Page 123 Table 2.11, m=1: Replace "YK=K1 (X0 cos(Z0)+Y0sin(Z0))" with "YK=K1 (Y0 cos(Z0)+X0sin(Z0))" Page 123 Table 2.11, m=-1: Replace "XK=K-1√X0 2 + Y0 2 " with "XK=K-1√X0 2-Y0 2 " Replace "YK=K-1 (X0 cosh(Z0)+Y0sinh(Z0))" with "YK=K-1 (Y0 cosh(Z0)+X0sinh(Z0)

Purpose: To assess the potential of machine learning with multiparametric MRI (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients. Materials and Methods: This IRB-approved prospective study included 38 women (median age 46.5 years; range 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3T with DCE, DWI and T2-weighted imaging prior to and after two cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS, quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time) and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, eight classifiers including linear support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), stochastic gradient descent (SGD), decision tree, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) were employed to rank the features. Histopathologic Residual Cancer Burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS) and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the

International audienceDetermining the hydrogen-deuterium exchange speeds of single residues from data for peptic fragments obtained by FT-ICS MS is currently mainly done by manual interpretation. We provide an automated method based on combinatorial optimization. More precisely, we present an algorithm that enumerates all possible exchange speeds for single residues that explain the observed data of the peptic fragments

The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of such a neural network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. We present a new method of analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We are able to show the conditions under which the solutions of such a system are bounded being less restrictive than with the K-monotone theory, singular perturbation theory, or those based on supervised synaptic learning. We prove the existence and the uniqueness of the equilibrium. A strict Lyapunov function for the flow of a competitive neural system with different time scales is given and based on it we are able to prove the global exponential stability of the equilibrium point.

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Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised Clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.

Background Detecting pathological breast calcifications remains challenging. Based on recent studies, contrast-enhanced spectral mammography (CESM) was shown to be superior compared to full-field digital mammography (FFDM). Purpose To evaluate the diagnostic accuracy of CESM in suspicious breast calcifications and its impact on surgical decision-making. Material and Methods All screening recalled patients with suspicious calcifications that underwent CESM in the period October 2012 until September 2015 were included. One experienced radiologist provided a BI-RADS classification for the FFDM images only. The evaluation was repeated for the CESM exam. In a simulated tumor board meeting, two breast surgeons decided on the preferred surgical treatment (breast conservation therapy [BCT] versus mastectomy) for all malignant cases. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated defining BI-RADS ≥4 as being malignant. In addition, differences in surgical decision-making were analyzed and compared using McNemar’s test. Results In total, 147 women were included in this study (mean age = 61 years; age range = 49–75 years). Pathology showed 82 benign and 65 malignant lesions, of which 33 were ductal carcinomas in situ and 32 were invasive lesions. Diagnostic performances of CESM (differences compared to FFDM in brackets) were: sensitivity 93.8% (+3%), specificity 36.6% (−2.5%), PPV 54% (0%), and NPV 88.2% (+4%). Based on low-energy images, surgeons suggested BCT in 89% of the cases. Based on the CESM exam, no statistical changes in decisions were observed (86% BCT, P = 0.453). Conclusion CESM only slightly improves the diagnostic accuracy of the evaluation of breast calcifications. It is not of added value compared to FFDM in guiding surgical decision-making.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.