Time-dependent diffusion tensor imaging and diffusion modeling of age-related differences in the medial gastrocnemius and feasibility study of correlations to histopathology.

PURPOSE: Implement STEAM-DTI to model time-dependent diffusion eigenvalues using the random permeable barrier model (RPBM) to study age-related differences in the medial gastrocnemius (MG) muscle. Validate diffusion model-extracted fiber diameter for histological assessment. METHODS: Diffusion imaging at different diffusion times (Δ) was performed on seven young and six senior participants. Time-dependent diffusion eigenvalues (λ2 (t), λ3 (t), and D⊥ (t); average of λ2 (t) and λ3 (t)) were fit to the RPBM to extract tissue microstructure parameters. Biopsy of the MG tissue for histological assessment was performed on a subset of participants (four young, six senior). RESULTS: λ3 (t) was significantly higher in the senior cohort for the range of diffusion times. RPBM fits to λ2 (t) yielded fiber diameters in agreement to those from histology for both cohorts. The senior cohort had lower values of volume fraction of membranes, ζ, in fits to λ2 (t), λ3 (t), and D⊥ (t) (significant for fit to λ3 (t)). Fits of fiber diameter from RPBM to that from histology had the highest correlation for the fit to λ2 (t). CONCLUSION: The age-related patterns in λ2 (t) and λ3 (t) could tentatively be explained from RPBM fits; these patterns may potentially arise from a decrease in fiber asymmetry and an increase in permeability with age.

Exploring the Negative Likelihood Ratio and How It Can Be Used to Minimize False-Positives in Breast Imaging.

OBJECTIVE: This article describes the definition and proposed utilization of negative likelihood ratios (NLRs) as statistical parameters in breast imaging. Examples with calculations are provided using BI-RADS category 4 subcategories. CONCLUSION: By auditing individual performance early and often against American College of Radiology benchmark positive predictive value ranges for the BI-RADS category 4 subcategories, and by fully understanding NLRs and their application in breast imaging, radiologists can minimize false-positive findings and unnecessary biopsies.

Correlation between Imaging Markers Derived from PET/MRI and Invasive Acquired Biomarkers in Newly Diagnosed Breast Cancer.

PURPOSE: Evaluate the diagnostic potential of [18F]FDG-PET/MRI data compared with invasive acquired biomarkers in newly diagnosed early breast cancer (BC). METHODS: Altogether 169 women with newly diagnosed BC were included. All underwent a breast- and whole-body [18F]FDG-PET/MRI for initial staging. A tumor-adapted volume of interest was placed in the primaries and defined bone regions on each standard uptake value (SUV)/apparent diffusion coefficient (ADC) dataset. Immunohistochemical markers, molecular subtype, tumor grading, and disseminated tumor cells (DTCs) of each patient were assessed after ultrasound-guided biopsy of the primaries and bone marrow (BM) aspiration. Correlation analysis and group comparisons were assessed. RESULTS: A significant inverse correlation of estrogen-receptor (ER) expression and progesterone-receptor (PR) expression towards SUVmax was found (ER: r = 0.27, p < 0.01; PR: r = 0.19, p < 0.05). HER2-receptor expression showed no significant correlation towards SUV and ADC values. A significant positive correlation between Ki67 and SUVmax and SUVmean (r = 0.42 p < 0.01; r = 0.19 p < 0.05) was shown. Tumor grading significantly correlated with SUVmax and SUVmean (ρ = 0.36 and ρ = 0.39, both p's < 0.01). There were no group differences between SUV/ADC values of DTC-positive/-negative patients. CONCLUSIONS: [18F]FDG-PET/MRI may give a first impression of BC-receptor status and BC-tumor biology during initial staging by measuring glucose metabolism but cannot distinguish between DTC-positive/-negative patients and replace biopsy.

CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.

This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.