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Interim analysis is the practice of performing a statistical analysis when the data have only been partially collected, for example, to save resources or to handle the uncertainty of the true effect size. Most statistical designs featuring interim analysis have been developed either in a general statistical setting or for application in clinical trials. As a result, most of them make assumptions and have conditions that in a preclinical setting are usually not met. In this paper, we present necessary changes to the most common forms of interim analysis enhanced for animal experiments, specifically for the t-test and the one-way ANOVA. Finally, we present software that allows freeware use to serve the research community to facilitate the design of experiments featuring interim analyses. The app can be found at icds.be/gsdesigner. It is in the public domain and its code can be found on github.com/ICDS-vubUZ/gsd-designer. In this GitHub folder, one can also find a tutorial for the app.

In order to optimize a patient's cancer treatment, the prediction of the tumor's response to a planned radiotherapy is vital. For patients with rectal cancer, the quality of life depends heavily on the surgery leaving some patients with a stoma or other problems. If we would be able to predict the response upfront, we would be able to personalize the treatment and therefore preserve the QOL as much as possible. The Dworak tumor regression grade is a typical diagnostic tool to assess the tumor response of colorectal cancer patients. However, the Dworak grade is determined by a pathologist by inspection of the tumor biopsy without a dedicated measurement instrument. The measurement of the Dworak grade in an automated way by using the pre-operative CT scans, is a challenge. In this paper, we propose a novel methodology to measure the Dworak grade based on a customized random forest. We created a new random forest based on the methods from Breiman and Ho. These methods are further enhanced by evolutionary computation. We give the results of both our new method and other classical classification methods and highlight that the choice of classifier and knowledge is crucial. We extracted 111 radiomic features from 141 patients with colorectal cancer, of which 97 bad and 44 good responders. The evaluation gave a 77.507% accuracy and the cross-validation used for validation, gave a 67.081% accuracy. Our results were the best compared with the some other classification methods showing the need of a new method.

BACKGROUND AND PURPOSE: Stereotactic radiotherapy (SRT, SBRT) is widely used in oligometastatic cancer, but the heterogeneity of the population complicates estimation of the prognosis. We investigated the role of different clinical and inflammatory parameters. MATERIALS AND METHODS: We included all patients treated with SRT for 1-5 oligometastases between 2003 and 2017 in our center. Patients were randomized between a model training set (2/3) and a separate validation set (1/3). A Cox regression model was built, validated and risk points were attributed to the resulting parameters. RESULTS: 403 patients received SRT for 760 metastases. Treated sites were mainly lung, liver, nodal areas, and brain. Most common primaries were colorectal and lung cancer. Median follow-up for living patients reached 42 months and median overall survival (MS) was 26.6 months (95% CI 23.8-29.3). Five independent adverse factors were discriminated: male sex, synchronous timing of oligometastases, brain metastasis, non-adenocarcinoma histology, KPS

The article presents a methodology to discriminate glucose levels using electrical impedance spectroscopy technology. The method is based on an adequate estimation and assessment of the impedance data followed by identification of a general rational fractional model. The methodology is illustrated on a group of saline–protein–glucose solutions at physiological concentrations, and shows the ability of the fractional models to discriminate glucose levels. The method exhibit significant differences in the zero position of the fractional model for different glucose concentrations allowing discriminate the glucose effect on the impedance data along different matrix solutions. The results based on fractional model method are compared with classical circuit models and rational integer models. Even when the different methodologies perceive variations in the impedance data given glucose alterations, the fractional models present low uncertainty allowing discriminating small glucose alterations in the physiological range.

The large diffusion of smartphones equipped with new built-in sensors has stimulated researchers to develop applications in different fields. The aim of this work is to provide an overview of the most significant health-care applications. Interest is focused on the use of (i) 3D accelerometer for actimetric, body posture and falls monitoring, (ii) camera for evaluating cardiovascular system parameters, blood oxygen saturation, and eye pathologies, and (iii) microphone for recognizing diseases of the respiratory system. A comparison of these applications to traditional medical devices is discussed, in order to highlight strong and weak points. Limits and possible future research directions are argued.

