Radiology. Imaging cancer最新文献

{"title":"Erratum for: The Era of ChatGPT and Large Language Models: Can We Advance Patient-centered Communications Appropriately and Safely?","authors":"Wendy Tu, Bonnie N Joe","doi":"10.1148/rycan.249009","DOIUrl":"https://doi.org/10.1148/rycan.249009","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058171","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":"Prognostic Value of Preoperative MRI-derived 3D Quantitative Tumor Arterial Burden in Patients with Hepatocellular Carcinoma Receiving Transarterial Chemoembolization.","authors":"Gang Peng, Xiao-Yu Huang, Ya-Nan Wang, Xiao-Jing Cao, Xiang Zhou","doi":"10.1148/rycan.230167","DOIUrl":"10.1148/rycan.230167","url":null,"abstract":"<p><p>Purpose To investigate the association of tumor arterial burden (TAB) on preoperative MRI with transarterial chemoembolization refractoriness (TACER) and progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC). Materials and Methods This retrospective study included patients with HCC who underwent repeated transarterial chemoembolization (TACE) treatments between January 2013 and December 2020. HCC was confirmed with pathology or imaging, and patients with other tumors, lost follow-up, or with a combination of other treatments were excluded. TACER was defined as viable lesions of more than 50% or increase in tumor number after two or more consecutive TACE treatments, continuous elevation of tumor markers, extrahepatic spread, or vascular invasion. TAB assessed with preoperative MRI was divided into high and low groups according to the median. A Cox proportional hazards model was used to determine the predictors of TACER and PFS. Results A total of 355 patients (median age, 61 years [IQR, 54-67]; 306 [86.2%] men, 49 [13.8%] women) were included. During a median follow-up of 32.7 months, the high TAB group had significantly faster TACER and decreased PFS than the low TAB group (all log-rank <i>P</i> < .001). High TAB was the strongest independent predictor of TACER and PFS in multivariable Cox regression analyses (hazard ratio [HR], 2.23 [95% CI: 1.51, 3.29]; HR, 2.30 [95% CI: 1.61, 3.27], respectively), especially in patients with Barcelona Clinic Liver Cancer stage A or a single tumor. The restricted cubic spline plot demonstrated that the HR of TACER and PFS continuously increased with increasing TAB. Conclusion High preoperative TAB at MRI was a risk factor for faster refractoriness and progression in patients with HCC treated with TACE. <b>Keywords:</b> Interventional-Vascular, MR Angiography, Hepatocellular Carcinoma, Transarterial Chemoembolization, Progression-free Survival, MRI <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140851189","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":"Individual Participant Data Meta-Analyses for Diagnostic Accuracy Research: Challenges and Lessons Learned from the LI-RADS IPD Group.","authors":"Andreu F Costa, Matthew D F McInnes, Christian B van der Pol, Eric Lam, Haben Dawit, Jean-Paul Salameh, Brooke Levis, Mustafa R Bashir","doi":"10.1148/rycan.240015","DOIUrl":"10.1148/rycan.240015","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140851188","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":"Advances in Tumor Management: Harnessing the Potential of Histotripsy.","authors":"Yash Verma, Arosh S Perera Molligoda Arachchige","doi":"10.1148/rycan.230159","DOIUrl":"10.1148/rycan.230159","url":null,"abstract":"<p><p>Tissue ablation techniques have long been used in clinical settings to treat various oncologic diseases. However, many of these techniques are invasive and can cause substantial adverse effects. Histotripsy is a noninvasive, nonionizing, nonthermal tissue ablation technique that has the potential to replace surgical interventions in various clinical settings. Histotripsy works by delivering high-intensity focused ultrasound waves to target tissue. These waves create cavitation bubbles within tissues that rapidly expand and collapse, thereby mechanically fractionating the tissue into acellular debris that is subsequently absorbed by the body's immune system. Preclinical and clinical studies have demonstrated the efficacy of histotripsy in treating a range of diseases, including liver, pancreatic, renal, and prostate tumors. Safety outcomes of histotripsy have been generally favorable, with minimal adverse effects reported. However, further studies are needed to optimize the technique and understand its long-term effects. This review aims to discuss the importance of histotripsy as a noninvasive tissue ablation technique, the preclinical and clinical literature on histotripsy and its safety, and the potential applications of histotripsy in clinical practice. <b>Keywords:</b> Tumor Microenvironment, Ultrasound-High-Intensity Focused (HIFU), Ablation Techniques, Abdomen/GI, Genital/Reproductive, Nonthermal Tissue Ablation, Histotripsy, Clinical Trials, Preclinical Applications, Focused Ultrasound © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140864151","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":"Long-term Mammography Screening Trends and Predictors of Return to Screening after the COVID-19 Pandemic: Results from a Statewide Registry.","authors":"Brian