical versus Ablative Therapy Treatment Planning for Colorectal Liver Metastases: Recommendations from the COLLISION and COLDFIRE Trial Expert Panels.","authors":"Bente A T van den Bemd, Robbert S Puijk, Han Keijzers, Petrousjka M van den Tol, Martijn R Meijerink","doi":"10.1148/rycan.240068","DOIUrl":"10.1148/rycan.240068","url":null,"abstract":"<p><p>Purpose To further define anatomic criteria for resection and ablation using an expert panel-based three-dimensional liver model to objectively predict local treatment recommendations for colorectal liver metastases (CRLM). Materials and Methods This study analyzed data from participants with small CRLM (≤3 cm) considered suitable for resection, thermal ablation, or irreversible electroporation (IRE), according to a multidisciplinary expert panel, who were included in two prospective multicenter trials (COLLISION [NCT03088150] and COLDFIRE-2 [NCT02082782]) between August 2017 and June 2022. Ten randomly selected participants were used to standardize the model's Couinaud segments. CRLM coordinates were measured and plotted in the model as color-coded lesions according to the treatment recommendations. Statistical validation was achieved through leave-one-out cross-validation. Results A total of 611 CRLM in 202 participants (mean age, 63 [range, 29-87] years; 138 male and 64 female) were included. Superficially located CRLM were considered suitable for resection, whereas more deep-seated CRLM were preferably ablated, with the transition zone at a subsurface depth of 3 cm. Ninety-three percent (25 of 27) of perihilar CRLM treated with IRE were at least partially located within 1 cm from the portal triad. Use of the model correctly predicted the preferred treatment in 313 of 424 CRLM (73.8%). Conclusion The results suggest that CRLM can be defined as superficial (preferably resected) and deep-seated (preferably ablated) if the tumor center is within versus beyond 3 cm from the liver surface, respectively, and as perihilar if the tumor margins extend to within 1 cm from the portal triad. <b>Keywords:</b> Ablation Techniques, CT, MRI, Liver, Abdomen/GI, Metastases, Oncology <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240068"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142473286","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}

{"title":"Exploring New Frontiers in Cryoablation and Immunotherapy Synergy.","authors":"Delmarie M Rivera Rodríguez, Samdeep K Mouli","doi":"10.1148/rycan.240384","DOIUrl":"10.1148/rycan.240384","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240384"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639664","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}

{"title":"Integrating Contrast-enhanced US to O-RADS US for Classification of Adnexal Lesions with Solid Components: Time-intensity Curve Analysis versus Visual Assessment.","authors":"Manli Wu, Ying Wang, Manting Su, Ruili Wang, Xiaofeng Sun, Rui Zhang, Liang Mu, Li Xiao, Hong Wen, Tingting Liu, Xiaotao Meng, Licong Huang, Xinling Zhang","doi":"10.1148/rycan.240024","DOIUrl":"10.1148/rycan.240024","url":null,"abstract":"<p><p>Purpose To compare the diagnostic performance of time-intensity curve (TIC) analysis and subjective visual assessment of contrast-enhanced US (CEUS) when integrated with the Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification system for characterizing adnexal lesions with solid components. Materials and Methods In this prospective multicenter study conducted from September 2021 to December 2022, female individuals with suspected adnexal lesions containing solid components detected at routine US were enrolled. All participants underwent preoperative CEUS examinations. Histopathologic findings were used as the reference standard for diagnosis. Lesions were classified according to the O-RADS US system. Enhancement of solid tissue compared with the outer myometrium was evaluated using both TIC analysis and subjective visual assessment. The diagnostic performance of O-RADS alone and each CEUS assessment method when integrated with the O-RADS US system was assessed and compared using receiver operating characteristic curve analysis. Results A total of 180 lesions (80 malignant and 100 benign histopathologic outcomes) in 175 participants (median age, 47 years [IQR, 33-56]) were analyzed. Incorporating CEUS (assessed through both TIC analysis and subjective visual assessment) with O-RADS US showed significantly improved diagnostic performance over O-RADS US alone, with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI: 0.80, 0.91) compared with 0.78 (95% CI: 0.71, 0.84). No evidence of a difference was observed between the AUCs of TIC analysis and subjective visual assessment in the enhancement evaluation of solid tissue with CEUS for adnexal malignancy categorization (<i>P</i> = .83). Conclusion Subjective visual assessment and TIC analysis of CEUS features when integrated with the O-RADS US scoring system showed comparable diagnostic performance in assigning adnexal malignancy risk. <b>Keywords:</b> Adnexal Lesions, Contrast-enhanced US, O-RADS, Time-intensity Curve Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240024"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401091","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}

