Radiology. Imaging cancer最新文献

{"title":"Thyroid Nodule Ablation: Ever Expanding Indications.","authors":"Salomao Faintuch, Barry A Sacks","doi":"10.1148/rycan.240423","DOIUrl":"10.1148/rycan.240423","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240423"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922686","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":"Gastric Cancer Metastasis Mimicking Thyroiditis.","authors":"Aws Kamona, Javad R Azadi","doi":"10.1148/rycan.240165","DOIUrl":"10.1148/rycan.240165","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240165"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819095","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":"¹⁸F-FLT PET and Blood-based Biomarkers for Identifying Gastrointestinal Graft versus Host Disease after Allogeneic Cell Transplantation.","authors":"Jennifer Holter-Chakrabarty, Lacey McNally, John Levine, James Ferrara, Sara K Vesely, Christopher G Kanakry, Tabitha Garwe, Zheng Han, Manu Pandey, Joshua Glover, Yuejin Wen, Ron Gress, Kirsten M Williams","doi":"10.1148/rycan.240096","DOIUrl":"10.1148/rycan.240096","url":null,"abstract":"<p><p>Purpose To determine whether fluorine 18 (¹⁸F) fluorothymidine (FLT) PET imaging alone or combined with Mount Sinai Acute GVHD International Consortium (MAGIC) biomarkers could help identify subclinical gastrointestinal graft versus host disease (GI-GVHD) by day 100 following hematopoietic stem cell transplantation (HSCT). Materials and Methods ¹⁸F-FLT PET imaging was analyzed in a prospective pilot study (ClinicalTrials.gov identifier no. NCT01338987) with a primary end point of engraftment for a planned secondary end point identifying GI-GVHD. Regions of interest (ROIs) in the colon (1 cm³), jejunum (1 cm³), and ileum (1 cm³) were drawn in the area of greatest signal intensity within each segment of the GI tract by using software. Standardized uptake values (SUVs) were captured on day 28 following transplantation, along with MAGIC serum biomarkers and MAGIC algorithm probability (MAP) scores using MAGIC serum biomarkers collected at days 28-35. Results Among 20 participants (median age, 33.85 years [IQR: 28.65-39.25 years]; 11 female, nine male), seven presented with clinically diagnosed GI-GVHD by 100 days. Increased SUV was observed throughout the GI tract, most predominantly in the jejunum. Maximum and mean SUV by day 100 were significantly elevated in those with GI-GVHD (maximum SUV, 4.81; mean SUV, 3.73; <i>n</i> = 7) compared with those without (maximum SUV, 3.99; mean SUV, 2.56). MAP score (<i>P</i> = .02) was associated with acute GVHD on day 28 but not on day 100. Spearman correlation between maximum SUV in the jejunum and MAP score was <i>r</i> = 0.65 (<i>P</i> = .002). Conclusion These data suggest that ¹⁸F-FLT PET may help identify acute GI-GVHD after HSCT and could inform location in areas difficult to biopsy. <b>Keywords:</b> Transplantation, PET/CT, Bone Marrow, Abdomen/GI ClinicalTrials.gov identifier: NCT01338987 © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240096"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819094","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":"Augmented Reality for Surgical Navigation: A Review of Advanced Needle Guidance Systems for Percutaneous Tumor Ablation.","authors":"Michael Evans, Saakhi Kang, Abubakr Bajaber, Kyle Gordon, Charles Martin","doi":"10.1148/rycan.230154","DOIUrl":"10.1148/rycan.230154","url":null,"abstract":"<p><p>Percutaneous tumor ablation has become a widely accepted and used treatment option for both soft and hard tissue malignancies. The current standard-of-care techniques for performing these minimally invasive procedures require providers to navigate a needle to their intended target using two-dimensional (2D) US or CT to obtain complete local response. These traditional image-guidance systems require operators to mentally transpose what is visualized on a 2D screen into the inherent three-dimensional (3D) context of human anatomy. Advanced navigation systems designed specifically for percutaneous needle-based procedures often fuse multiple imaging modalities to provide greater awareness and planned needle trajectories for the avoidance of critical structures. However, even many of these advanced systems still require mental transposition of anatomy from a 2D screen to human anatomy. Augmented reality (AR)-based systems have the potential to provide a 3D view of the patient's anatomy, eliminating the need for mental transposition by the operator. The purpose of this article is to review commercially available advanced percutaneous