Jan Luts

Triple-negative breast cancer: Present challenges and new perspectives

Triple‐negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined ‘omics’ approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi‐dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

A tutorial on support vector machine-based methods for classification problems in chemometrics

This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.

A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection

OBJECTIVE This study investigates the use of automated pattern recognition methods on magnetic resonance data with the ultimate goal to assist clinicians in the diagnosis of brain tumours. Recently, the combined use of magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) has demonstrated to improve the accuracy of classifiers. In this paper we extend previous work that only uses binary classifiers to assess the type and grade of a tumour to a multiclass classification system obtaining class probabilities. The important problem of input feature selection is also addressed. METHODS AND MATERIAL Least squares support vector machines (LS-SVMs) with radial basis function kernel are applied and compared with linear discriminant analysis (LDA). Both a Bayesian framework and cross-validation are used to infer the parameters of the LS-SVM classifiers. Four different techniques to obtain multiclass probabilities as a measure of accuracy are compared. Four variable selection methods are explored. MRI and MRSI data are selected from the INTERPRET project database. RESULTS The results illustrate the significantly better performance of automatic relevance determination (ARD), in combination with LS-SVMs in a Bayesian framework and coupling of class probabilities, compared to classical LDA. CONCLUSION It is demonstrated that binary LS-SVMs can be extended to a multiclass classifier system obtaining class probabilities by Bayesian techniques and pairwise coupling. Feature selection based on ARD further improves the results. This classifier system can be of great help in the diagnosis of brain tumours.

Clinical implications of intra- and interobserver reproducibility of transvaginal sonographic measurement of gestational sac and crown-rump length at 6-9 weeks' gestation

To assess intra‐ and interobserver agreement of routinely performed measurements—crown–rump length (CRL) and mean gestational sac diameter (MSD)—for assessing the likelihood of miscarriage in the first trimester of pregnancy using transvaginal sonography.

Nosologic imaging of the brain: segmentation and classification using MRI and MRSI.

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject‐specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high‐resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making. Copyright

The effect of combining two echo times in automatic brain tumor classification by MRS

1H MRS is becoming an accurate, non‐invasive technique for initial examination of brain masses. We investigated if the combination of single‐voxel 1H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20–32 ms) and long TE (PRESS, 135–136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low‐grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low‐grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short‐TEs and long‐TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short‐TE, long‐TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low‐grade glial tumours, the use of short‐TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web‐based multicentric classifier development studies. Copyright

Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra.

This study examines the effect of feature extraction methods prior to automated pattern recognition based on magnetic resonance spectroscopy (MRS) for brain tumor diagnosis. Since individual inspection of spectra is time‐consuming and requires specific spectroscopic expertise, the introduction of clinical decision support systems (DSSs) is expected to strongly promote the clinical use of MRS. This study focuses on the feature extraction step in the preprocessing protocol of MRS when using a DSS. On two independent data sets, encompassing single‐voxel and multi‐voxel data, it is observed that the use of the full spectra together with a kernel‐based technique, handling high dimensional data, or using an automated pattern recognition method based on independent component analysis or Relief‐F achieves accurate performances. In addition, these approaches have low cost and are easy to automate. When sophisticated quantification methods are used in a DSS, user interaction should be minimized. The computationally intensive quantification techniques do not tend to increase the performance in these circumstances. The results suggest to simplify the feature reduction step in the preprocessing protocol when using a DSS purely for classification purposes. This can greatly speed up the execution of classifiers and DSSs and may accelerate their introduction into clinical practice. Magn Reson Med 60:288–298, 2008.

Quantitative autism traits in first degree relatives: evidence for the broader autism phenotype in fathers, but not in mothers and siblings:

Autism spectrum disorder (ASD) symptoms are present in unaffected relatives and individuals from the general population. Results are inconclusive, however, on whether unaffected relatives have higher levels of quantitative autism traits (QAT) or not. This might be due to differences in research populations, because behavioral data and molecular genetic research suggest that the genetic etiology of ASD is different in multiplex and simplex families. We compared 117 unaffected siblings and 276 parents of at least one child with ASD with 280 children and 595 adults from the general population on the presence of QAT using the Social Responsiveness Scale (SRS). Mean SRS scores for siblings, control children, parents and control adults were 25.4, 26.6, 33.7 and 32.9. Fathers of children with ASD showed significantly higher levels of QAT than controls, but siblings and mothers did not. We could not detect a statistically significant difference in SRS scores between relatives from simplex and multiplex families. These results do not support the theory of differential (genetic) etiology in multiplex and simplex families and suggest that a carried genetic risk is generally not expressed phenotypically in most relatives, except in fathers.

A mixed effects least squares support vector machine model for classification of longitudinal data

A mixed effects least squares support vector machine (LS-SVM) classifier is introduced to extend the standard LS-SVM classifier for handling longitudinal data. The mixed effects LS-SVM model contains a random intercept and allows to classify highly unbalanced data, in the sense that there is an unequal number of observations for each case at non-fixed time points. The methodology consists of a regression modeling and a classification step based on the obtained regression estimates. Regression and classification of new cases are performed in a straightforward manner by solving a linear system. It is demonstrated that the methodology can be generalized to deal with multi-class problems and can be extended to incorporate multiple random effects. The technique is illustrated on simulated data sets and real-life problems concerning human growth.

Detection of intracavitary uterine pathology using offline analysis of three-dimensional ultrasound volumes: interobserver agreement and diagnostic accuracy

To estimate the diagnostic accuracy and interobserver agreement in predicting intracavitary uterine pathology at offline analysis of three‐dimensional (3D) ultrasound volumes of the uterus.