Predictors of nonadherence to breast cancer screening guidelines in a United States urban comprehensive cancer center.

BACKGROUND: This study aimed to identify predictors of nonadherence to breast cancer screening guidelines in an urban screening clinic among high- and average-risk women in the United States. METHODS: We reviewed records of 6090 women who received ≥2 screening mammograms over 2 years at the Karmanos Cancer Institute to examine how breast cancer risk and breast density were associated with guideline-concordant screening. Incongruent screening was defined as receiving supplemental imaging between screening mammograms for average-risk women, and as not receiving recommended supplemental imaging for high-risk women. We used t-tests and chi-square tests to examine bivariate associations with guideline-congruent screening, and probit regression to regress guideline-congruence unto breast cancer risk, breast density, and their interaction, controlling for age and race. RESULTS: Incongruent screening was more likely among high- versus average-risk women (97.7% vs. 0.9%, p < 0.01). Among average-risk women, incongruent screening was more likely among those with dense versus nondense breasts (2.0% vs. 0.1%, p < 0.01). Among high-risk women, incongruent screening was more likely among those with nondense versus dense breasts (99.5% vs. 95.2%, p < 0.01). The significant main effects of density and high-risk on increased incongruent screening were qualified by a density by high-risk interaction, showing a weaker association between risk and incongruent screening among women with dense breasts (simple slope = 3.71, p < 0.01) versus nondense breasts (simple slope = 5.79, p < 0.01). Age and race were not associated with incongruent screening. CONCLUSIONS: Lack of adherence to evidence-based screening guidelines has led to underutilization of supplementary imaging for high-risk women and potential overutilization for women with dense breasts without other risk factors.

Multimodal neuroimaging of gliomatosis cerebri: a case series of four patients.

In the latest World Health Organization classification of brain tumors, gliomatosis cerebri has been redefined to varying subsets of diffuse gliomas; however, the term is still used to describe gliomas with infiltrative growth into three or more cerebral lobes. These tumors are frequently misdiagnosed and difficult to treat due to their atypical presentation using structural imaging modalities including computed tomography and T1/T2-weighted magnetic resonance imaging (MRI). In this retrospective case series, we compared clinical MRI to amino acid positron emission tomography (PET) to assess the potential value of PET in the assessment of the extent of tumor involvement and in monitoring disease progression. We report the clinical course and serial multimodal imaging findings of four patients. Each patient presented at varying points in disease progression with widespread glioma brain involvement and was evaluated at least once by amino acid PET using alpha-[11C]methyl-L-tryptophan ([11C]-AMT). Increased uptake of [11C]-AMT was detected in a subset of non-enhancing brain lesions and detected tumor invasion before MRI signs of tumor in some regions. Increased uptake of [11C]-AMT was also detected in tumorous regions not detected by perfusion MRI or MR spectroscopy. Metabolic response to treatment was also observed in two patients. Overall, these data are consistent with and expand upon previous reports using other amino acid PET tracers in gliomatosis and show the potential added value of this imaging modality to clinical MRI in the detection and monitoring of these diffusely infiltrative tumors.

Multimodal Imaging of Nonenhancing Glioblastoma Regions.

BACKGROUND: Clinical glioblastoma treatment mostly focuses on the contrast-enhancing tumor mass. Amino acid positron emission tomography (PET) can detect additional, nonenhancing glioblastoma-infiltrated brain regions that are difficult to distinguish on conventional magnetic resonance imaging (MRI). We combined MRI with perfusion imaging and amino acid PET to evaluate such nonenhancing glioblastoma regions. METHODS: Structural MRI, relative cerebral blood volume (rCBV) maps from perfusion MRI, and α-[11C]-methyl-l-tryptophan (AMT)-PET images were analyzed in 20 patients with glioblastoma. The AMT uptake and rCBV (expressed as tumor to normal [T/N] ratios) were compared in nonenhancing tumor portions showing increased signal on T2/fluid-attenuated inversion recovery (T2/FLAIR) images. RESULTS: Thirteen (65%) tumors showed robust heterogeneity in nonenhancing T2/FLAIR hyperintense areas on AMT-PET, whereas the nonenhancing regions in the remaining 7 cases had homogeneous AMT uptake (low in 6, high in 1). AMT and rCBV T/N ratios showed only a moderate correlation in the nonenhancing regions (r = 0.41, P = .017), but regions with very low rCBV (<0.79 T/N ratio) had invariably low AMT uptake. CONCLUSIONS: The findings demonstrate the metabolic and perfusion heterogeneity of nonenhancing T2/FLAIR hyperintense glioblastoma regions. Amino acid PET imaging of such regions can detect glioma-infiltrated brain for treatment targeting; however, very low rCBV values outside the contrast-enhancing tumor mass make increased AMT uptake in nonenhancing glioblastoma regions unlikely.

Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma.

Purpose: Amino acid PET has shown high accuracy for the diagnosis and prognostication of malignant gliomas, however, this imaging modality is not widely available in clinical practice. This study explores a novel end-to-end deep learning framework ("U-Net") for its feasibility to detect high amino acid uptake glioblastoma regions (i.e., metabolic tumor volume) using clinical multimodal MRI sequences. Methods: T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient map, contrast-enhanced T1, and alpha-[11C]-methyl-L-tryptophan (AMT)-PET images were analyzed in 21 patients with newly-diagnosed glioblastoma. U-Net system with data augmentation was implemented to deeply learn non-linear voxel-wise relationships between intensities of multimodal MRI as the input and metabolic tumor volume from AMT-PET as the output. The accuracy of the MRI- and PET-based volume measures to predict progression-free survival was tested. Results: In the augmented dataset using all four MRI modalities to investigate the upper limit of U-Net accuracy in the full study cohort, U-Net achieved high accuracy (sensitivity/specificity/positive predictive value [PPV]/negative predictive value [NPV]: 0.85/1.00/0.81/1.00, respectively) to predict PET-defined tumor volumes. Exclusion of FLAIR from the MRI input set had a strong negative effect on sensitivity (0.60). In repeated hold out validation in randomly selected subjects, specificity and NPV remained high (1.00), but mean sensitivity (0.62), and PPV (0.68) were moderate. AMT-PET-learned MRI tumor volume from this U-net model within the contrast-enhancing volume predicted 6-month progression-free survival with 0.86/0.63 sensitivity/specificity. Conclusions: These data indicate the feasibility of PET-based deep learning for enhanced pretreatment glioblastoma delineation and prognostication by clinical multimodal MRI.

Multimodal imaging-defined subregions in newly diagnosed glioblastoma: impact on overall survival.

BACKGROUND: Although glioblastomas are heterogeneous brain-infiltrating tumors, their treatment is mostly focused on the contrast-enhancing tumor mass. In this study, we combined conventional MRI, diffusion-weighted imaging (DWI), and amino acid PET to explore imaging-defined glioblastoma subregions and evaluate their potential prognostic value. METHODS: Contrast-enhanced T1, T2/fluid attenuated inversion recovery (FLAIR) MR images, apparent diffusion coefficient (ADC) maps from DWI, and alpha-[11C]-methyl-L-tryptophan (AMT)-PET images were analyzed in 30 patients with newly diagnosed glioblastoma. Five tumor subregions were identified based on a combination of MRI contrast enhancement, T2/FLAIR signal abnormalities, and AMT uptake on PET. ADC and AMT uptake tumor/contralateral normal cortex (T/N) ratios in these tumor subregions were correlated, and their prognostic value was determined. RESULTS: A total of 115 MRI/PET-defined subregions were analyzed. Most tumors showed not only a high-AMT uptake (T/N ratio > 1.65, N = 27) but also a low-uptake subregion (N = 21) within the contrast-enhancing tumor mass. High AMT uptake extending beyond contrast enhancement was also common (N = 25) and was associated with low ADC (r = -0.40, P = 0.05). Higher AMT uptake in the contrast-enhancing tumor subregions was strongly prognostic for overall survival (hazard ratio: 7.83; 95% CI: 1.98-31.02, P = 0.003), independent of clinical and molecular genetic prognostic variables. Nonresected high-AMT uptake subregions predicted the sites of tumor progression on posttreatment PET performed in 10 patients. CONCLUSIONS: Glioblastomas show heterogeneous amino acid uptake with high-uptake regions often extending into non-enhancing brain with high cellularity; nonresection of these predict the site of posttreatment progression. High tryptophan uptake values in MRI contrast-enhancing tumor subregions are a strong, independent imaging marker for longer overall survival.

Amino Acid PET Imaging of the Early Metabolic Response During Tumor-Treating Fields (TTFields) Therapy in Recurrent Glioblastoma.

Tumor-treating fields (TTFields) therapy is a relatively new treatment approach for malignant gliomas. We evaluated if amino acid PET can detect an objective metabolic response to TTFields therapy in recurrent glioblastomas. PET scanning with alpha[C-11]-methyl-L-tryptophan (AMT) before and 2 to 3 months after the start of TTFields treatment showed an interval decrease of tryptophan uptake in the whole tumor (2 patients) or in a portion of the tumor (1 patient). These data demonstrate that TTFields therapy can induce an early metabolic response in recurrent glioblastoma, and this treatment response can be detected by amino acid PET.