A comparative study in ultrasound breast imaging classification

American College of Radiology introduces a standard in classification, the breast imaging reporting and data system (BIRADS), standardize the reporting of ultrasound findings, clarify its interpretation, and facilitate communication between clinicians. The effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing computer tools in the diagnostics process. Initially a detailed study was carried out to evaluate the performance of two commonly used appearance based classification algorithms, based on the use of Principal Component Analysis (PCA), and two dimensional linear discriminant analysis (2D-LDA). The study showed that these two appearance based classification approaches are not capable of handling the classification of ultrasound breast image lesions. Therefore further investigations in the use of a popular feature based classifier – Support Vector Machine (SVM) was conducted. A pre-processing step before feature based classification is feature extraction, which involve shape, texture and edge descriptors for the Region of Interest (ROI). The input dataset to SVM classification is from a fully automated ROI detection. We achieve the success rate of 0.550 in PCA, 0.500 in LDA, and 0.931 in SVM. The best combination of features in SVM classification is to combine the shape, texture and edge descriptors, with sensitivity 0.840 and specificity 0.968. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of our work

A comparative study in ultrasound breast imaging classification

Revue Européenne d'Ethnographie de l'Education / Revista europeia de etnografia da educação Sujet : Fondée en 1999 au colloque de Lecce (Italie), la Société européenne d'ethnographie de l'éducation (SEEE) s'était donnée pour tâche, à coté de la promotion de la démarche ethnographique, du développement de la recherche et du renforcement des contacts entre étudiants dans les différents pays d'Europe, une dimension éditoriale où figurent à la fois le projet de publications de recherches prop..

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

Interobserver agreement of Thyroid Imaging Reporting and Data System (TIRADS) and strain elastography for the assessment of thyroid nodules

Background: Thyroid Imaging Reporting and Data System (TIRADS) was developed to improve patient management and cost-effectiveness by avoiding unnecessary fine needle aspiration biopsy (FNAB) in patients with thyroid nodules. However, its clinical use is still very limited. Strain elastography (SE) enables the determination of tissue elasticity and has shown promising results for the differentiation of thyroid nodules. Methods: The aim of the present study was to evaluate the interobserver agreement (IA) of TIRADS developed by Horvath et al. and SE. Three blinded observers independently scored stored images of TIRADS and SE in 114 thyroid nodules (114 patients). Cytology and/or histology was available for all benign (n = 99) and histology for all malignant nodules (n = 15). Results: The IA between the 3 observers was only fair for TIRADS categories 2–5 (Coheńs kappa = 0.27,p = 0.000001) and TIRADS categories 2/3 versus 4/5 (ck = 0.25,p = 0.0020). The IA was substantial for SE scores 1–4 (ck = 0.66,p<0.000001) and very good for SE scores 1/2 versus 3/4 (ck = 0.81,p<0.000001). 92–100% of patients with TIRADS-2 had benign lesions, while 28–42% with TIRADS-5 had malignant cytology/histology. The negative-predictive-value (NPV) was 92–100% for TIRADS using TIRADS-categories 4&5 and 96–98% for SE using score ES-3&4 for the diagnosis of malignancy, respectively. However, only 11–42% of nodules were in TIRADS-categories 2&3, as compared to 58–60% with ES-1&2. Conclusions: IA of TIRADS developed by Horvath et al. is only fair. TIRADS and SE have high NPV for excluding malignancy in the diagnostic work-up of thyroid nodules