surgical navigation platforms and discuss the current state of AR-based navigation systems, including their potential benefits, challenges for adoption, and future developments. <b>Keywords:</b> Computer Applications-Virtual Imaging, Technology Assessment, Augmented Reality, Surgical Navigation, Percutaneous Ablation, Interventional Radiology ©RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e230154"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922676","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":"Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.","authors":"Christian Roest, Thomas C Kwee, Igle J de Jong, Ivo G Schoots, Pim van Leeuwen, Stijn W T P J Heijmink, Henk G van der Poel, Stefan J Fransen, Anindo Saha, Henkjan Huisman, Derya Yakar","doi":"10.1148/rycan.240078","DOIUrl":"10.1148/rycan.240078","url":null,"abstract":"<p><p>Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up). Internal and external testing was performed. The model's ability to predict progression to csPCa was assessed by Cox regression analyses. Predictive performance of the DL model up to 5 years after baseline MRI in comparison with the European Randomized Study of Screening for Prostate Cancer (ERSPC) future-risk calculator, Prostate Cancer Prevention Trial (PCPT) risk calculator, and Prostate Imaging Reporting and Data System (PI-RADS) was assessed using the Harrell C-index. Optimized follow-up intervals were derived from Kaplan-Meier curves. Results DL scores predicted csPCa progression (internal cohort: hazard ratio [HR], 1.97 [95% CI: 1.61, 2.41; <i>P</i> < .001]; external cohort: HR, 1.32 [95% CI: 1.14, 1.55; <i>P</i> < .001]). The model identified a subgroup of patients (approximately 20%) with risks for csPCa of 3% or less, 8% or less, and 18% or less after 1-, 2-, and 4-year follow-up, respectively. DL scores had a C-index of 0.68 (95% CI: 0.63, 0.74) at internal testing and 0.56 (95% CI: 0.51, 0.61) at external testing, outperforming ERSPC and PCPT (both <i>P</i> < .001) at internal testing. Conclusion The DL model accurately predicted PCa progression and provided improved risk estimations, demonstrating its ability to aid in personalized follow-up for low-risk PCa. <b>Keywords:</b> MRI, Prostate Cancer, Deep Learning <i>Supplemental material is available for this article.</i> ©RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240078"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142953970","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":"Halo Sign: Metastatic Cardiac Angiosarcoma Mimicking Fungal Infection.","authors":"Nikita Goyal, Zachary Ohs, Syed Muhammad Awais Bukhari, Amit Gupta","doi":"10.1148/rycan.240211","DOIUrl":"10.1148/rycan.240211","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240211"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010600","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":"Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment.","authors":"Aritrick Chatterjee, Ambereen N Yousuf, Roger Engelmann, Carla Harmath, Grace Lee, Milica Medved, Ernest B Jamison, Abel Lorente Campos, Batuhan Gundogdu, Glenn Gerber, Luke F Reynolds, Parth K Modi, Tatjana Antic, Mihai Giurcanu, Scott Eggener, Gregory S Karczmar, Aytekin Oto","doi":"10.1148/rycan.240156","DOIUrl":"10.1148/rycan.240156","url":null,"abstract":"<p><p>Purpose To evaluate the use of an automated hybrid multidimensional MRI (HM-MRI)-based tool to prospectively identify prostate cancer targets before MRI/US fusion biopsy in comparison with Prostate Imaging and Reporting Data System (PI-RADS)-based multiparametric MRI (mpMRI) evaluation by expert radiologists. Materials and Methods In this prospective clinical trial (ClinicalTrials.gov registration no. NCT03585660), 91 male participants (mean age, 65 years ± 8 [SD]) with known or suspected prostate cancer underwent 3-T MRI with a conventional mpMRI protocol and HM-MRI followed by subsequent biopsy between August 2018 and March 2023. Using the HM-MRI tool, tissue composition was calculated using a three-compartment model, and suspected prostate cancer regions with elevated epithelium (>40%) and reduced lumen (<20%) meeting the minimum size requirement of 25 mm² were identified. Up to two additional biopsy targets per participant were automatically selected with the HM-MRI tool in addition to the biopsy targets selected based on an expert radiologist's mpMRI interpretation (≥PI-RADS 3) using an MRI/US fusion biopsy device. Additional 12-core transrectal US-guided sextant random biopsy cores were also obtained. Detection of clinically significant prostate cancer (≥Gleason 