Electrochemical impedance spectroscopy (EIS) allows measuring the properties of the system as a function of the frequency as well as distinguishing between processes that could be involved: resistance, reactance, relaxation times, amplitudes, etc. Although it is possible to find related literature to in vitro and in vivo experiments to estimate glucose concentration, no clear information regarding the condition and precision of the measurements are easily available. This article first address the problem of the condition and precision of the measurements, as well as the effect of the glucose over the impedance spectra at some physiological (normal and pathological) levels. The significance of the measurements and the glucose effect over the impedance are assessment regarding the noise level of the system, the experimental error and the effect of using different sensors. Once the data measurements are analyzed the problem of the glucose estimation is addressed. A rational parametric model in the Laplace domainis proposed to track the glucose concentration. The electrochemical spectrum is measured employing odd random phase multisine excitation signals. This type of signals provides short acquisition time, broadband measurements and allows identifying the best linear approximation of the impedance as well as estimating the level of noise and non-linearities present in the system. All the experiments were repeated five times employing three different sensors from the same brand in order to estimate the significance of the experimental error, the effects of the sensors and the effect of the glucose over the impedance.

Oscillometric blood pressure (BP) monitors are omnipresent and used on a daily basis for personalized healthcare. Nevertheless, physicians generally approach these devices cautiously since the mercury Korotkoff sphygmomanometer remains the golden standard. Various reasons explain the hesitating attitude of the medical world towards automated BP monitors: (i) its principle is based on the pressure pulsations arriving at the cuff by the cardiac cycle instead of an audio wave used by physicians triggered by the turbulences in the artery, (ii) the actual computation of the systolic and diastolic BP from the measured oscillometry is manufacturer dependent and not based on general scientific principles, (iii) the quality of the oscillometric monitors is labeled by a trial such that the devices correspond well to the Korotkoff method for the average healthy patient but deviates for patients suffering from hypo- or hypertension. In this paper, we develop a statistical learning technique to calibrate and correct an oscillometric monitor such that the device better corresponds to the Korotkoff method regardless of the health status of the patient. The technique is based on logistic regression which allows correcting and eliminating systematic errors caused by patients suffering from hyper -or hypotension. No user interaction is required since the technique is able to train and validate the calibration procedure in an unsupervised way. In our case study, the systematic error is reduced by nearly 50% corresponding to the performance specifications of the device.

The steady state response of a system to a periodic input is still subject to noise transients. For lightly damped systems these noise transients can significantly increase the variance of the estimated frequency response function (FRF). This paper presents a method that suppresses the influence of the noise transients (leakage errors) in nonparametric FRF and noise (co-)variance estimates of dynamic systems excited by periodic signals. The method is based on a local polynomial approximation of the noise leakage errors on the FRF. Compared with the classical approaches, the proposed procedure is more robust and needs less measurement time (two signal periods are sufficient). The theory is supported by simulation and real measurement examples.

This is Part II of a series of two papers on the estimation of nonparametric noise and frequency response function models for multiple input, multiple output systems excited by arbitrary inputs. Part I (Pintelon et al. (2009)) [1] develops the methodology for linear dynamic multivariable systems with exactly known input and where the output is disturbed by stationary noise ( = output error problem ) . The contributions of Part II are the following. First, it is shown that the proposed method can be used for nonlinear systems and for parametric identification of the system dynamics in a generalized output error framework. Next, the methodology is generalized to handle noisy input–output data ( = errors -in-variables problem), and identification in feedback. Finally, the approach is illustrated on simulations and real measurements examples.

This paper analyses the variance of linear models that are identified in the presence of nonlinear distortions. The best linear approximation G BLA to a nonlinear system, driven by a Gaussian distributed random input, is identified in least squares sense. Even in the absence of disturbing noise, the estimated model Ĝ BLA will vary from one realization of the excitation signal to the other. In this paper it will be shown that the variance of the nonparametric frequency response function (FRF) Ĝ BLA ( k ) can still be obtained from the experimental data using the classical variance formulas. However, for parametric estimates Ĝ BLA ( q , θ), an increased variance is observed. The contributing terms to this additional variance are studied in this paper.

The chemical composition of a bivalve shell is strongly coupled to the seasonal variations in the environment. The nonlinear nature of this relation however makes it hard to predict, e.g. the temperature, from the chemical composition of a shell. In this paper we compare the ability of three nonlinear system identification methods to reconstruct the temperature from the chemical composition of a shell. The comparison shows that nonlinear multi-proxy approaches are potential tools for climate reconstructions with a preference for manifold based methods that results in smoother and a more precise temperature reconstruction.

In this paper, we study the properties of existing non-parametric methods for estimating the plant and noise transfer functions of a linear dynamic system. The analysis is based on the recent insight that leakage errors in the frequency domain have a smooth nature that is completely similar to the initial transients in the time domain. This not only allows us to understand better the existing clasic methods, but also opens the road to new better performing algorithms. The paper includes the output error setup, the errors-in-variables setup, and measurements under feedback conditions. Eventually, some of the methods are illustrated in the analysis of a vibrating metal beam.