Radiology. Imaging cancer最新文献

{"title":"Breast Cancer Detection Using a Low-Dose Positron Emission Digital Mammography System.","authors":"Vivianne Freitas, Xuan Li, Anabel Scaranelo, Frederick Au, Supriya Kulkarni, Sandeep Ghai, Samira Taeb, Oleksandr Bubon, Brandon Baldassi, Borys Komarov, Shayna Parker, Craig A Macsemchuk, Michael Waterston, Kenneth O Olsen, Alla Reznik","doi":"10.1148/rycan.230020","DOIUrl":"10.1148/rycan.230020","url":null,"abstract":"<p><p>Purpose To investigate the feasibility of low-dose positron emission mammography (PEM) concurrently to MRI to identify breast cancer and determine its local extent. Materials and Methods In this research ethics board-approved prospective study, participants newly diagnosed with breast cancer with concurrent breast MRI acquisitions were assigned independently of breast density, tumor size, and histopathologic cancer subtype to undergo low-dose PEM with up to 185 MBq of fluorine 18-labeled fluorodeoxyglucose (¹⁸F-FDG). Two breast radiologists, unaware of the cancer location, reviewed PEM images taken 1 and 4 hours following ¹⁸F-FDG injection. Findings were correlated with histopathologic results. Detection accuracy and participant details were examined using logistic regression and summary statistics, and a comparative analysis assessed the efficacy of PEM and MRI additional lesions detection (ClinicalTrials.gov: NCT03520218). Results Twenty-five female participants (median age, 52 years; range, 32-85 years) comprised the cohort. Twenty-four of 25 (96%) cancers (19 invasive cancers and five in situ diseases) were identified with PEM from 100 sets of bilateral images, showcasing comparable performance even after 3 hours of radiotracer uptake. The median invasive cancer size was 31 mm (range, 10-120). Three additional in situ grade 2 lesions were missed at PEM. While not significant, PEM detected fewer false-positive additional lesions compared with MRI (one of six [16%] vs eight of 13 [62%]; <i>P</i> = .14). Conclusion This study suggests the feasibility of a low-dose PEM system in helping to detect invasive breast cancer. Though large-scale clinical trials are essential to confirm these preliminary results, this study underscores the potential of this low-dose PEM system as a promising imaging tool in breast cancer diagnosis. ClinicalTrials.gov registration no. NCT03520218 <b>Keywords:</b> Positron Emission Digital Mammography, Invasive Breast Cancer, Oncology, MRI <i>Supplemental material is available for this article.</i> © RSNA, 2024 See also commentary by Barreto and Rapelyea in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 2","pages":"e230020"},"PeriodicalIF":5.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139707638","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":"Primary Osteosarcoma of the Sternum with Lung Metastases.","authors":"Anitha Mandava, Sanath Kandem, Rakesh Juluri, Arvind K Reddy, Veeraiah Koppula","doi":"10.1148/rycan.230199","DOIUrl":"10.1148/rycan.230199","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 2","pages":"e230199"},"PeriodicalIF":5.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139932639","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":"Calvarial Epithelioid Hemangioendothelioma Mimicking Osteosarcoma.","authors":"Yashaswi Singh, Amit Gupta, Asit Ranjan Mridha, Krithika Rangarajan","doi":"10.1148/rycan.230198","DOIUrl":"10.1148/rycan.230198","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 2","pages":"e230198"},"PeriodicalIF":5.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140060370","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":"Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.","authors":"Wen Li, Savannah C Partridge, David C Newitt, Jon Steingrimsson, Helga S Marques, Patrick J Bolan, Michael Hirano, Benjamin Aaron Bearce, Jayashree Kalpathy-Cramer, Michael A Boss, Xinzhi Teng, Jiang Zhang, Jing Cai, Despina Kontos, Eric A Cohen, Walter C Mankowski, Michael Liu, Richard Ha, Oscar J Pellicer-Valero, Klaus Maier-Hein, Simona Rabinovici-Cohen, Tal Tlusty, Michal Ozery-Flato, Vishwa S Parekh, Michael A Jacobs, Ran Yan, Kyunghyun Sung, Anum S Kazerouni, Julie C DiCarlo, Thomas E Yankeelov, Thomas L Chenevert, Nola M Hylton","doi":"10.1148/rycan.230033","DOIUrl":"10.1148/rycan.230033","url":null,"abstract":"<p><p>Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 <b>Keywords:</b> MRI, Breast, Tumor Response <i>Supplemental material is available for this article</i>. © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e230033"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139098469","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":"Imaging of the Posttreatment Head and Neck: Expected Findings and Potential Complications.","authors":"Sneh Brahmbhatt, Cameron J Overfield, Patricia A Rhyner, Alok A Bhatt","doi":"10.1148/rycan.230155","DOIUrl":"10.1148/rycan.230155","url":null,"abstract":"<p><p>Interpretation of posttreatment imaging findings in patients with head and neck cancer can pose a substantial challenge. Malignancies in this region are often managed through surgery, radiation therapy, chemotherapy, and newer approaches like immunotherapy. After treatment, patients may experience