PURPOSE: A non-invasive computer-aidedtheragnosis (CAT) system was developed for the early therapeutic @entity5 response assessment in @entity1 with locally advanced @entity0 (LABC) treated with neoadjuvant chemotherapy. METHODS: The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC @entity1 . RESULTS: The CAT system was tested on 56 @entity1 using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding @entity1 populations as well as high accuracy, sensitivity, and specificity in discriminating between the two @entity1 groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. CONCLUSION: The proposed CAT system thus establishes a non-invasive framework for monitoring @entity5 treatment response in @entity5 using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in @entity1 to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced XXXX .

@entity5 :: ('MESH:D009369', 'Disease') :: ['cancer', 'tumours'] @entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancer']
@entity0 :: (MESH:D001943,Disease) :: ['Breast Cancer']
