Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images

VoxLogicA : A Spatial Model Checker for Declarative Image Analysis

Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis.We explore a new domain, namely (semi-)automatic contouring in Medical Imaging, introducing the tool VoxLogicA which merges the state-of-the-art library of computational imaging algorithms ITK with the unique combination of declarative specification and optimised execution provided by spatial logic model checking. The result is a rapid, logic based analysis development methodology. The analysis of an existing benchmark of medical images for segmentation of brain tumours shows that simple VoxLogicA analysis can reach state-of-the-art accuracy, competing with best-in-class algorithms, with the advantage of explainability and easy replicability. Furthermore, due to a two-orders-of-magnitude speedup compared to the existing generalpurpose spatio-temporal model checker topochecker, VoxLogicA enables interactive development of analysis of 3D medical images, which can greatly facilitate the work of professionals in this domain

Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer

To assess the changes in computed tomography (CT) tumor heterogeneity following neoadjuvant chemotherapy in esophageal cancer. Thirty-one consecutive patients who received neoadjuvant chemotherapy for esophageal cancer were identified. Analysis of primary tumor heterogeneity (texture) was performed on staging and post-chemotherapy CT scans. Image texture parameters (mean grey-level intensity, entropy, uniformity, kurtosis, skewness, standard deviation of histogram) were derived for different levels of image filtration (0-2.5). Proportional changes in each parameter following treatment were obtained. Comparison between pathological tumor response and texture parameters was analyzed using Mann-Whitney U-test. The relationship between CT texture and overall survival) was estimated using the Kaplan-Meier method. Tumor texture became more homogeneous after treatment with a significant decrease in entropy and increase in uniformity (filter 1.0 and 2.5). Pretreatment (filter 1.5, P = 0.006) and posttreatment standard deviation of histogram (filter 1.0, P = 0.009) showed a borderline association with pathological tumor response. A proportional change in skewness <0.39 (filter 1.0) was associated with improved survival (median overall survival 36.1 vs. 11.1 months; P < 0.001). CT tumor heterogeneity decreased following neoadjuvant chemotherapy and has the potential to provide additional information in primary esophageal cancer.Peer reviewe

Increased microvascular proliferation is negatively correlated to tumour blood flow and is associated with unfavourable outcome in endometrial carcinomas

Background: We aimed to study the angiogenic profile based on histomorphological markers in endometrial carcinomas in relation to imaging parameters obtained from preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) and to explore the potential value of these markers to identify patients with poor outcome. Methods: In fifty-four surgically staged endometrial carcinoma patients, immunohistochemical staining with factor VIII and Ki67 allowed assessment of microvessel density (MVD) and microvascular proliferation reflecting tumour angiogenesis. In the same patients, preoperative pelvic DCE-MRI and DWI allowed the calculation of parameters describing tumour microvasculature and microstructure in vivo. Results: Microvascular proliferation was negatively correlated to tumour blood flow (Fb) (r=−0.36, P=0.008), capillary permeability surface area product (PS) (r=−0.39, P=0.004) and transfer from the blood to extravascular extracellular space (EES) (Ktrans) (r=−0.40, P=0.003), and was positively correlated to tumour volume (r=0.34; P=0.004). High-tumour microvascular proliferation, low Fb and low Ktrans were all significantly associated with reduced progression/recurrence-free survival (P<0.05). Conclusion: Disorganised angiogenesis with coexisting microvascular proliferation and low tumour blood flow is a poor prognostic factor supporting that hypoxia is associated with progression and metastatic spread in endometrial carcinomas