various expected changes, including mucositis, soft-tissue inflammation, laryngeal edema, and salivary gland inflammation. Imaging techniques such as CT, MRI, and PET scans help differentiate these changes from tumor recurrence. Complications such as osteoradionecrosis, chondroradionecrosis, and radiation-induced vasculopathy can arise because of radiation effects. Radiation-induced malignancies may occur in the delayed setting. This review article emphasizes the importance of posttreatment surveillance imaging to ensure proper care of patients with head and neck cancer and highlights the complexities in distinguishing between expected treatment effects and potential complications. <b>Keywords:</b> CT, MR Imaging, Radiation Therapy, Ear/Nose/Throat, Head/Neck, Nervous-Peripheral, Bone Marrow, Calvarium, Carotid Arteries, Jaw, Face, Larynx © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e230155"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564814","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 Evolution of AI in Predicting Response to Minimally Invasive Image-guided Therapies.","authors":"Nariman Nezami, Mohammad Mirza-Aghazadeh-Attari","doi":"10.1148/rycan.249004","DOIUrl":"10.1148/rycan.249004","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e249004"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564830","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":"Post-Renal Transplant Lymphoproliferative Disorder.","authors":"Yash Jain, Nilendu Purandare, Archi Agrawal, Sneha Shah, Ameya Puranik, Venkatesh Rangarajan","doi":"10.1148/rycan.230075","DOIUrl":"10.1148/rycan.230075","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e230075"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807222","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":"White Fat Uptake: A Rare Confounder of Pediatric ¹⁸F-FDG PET/CT.","authors":"Lance Zimmerman, Hassan Aboughalia","doi":"10.1148/rycan.230148","DOIUrl":"10.1148/rycan.230148","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e230148"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139098470","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":"Carcinoid Heart Disease.","authors":"Sean Johnson, Matthias R Benz, Kathleen Ruchalski","doi":"10.1148/rycan.230164","DOIUrl":"10.1148/rycan.230164","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e230164"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564798","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":"Whole-Brain Intracellular pH Mapping of Gliomas Using High-Resolution ³¹P MR Spectroscopic Imaging at 7.0 T.","authors":"Daniel Paech, Nina Weckesser, Vanessa L Franke, Johannes Breitling, Steffen Görke, Katerina Deike-Hofmann, Antje Wick, Moritz Scherer, Andreas Unterberg, Wolfgang Wick, Martin Bendszus, Peter Bachert, Mark E Ladd, Heinz-Peter Schlemmer, Andreas Korzowski","doi":"10.1148/rycan.220127","DOIUrl":"10.1148/rycan.220127","url":null,"abstract":"<p><p>Malignant tumors commonly exhibit a reversed pH gradient compared with normal tissue, with a more acidic extracellular pH and an alkaline intracellular pH (pH_i). In this prospective study, pH_i values in gliomas were quantified using high-resolution phosphorous 31 (³¹P) spectroscopic MRI at 7.0 T and were used to correlate pH_i alterations with histopathologic findings. A total of 12 participants (mean age, 58 years ± 18 [SD]; seven male, five female) with histopathologically proven, newly diagnosed glioma were included between September 2018 and November 2019. The ³¹P spectroscopic MRI scans were acquired using a double-resonant ³¹P/¹H phased-array head coil together with a three-dimensional (3D) ³¹P chemical shift imaging sequence (5.7-mL voxel volume) performed with a 7.0-T whole-body system. The 3D volumetric segmentations were performed for the whole-tumor volumes (WTVs); tumor subcompartments of necrosis, gadolinium enhancement, and nonenhancing T2 (NCE T2) hyperintensity; and normal-appearing white matter (NAWM), and pH_i values were compared. Spearman correlation was used to assess association between pH_i and the proliferation index Ki-67. For all study participants, mean pH_i values were higher in the WTV (7.057 ± 0.024) compared with NAWM (7.006 ± 0.012; <i>P</i> < .001). In eight participants with high-grade gliomas, pH_i was increased in all tumor subcompartments (necrosis, 7.075 ± 0.033; gadolinium enhancement, 7.075 ± 0.024; NCE T2 hyperintensity, 7.043 ± 0.015) compared with NAWM (7.004 ± 0.014; all <i>P</i> < .01). The pH_i values of WTV positively correlated with Ki-67 (<i>R</i>² = 0.74, <i>r</i> = 0.78, <i>P</i> = .001). In conclusion, ³¹P spectroscopic MRI at 7.0 T enabled high-resolution quantification of pH_i in gliomas, with pH_i alteration associated with the Ki-67 proliferation index, and may aid in diagnosis and treatment monitoring. <b>Keywords:</b> ³¹P MRSI, pH, Glioma, Glioblastoma, Ultra-High-Field MRI, Imaging Biomarker, 7 Tesla <i>Supplemental material is available for this article.</i> © RSNA, 2023.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 1","pages":"e220127"},"PeriodicalIF":5.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138831228","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}