L Sprague, Sarah A Nowak, Thomas P Ahern, Sally D Herschorn, Peter A Kaufman, Catherine Odde, Hannah Perry, Michelle M Sowden, Pamela M Vacek, Donald L Weaver","doi":"10.1148/rycan.230161","DOIUrl":"10.1148/rycan.230161","url":null,"abstract":"<p><p>Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022. Log-binomial regression models estimated associations of sociodemographic and risk characteristics with post-COVID-19 pandemic return to screening. Results The proportion of female individuals in Vermont aged 50-74 years with a screening mammogram obtained in the previous 2 years declined from a prepandemic level of 61.3% (95% CI: 61.1%, 61.6%) in 2019 to 56.0% (95% CI: 55.7%, 56.3%) in 2021 before rebounding to 60.7% (95% CI: 60.4%, 61.0%) in 2022. Screening adherence in 2022 remained substantially lower than that observed during the 2007-2010 apex of screening adherence (66.1%-67.0%). Joinpoint models estimated an annual percent change of -1.1% (95% CI: -1.5%, -0.8%) during 2010-2022. Among the cohort of 95 644 individuals screened during January 2018-March 2020, the probability of returning to screening during 2020-2022 varied by age (eg, risk ratio [RR] = 0.94 [95% CI: 0.93, 0.95] for age 40-44 vs age 60-64 years), race and ethnicity (RR = 0.84 [95% CI: 0.78, 0.90] for Black vs White individuals), education (RR = 0.84 [95% CI: 0.81, 0.86] for less than high school degree vs college degree), and by 5-year breast cancer risk (RR = 1.06 [95% CI: 1.04, 1.08] for very high vs average risk). Conclusion Despite a rebound to near prepandemic levels, Vermont mammography screening rates have steadily declined since 2010, with certain sociodemographic groups less likely to return to screening after the pandemic. <b>Keywords:</b> Mammography, Breast, Health Policy and Practice, Neoplasms-Primary, Epidemiology, Screening <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140852722","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":"Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.","authors":"Dogan S Polat, Son Nguyen, Paniz Karbasi, Keith Hulsey, Murat Can Cobanoglu, Liqiang Wang, Albert Montillo, Basak E Dogan","doi":"10.1148/rycan.230107","DOIUrl":"10.1148/rycan.230107","url":null,"abstract":"<p><p>Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. <b>Keywords:</b> MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140867681","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":"Theranostic Capacity of a Mucin 16-targeted Antibody for Ovarian Cancer.","authors":"Kel Vin Tan","doi":"10.1148/rycan.249013","DOIUrl":"10.1148/rycan.249013","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141180288","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":"The Era of ChatGPT and Large Language Models: Can We Advance Patient-centered Communications Appropriately and Safely?","authors":"Wendy Tu, Bonnie N. Joe","doi":"10.1148/rycan.240038","DOIUrl":"10.1148/rycan.240038","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140852406","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":"Prediction of Major Adverse Cardiovascular Events in Patients with Chest Pain Using Coronary Artery Calcium Score.","authors":"Lauren E Burkard-Mandel","doi":"10.1148/rycan.249008","DOIUrl":"10.1148/rycan.249008","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863773","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":"Revisiting the \"Puffed Cheek\" Technique: Advantages, Fallacies, and Potential Solutions.","authors":"Shehbaz Ansari, Surjith Vattoth, Eric R Basappa, Pokhraj Prakashchandra Suthar, Santhosh Gaddikeri, Miral D Jhaveri","doi":"10.1148/rycan.230211","DOIUrl":"10.1148/rycan.230211","url":null,"abstract":"<p><p>The \"puffed cheek\" technique is routinely performed during CT neck studies in patients with suspected oral cavity cancers. The insufflation of air within the oral vestibule helps in the detection of small buccal mucosal lesions, with better delineation of lesion origin, depth, and extent of spread. The pitfalls associated with this technique are often underrecognized and poorly understood. They can mimic actual lesions, forfeiting the technique's primary purpose. This review provides an overview of the puffed cheek technique and its associated pitfalls. These pitfalls include pneumoparotid, soft palate elevation that resembles a nasopharyngeal mass, various tongue displacements or distortions that obscure tongue lesions or mimic them, sublingual gland herniation, an apparent exacerbation of the airway edema, vocal cord adduction that hinders glottic evaluation, and false indications of osteochondronecrosis in laryngeal cartilage. Most stem from a common underlying mechanism of unintentional Valsalva maneuver engaged in by the patient while trying to perform a puffed cheek, creating a closed air column under positive pressure with resultant surrounding soft-tissue displacement. These pitfalls can thus be avoided by instructing the patient to maintain continuous nasal breathing while puffing out their cheek during image acquisition, preventing the formation of the closed air column. <b>Keywords:</b> CT, Head/Neck © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899379","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}