Alexanderpayne1908
To compare the ability of 2-hydroxyglutarate (2HG)-to-lipid and lactate (2HG/[lipid + lactate]) ratio with the ability of 2HG concentration alone to predict the isocitrate dehydrogenase
mutation status in patients with glioma.
In this retrospective study, consecutive patients with histopathologically proven glioma were enrolled between July 2016 and February 2019. A total of 79 patients were enrolled (mean age, 44 years; 49 men). The 2HG concentration and other MR spectroscopic parameters were measured by single-voxel point-resolved spectroscopy before surgery. The diagnostic performance of the 2HG concentration and 2HG/(lipid + lactate) ratio were calculated. Internal validation was assessed by the bootstrap approach with 1000 bootstrap resamples. Differences in the predictive accuracy of 2HG/(lipid + lactate) ratio and 2HG concentration were determined by calculating the integrated discrimination improvement. The diagnostic accuracy (sensitivity, specificity, and area under the receiver operating chaer-grade glioma, the 2HG/(lipid + lactate) ratio and the 2HG concentration showed comparable diagnostic performance (
= .72).
The 2HG/(lipid + lactate) ratio is more accurate for predicting
mutation status in patients with glioma than the 2HG concentration alone.
Brain/Brain Stem, CNS, MR-Imaging, MR-Spectroscopy, Neoplasms-Primary, Neuro-Oncology© RSNA, 2020.
The 2HG/(lipid + lactate) ratio is more accurate for predicting IDH mutation status in patients with glioma than the 2HG concentration alone.Keywords Brain/Brain Stem, CNS, MR-Imaging, MR-Spectroscopy, Neoplasms-Primary, Neuro-Oncology© RSNA, 2020.
To investigate the prognostic value of differential enhancement on baseline dual-energy CT images in patients with treatment-naive pancreatic ductal adenocarcinoma (PDAC), with a focus on tumor-host interface characterization.
This was a retrospective, institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study of 158 consecutive adult patients (mean age, 68 years; age range, 40.9-88.9 years; 50% women) with histopathologically proven, treatment-naive PDAC, who had undergone multiphasic pancreatic dual-energy CT from December 2011 to March 2017. Regions of interest in tumor core, tumor border, pancreas border with tumor, nontumoral pancreas, and aorta were recorded on pancreatic parenchymal phase (PPP) dual-energy CT 70-keV, 52-keV, and iodine material density (MD) images, plus portal venous phase (PVP) conventional CT images. Enhancement gradient (delta) across the tumor-pancreas interface was calculated. Delta was evaluated combining the dual-energy CT valuescharacterization of PDAC borders is best achieved using iodine MD and lower-energy simulated monoenergetic images at pancreatic protocol dual-energy CT.Keywords Abdomen/GI, CT, CT-Dual Energy, CT-Quantitative, PancreasSupplemental material is available for this article.© RSNA, 2020.Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. read more Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting. Keywords Adults and Pediatrics, Brain/Brain Stem, CNS, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, Tumor Response Supplemental material is available for this article. link2 © RSNA, 2020.
The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic.
An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each stThere are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care.© 2020 RSNA; The American College of Chest Physicians, published by Elsevier Inc; and The American College of Radiology, published by Elsevier Inc.
There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care.© 2020 RSNA; The American College of Chest Physicians, published by Elsevier Inc; and The American College of Radiology, published by Elsevier Inc.
To determine the feasibility of texture analysis of apparent diffusion coefficient (ADC) maps and to assess the performance of texture analysis and ADC to predict histologic grade, parametrial invasion, lymph node metastasis, International Federation of Gynecology and Obstetrics (FIGO) stage, recurrence, and recurrence-free survival (RFS) in patients with cervical carcinoma.
This retrospective study included 58 patients with cervical carcinoma who were examined with a 1.5-T MRI system and diffusion-weighted imaging with
values of 0 and 1000 sec/mm
. Software with volumes of interest on ADC maps was used to extract 45 texture features, including higher-order texture features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance of ADC map random forest models and of ADC values. Dunnett test, Spearman rank correlation coefficient, Kaplan-Meier analyses, log-rank test, and Cox proportional hazards regression analyses were also used for statistical analyses.sion Weighted Imaging, MR-Imaging, Neoplasms-Primary, Pathology, Pelvis, Tissue Characterization, Uterus
© RSNA, 2020See also the commentary by Reinhold and Nougaret in this issue.
The ADC map random forest models were more useful for noninvasively evaluating histologic grade, parametrial invasion, lymph node metastasis, FIGO stage, and recurrence and for predicting RFS in patients with cervical carcinoma than were ADC values.Keywords Comparative Studies, Genital/Reproductive, MR-Diffusion Weighted Imaging, MR-Imaging, Neoplasms-Primary, Pathology, Pelvis, Tissue Characterization, UterusSupplemental material is available for this article.© RSNA, 2020See also the commentary by Reinhold and Nougaret in this issue.Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was perfore analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.Diagnosing cancer during early stages can substantially increase the cure rate, decrease the recurrence rate, and reduce health care costs. Over the past few decades, the continual development of new medical imaging modalities has been an important factor for diagnosing cancer, selecting therapies, and monitoring response to treatment. Photoacoustic tomography (PAT) is a hybrid imaging modality combining optical contrast from absorption of light with the outstanding spatiotemporal resolution of US imaging, providing biomedical morphologic and functional information of early-stage cancer. In this review, the basics and modalities of PAT, as well as a summary of its state-of-art applications in early-stage cancer (breast cancer, melanoma, and prostate cancer) detection and treatment guidance will be introduced. The potential clinical translation in cancer detection of PAT and prospects for the possibilities to lead to further clinical breakthroughs will also be discussed. Keywords Molecular Imaging-Cancer, Photoacoustic Imaging © RSNA, 2020.
To determine the diagnostic efficacy of gadolinium-based contrast agents for the detection of recurrent soft-tissue sarcoma compared with non-contrast-enhanced conventional MRI sequences.
A retrospective study of patients with soft-tissue sarcomas who were imaged from January 2009 to December 2014 was performed. MRI studies from 69 patients (mean age, 61 years ± 15 [standard deviation], 45 men) with recurrent soft-tissue sarcoma and 63 age-, sex-, and tumor-matched controls with positive findings (nonrecurrence) were presented to six musculoskeletal radiologists at a tertiary cancer center in three image groupings. Group 1 consisted of precontrast T1-weighted and fat-suppressed T2-weighted images (no contrast agent). Group 2 consisted of precontrast and postcontrast fat-saturated T1-weighted images. link3 Group 3 consisted of precontrast and fat-saturated postcontrast T1- and fat-suppressed T2-weighted images. Images within these three groups contained either recurrent soft-tissue sarcomas or positive postoperative findings (nonsarcoma).
