Radiology. Imaging cancer最新文献

{"title":"Fluorine 18-labeled Somatostatin Analog Improves Detection of Neuroendocrine Tumors.","authors":"Schuyler Karl, Gary D Luker","doi":"10.1148/rycan.239016","DOIUrl":"10.1148/rycan.239016","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546356/pdf/rycan.239016.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328028","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":"Do Imaging Core Laboratories Better Predict Progression Over Clinical Interpretations?","authors":"Daniel J A Margolis, Kathleen L Ruchalski","doi":"10.1148/rycan.230092","DOIUrl":"10.1148/rycan.230092","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546355/pdf/rycan.230092.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9971744","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":"Hyperpolarized Carbon 13 MRI: Clinical Applications and Future Directions in Oncology.","authors":"Surrin S Deen, Catriona Rooney, Ayaka Shinozaki, Jordan McGing, James T Grist, Damian J Tyler, Eva Serrão, Ferdia A Gallagher","doi":"10.1148/rycan.230005","DOIUrl":"10.1148/rycan.230005","url":null,"abstract":"<p><p>Hyperpolarized carbon 13 MRI (¹³C MRI) is a novel imaging approach that can noninvasively probe tissue metabolism in both normal and pathologic tissues. The process of hyperpolarization increases the signal acquired by several orders of magnitude, allowing injected ¹³C-labeled molecules and their downstream metabolites to be imaged in vivo, thus providing real-time information on kinetics. To date, the most important reaction studied with hyperpolarized ¹³C MRI is exchange of the hyperpolarized ¹³C signal from injected [1-¹³C]pyruvate with the resident tissue lactate pool. Recent preclinical and human studies have shown the role of several biologic factors such as the lactate dehydrogenase enzyme, pyruvate transporter expression, and tissue hypoxia in generating the MRI signal from this reaction. Potential clinical applications of hyperpolarized ¹³C MRI in oncology include using metabolism to stratify tumors by grade, selecting therapeutic pathways based on tumor metabolic profiles, and detecting early treatment response through the imaging of shifts in metabolism that precede tumor structural changes. This review summarizes the foundations of hyperpolarized ¹³C MRI, presents key findings from human cancer studies, and explores the future clinical directions of the technique in oncology. <b>Keywords:</b> Hyperpolarized Carbon 13 MRI, Molecular Imaging, Cancer, Tissue Metabolism © RSNA, 2023.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546364/pdf/rycan.230005.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10596290","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":"One Size Fits All?-Not Anymore: Personalizing Breast Cancer Treatment with Use of a Semiautomated Functional Tumor Volume-based Predictive Model in the Assessment of Neoadjuvant Therapy Response.","authors":"Shruthi Ram","doi":"10.1148/rycan.230089","DOIUrl":"https://doi.org/10.1148/rycan.230089","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413299/pdf/rycan.230089.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9979994","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":"Cutaneous Metastasis of Prostate Adenocarcinoma.","authors":"Iván Vega-González, Lina María López Vélez, Tatiana Cadavid, Juan Carlos Ramírez Fontalvo, Juan Carlos Ramírez Yepes","doi":"10.1148/rycan.230037","DOIUrl":"10.1148/rycan.230037","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413290/pdf/rycan.230037.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10348867","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":"Intense Brown Fat Uptake at FDG PET/CT Induced by Mirabegron.","authors":"Pokhraj Prakashchandra Suthar,&nbsp;Sumeet Virmani","doi":"10.1148/rycan.230055","DOIUrl":"https://doi.org/10.1148/rycan.230055","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413286/pdf/rycan.230055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10348897","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":"Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment.","authors":"Natsuko Onishi, Teffany Joy Bareng, Jessica Gibbs, Wen Li, Elissa R Price, Bonnie N Joe, John Kornak, Laura J Esserman, David C Newitt, Nola M Hylton","doi":"10.1148/rycan.220126","DOIUrl":"10.1148/rycan.220126","url":null,"abstract":"<p><p>Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (<i>P</i> = .03 and <i>P</i> = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. <b>Keywords:</b> Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 <i>Supplemental material is available for this article.</i> © RSNA, 2023 See also the commentary by Ram in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413289/pdf/rycan.220126.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10348927","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":"Adaptive Radiation Therapy: A Review of CT-based Techniques.","authors":"Elizaveta Lavrova, Matthew D Garrett, Yi-Fang Wang, Christine Chin, Carl Elliston, Michelle Savacool, Michael Price, Lisa A Kachnic, David P Horowitz","doi":"10.1148/rycan.230011","DOIUrl":"10.1148/rycan.230011","url":null,"abstract":"<p><p>Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. <b>Keywords:</b> Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413297/pdf/rycan.230011.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10330107","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":"Updated Recommendations by the American College of Radiology for Breast Cancer Screening in Individuals at Higher-Than-Average Risk.","authors":"Lauren Ton,&nbsp;Maggie Chung","doi":"10.1148/rycan.239015","DOIUrl":"https://doi.org/10.1148/rycan.239015","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413292/pdf/rycan.239015.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9979990","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":"A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.","authors":"Ken-Pin Hwang,&nbsp;Nabil A Elshafeey,&nbsp;Aikaterini Kotrotsou,&nbsp;Huiqin Chen,&nbsp;Jong Bum Son,&nbsp;Medine Boge,&nbsp;Rania M Mohamed,&nbsp;Abeer H Abdelhafez,&nbsp;Beatriz E Adrada,&nbsp;Bikash Panthi,&nbsp;Jia Sun,&nbsp;Benjamin C Musall,&nbsp;Shu Zhang,&nbsp;Rosalind P Candelaria,&nbsp;Jason B White,&nbsp;Elizabeth E Ravenberg,&nbsp;Debu Tripathy,&nbsp;Clinton Yam,&nbsp;Jennifer K Litton,&nbsp;Lei Huo,&nbsp;Alastair M Thompson,&nbsp;Peng Wei,&nbsp;Wei T Yang,&nbsp;Mark D Pagel,&nbsp;Jingfei Ma,&nbsp;Gaiane M Rauch","doi":"10.1148/rycan.230009","DOIUrl":"https://doi.org/10.1148/rycan.230009","url":null,"abstract":"<p><p>Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. <b>Keywords:</b> MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 <i>Supplemental material is available for this article.</i> © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413296/pdf/rycan.230009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9975056","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}