ObjectivesTo determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying “triple-negative" breast cancers.Materials and MethodsIn this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.ResultsImaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.ConclusionsConsidering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.
Purpose To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (i.e., luminal A/B or basal) of breast cancer. Materials and Methods 84 patients from one institution and 126 patients from the cancer genome atlas (TCGA) were used for discovery and external validation, respectively. 35 quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control false discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. Results On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus non-luminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P<0.05, FDR<0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. Conclusion DCE MR imaging characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.
Immunoglobulin G4 (IgG4)-related disease can affect the cardiovascular system, including the coronary arteries and pericardium and especially the walls of large and medium-sized vessels. The presence of coronary involvement is critical, as this condition can cause myocardial ischemia or sudden cardiac death. Although histopathologic examination remains the reference standard for detecting organ involvement and diagnosing IgG4-related disease, obtaining biopsy or surgical specimens from the vessel wall is still challenging. Because patients may be only mildly symptomatic, noninvasive imaging evaluation of IgG4-related cardiovascular disease (CVD) has an essential role in not only the diagnosis but also the management of this condition. Multidetector CT is a useful noninvasive examination for establishing the primary diagnosis and defining anatomic landmarks and their relationships. The spectrum of vessel involvement is vast, with varied manifestations. Radiologists should be familiar with inflammatory vasculitis, aneurysmal change, and pseudotumor formation in all vessels and the distribution of these conditions throughout the body. Electrocardiographically gated CT enables accurate, fast, and noninvasive characterization of coronary pathologic conditions and thus has an important advantage over catheter angiography. Combined PET/CT can depict inflammatory processes and help distinguish IgG4-related CVD from atherosclerosis. Familiarity with the PET/CT and CT findings of inflammatory processes involved in IgG4-related CVD is important for accurate diagnosis and evaluation of therapeutic response during follow-up. The multidetector CT and PET/CT characteristics of IgG4-related CVD, such as aortitis, periaortitis, arteritis, and periarteritis and including coronary artery involvement and pericarditis, are reviewed. In addition, the inflammatory process, quantification of active inflammation, and therapeutic response during follow-up associated with IgG4-related CVD are described.Abbreviations: CVD = cardiovascular disease, ECG = electrocardiography, FDG = fluorine 18 fluorodeoxyglucose, IgG4 = immunoglobulin G4, RCA = right coronary artery, SUV max = maximum standardized uptake value, TBR = target-to-background ratio
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.