Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis ...of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast.
Level of Evidence
2
Technical Efficacy Stage
2
In this article, we study the near‐optimal control of a class of stochastic vegetation‐water model. The near‐optimal control is one problem in which the density of vegetation and water is higher at ...the lowest cost. We have provided a priori estimates of the vegetation and water densities and obtained the sufficient and necessary conditions for the system's near‐optimal control problem by applying the maximum condition of the Hamiltonian function and the Ekeland principle. A numerical simulation is presented to verify our theoretical results.
In this paper, based on the pathogenesis of Alzheimer's disease, we investigate a stochastic mathematical model, focusing on the dynamics of β‐amyloid (Aβ) plaques, Aβ oligomers, PrPC proteins, and ...the Aβ‐x‐PrPC complex. Within the framework of the Lyapunov method, we first show existence and uniqueness of global positive solution of the model and then establish the sufficient conditions for existence of an ergodic stationary distribution of the positive solution. Ultimately, numerical examples are presented to illustrate the effectiveness of theoretical results.
This work investigates the existence and asymptotic behavior of solutions to a nonlocal dispersal in‐host viral model with humoral immunity. The model features both spatial movement of virus and ...humoral immunity response to the virus. This paper gives the principal eigenvalue (H0$\mathcal {H}_0$) of the nonlocal dispersal problem, and it is verified to be a critical value that determines the infection dynamics. The uninfected equilibrium is unique and stable for H0≤0$\mathcal {H}_0\le 0$. For H0>0$\mathcal {H}_0>0$, the equilibrium is not stable, and infected with/without B cells response equilibrium emerges. This paper also establishes the dynamics of infected equilibria. Numerical simulations are carried out to demonstrate the analytical results of this study.
In this paper, we present a periodic averaging (PA) method for impulsive stochastic age‐structured population model (ISASPM) in a polluted environment. By using Young's inequality and Gronwall's ...lemma and under the stochastic Lipschitz condition, we reveal that both the standard ISASPM and the averaged ISASPM have a unique solution. We also study the mean‐square convergence criteria of the numerical solutions produced by the PA method. Finally, asimulation example is given to demonstrate that the PA method is efficient for our results.
The change of parameters may influence the dynamic behaviors of epidemic diseases. Biological system parameters can also be changed due to diverse uncertainties such as lack of data and errors in the ...statistical approach. The problem of how to define and decide the optimal‐control strategies of epidemic diseases with imprecise parameters deserves further researches. The paper presents a stochastic susceptible, infected, and vaccinated (SIV) system that includes imprecise parameters. Firstly, we give the method of parameter estimates of the SIV model. Then, by using Ekeland's principle and Hamiltonian function, we obtain the sufficient conditions and necessary conditions of near‐optimal control of the SIV epidemic model with imprecise parameters. At last, numerical examples prove our theoretical results.
The mutational status of the isocitrate dehydrogenase (
) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. ...Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 participants for independent external testing) were enrolled using two different physio-metabolic MRI protocols. The algorithms trained with physio-metabolic data demonstrated the best classification performance in independent internal testing: precision, 91.7%; accuracy, 87.5%; area under the receiver operating curve (AUROC), 0.979. In external testing, traditional machine learning models trained with cMRI data exhibited the best
classification results: precision, 84.9%; accuracy, 81.8%; and AUROC, 0.879. The poor performance for the physio-metabolic MRI approach appears to be explainable by site-dependent differences in data acquisition methodologies. The physio-metabolic MRI approach potentially supports reliable classification of
gene status in the presurgical stage of glioma patients. However, non-standardized protocols limit the level of evidence and underlie the need for a reproducible framework of data acquisition techniques.
Most computational models for competitive neural networks describe activity–connectivity interactions at different time-scales. We extend these existing models by considering stochastic processes and ...establish stability results based on the theory of singularly perturbed stochastic systems. Based on a reduced-order model we determine conditions that ensure the existence of the exponentially mean-square stability equilibria of the stochastic nonlinear system. It is assumed that the system is described by Ito-type equations. We derive a Lyapunov function for the coupled system and an upper bound for the parameters of the independent stochastic process.
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