{"title":"Racial Disparities in Preoperative Breast MRI Use and Surgical Margin Outcomes among Patients with Recently Diagnosed Breast Cancer.","authors":"Amber Simmons, Lynn K Han, Jeffrey S Reiner, Victoria L Mango, Varadan Sevilimedu, Katja Pinker, Hong Zhang, Tracy-Ann Moo, Sarah Eskreis-Winkler","doi":"10.1148/rycan.240010","DOIUrl":"10.1148/rycan.240010","url":null,"abstract":"<p><p>Purpose To evaluate racial disparities in preoperative breast MRI use and surgical margin outcomes among patients with recently diagnosed breast cancer. Materials and Methods This retrospective study included patients with breast cancer who presented to a single cancer center between 2008 and 2020, underwent breast surgery, and self-identified as White or Black. Patients were divided into MRI or no-MRI cohorts based on preoperative MRI use. MRI use and positive surgical margin rates were determined for all patients and racial subgroups. Data were collected from the electronic medical record and analyzed using the χ² test for significance. Results The study included 28 384 female patients (mean age, 56 years ± 13 [SD]) with recently diagnosed breast cancer who self-identified as White (90.36%, <i>n</i> = 25 647) or Black (9.64%, <i>n</i> = 2737). Of the entire cohort, 32.78% (9305 of 28 384) underwent preoperative MRI. Black patients had a lower rate of preoperative MRI use than White patients (29.85% [817 of 2737] vs 33.10% [8488 of 25 647], respectively; <i>P</i> < .001). The MRI cohort had a lower positive margin rate compared with the no-MRI cohort (2.90% [133 of 4592] vs 4.78% [539 of 11 271], respectively; <i>P</i> = .03). In the no-MRI cohort, Black patients had a higher positive margin rate than White patients (6.17% [68 of 1103] vs 4.63% [471 of 10 168], respectively; <i>P</i> = .03). In the MRI cohort, there was no evidence of a difference in positive margin rates between Black and White patients (3.17% [12 of 379] vs 2.87% [121 of 4213], respectively; <i>P</i> = .90). Conclusion Compared with White patients, Black patients with breast cancer were less likely to undergo preoperative MRI and had a higher positive surgical margin rate. <b>Keywords:</b> MR Imaging, Breast Cancer, Breast MRI © RSNA, 2024 See also the commentary by Tran and Miles in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240010"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606090","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}

{"title":"Multifrequency MR Elastography for Tumor Stiffness Outperforms Conventional Imaging and Clinical Variables in Determining Lymphovascular Space Invasion in Endometrial Cancer.","authors":"Mohammad Mirza-Aghazadeh-Attari, Erin N Gomez","doi":"10.1148/rycan.249022","DOIUrl":"10.1148/rycan.249022","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e249022"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401092","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}

{"title":"Nonpuerperal Uterine Inversion.","authors":"Baran Vardar, Alexandra Bercow, Alexander Melamed, Theodore Pierce","doi":"10.1148/rycan.240162","DOIUrl":"10.1148/rycan.240162","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240162"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142506718","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}

{"title":"Accuracy and Impact of AI Software for Nodule and Cancer Detection at Lung Cancer Screening CT.","authors":"Netanja I Harlianto, Pim A de Jong","doi":"10.1148/rycan.249025","DOIUrl":"10.1148/rycan.249025","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e249025"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639663","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}

{"title":"Factors Influencing Probably Benign (BI-RADS 3) Radiologist Assessment at Diagnostic Mammography.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.249021","DOIUrl":"10.1148/rycan.249021","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e249021"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352691","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}

{"title":"Interrater Reliability Assessment of the Brain Tumor Reporting and Data System (BT-RADS).","authors":"Netanja I Harlianto","doi":"10.1148/rycan.249016","DOIUrl":"10.1148/rycan.249016","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e249016"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036771","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}

{"title":"Artificial Intelligence versus Radiologists: A Comparative Analysis for Detecting Clinically Significant Prostate Cancer.","authors":"Radhika Rajeev, Hero K Hussain","doi":"10.1148/rycan.249020","DOIUrl":"10.1148/rycan.249020","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e249020"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293973","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}