3+4) was compared between HM-MRI and mpMRI by calculating area under the receiver operating characteristic curve and diagnostic accuracy metrics. Results The diagnostic performance of HM-MRI was either higher than mpMRI or showed no evidence of a difference when compared with mpMRI. On a per-participant basis, HM-MRI had significantly higher accuracy (55% vs 44%; <i>P</i> = .02) and specificity (36% vs 14%: <i>P</i> = .002) than mpMRI. On a per-lesion basis, HM-MRI had significantly higher accuracy (58% vs 39%; <i>P</i> < .001) and positive predictive value (31% vs 22%; <i>P</i> = .004) compared with mpMRI. Only one lesion was missed when using the combination of mpMRI and HM-MRI. On a per-sextant basis, HM-MRI showed significantly better performance than mpMRI for all metrics, including primary end points of the area under the receiver operating characteristic curve (0.76 vs 0.65; <i>P</i> < .001) and accuracy (83.9% vs 79.0%; <i>P</i> = .006). Conclusion This study demonstrates that HM-MRI has the potential to improve MRI/US fusion biopsy results for prostate cancer detection by providing complementary information to PI-RADS-based evaluation by expert radiologists. <b>Keywords:</b> Prostate Cancer, Hybrid Multidimensional MRI, Multiparametric MRI, PI-RADS Clinical trial registration no. NCT03585660 ©RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240156"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010604","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":"Reevaluating Pediatric Brain Tumor Perfusion Imaging.","authors":"Michelle Wegscheid","doi":"10.1148/rycan.259003","DOIUrl":"10.1148/rycan.259003","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e259003"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034065","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":"Comparison of ⁶⁸Ga-DOTANOC and ¹⁸F-FDOPA PET/CT for Detection of Recurrent or Metastatic Paragangliomas.","authors":"Linjie Bian, Junyan Xu, Panli Li, Liyan Bai, Shaoli Song","doi":"10.1148/rycan.240059","DOIUrl":"10.1148/rycan.240059","url":null,"abstract":"<p><p>Purpose To evaluate the diagnostic performance of gallium 68 (⁶⁸Ga)-DOTA-NaI3-octreotide (⁶⁸Ga-DOTANOC) and fluorine 18 (¹⁸F)-fluoro-l-3,4-dihydroxyphenylalanine (¹⁸F-FDOPA) PET/CT in detecting recurrent or metastatic paragangliomas. Materials and Methods This single-center retrospective study included patients with paragangliomas who underwent both ⁶⁸Ga-DOTANOC PET/CT and ¹⁸F-FDOPA PET/CT between August 2021 and December 2023. The diagnostic performance of these two tracers in detecting recurrent or metastatic tumors was compared using several metrics, including sensitivity, negative predictive value, and accuracy. Results This study included 36 patients (median age, 52 years [range, 14-78 years]; 16 female, 20 male). Of these, nine underwent initial ⁶⁸Ga-DOTANOC and ¹⁸F-FDOPA PET/CT examinations before treatment, and the remaining 27 underwent posttreatment examinations. Twenty-two of those 27 patients had recurrence or metastasis. According to lesion-level analysis, ⁶⁸Ga-DOTANOC had higher sensitivity, negative predictive value, and accuracy for diagnosis of bone metastases than did ¹⁸F-FDOPA PET/CT (97% vs 78% [<i>P</i> < .001], 85% vs 42% [<i>P</i> = .02], and 97% vs 81% [<i>P</i> < .001], respectively). ¹⁸F-FDOPA PET/CT had higher sensitivity, negative predictive value, and accuracy for the diagnosis of liver metastases than did ⁶⁸Ga-DOTANOC PET/CT (73% vs 15% [<i>P</i> < .001], 68% vs 41% [<i>P</i> = .04], and 83% vs 46% [<i>P</i> < .001], respectively). According to patient-level analysis, the sensitivity of ¹⁸F-FDOPA PET/CT for diagnosing liver metastases was higher than that of ⁶⁸Ga-DOTANOC PET/CT (88% vs 25%; <i>P</i> = .04). Conclusion In patients with recurrent or metastatic paragangliomas, ⁶⁸Ga-DOTANOC PET/CT showed better performance than ¹⁸F-FDOPA PET/CT in detecting bone metastases, and ¹⁸F-FDOPA PET/CT performed better in detecting liver metastases. <b>Keywords:</b> ⁶⁸Ga-DOTANOC, ¹⁸F-FDOPA, Pheochromocytoma, Paraganglioma Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240059"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786861","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":"Association of Clinical and US Features with Malignancy of Breast Tumors: Investigating Shear-Wave Elastography and Radiomics.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.249028","DOIUrl":"10.1148/rycan.249028","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e249028"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865323","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}