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External beam radiation treatment for patients with cervical cancer is hindered by the relatively large motion of the target volume. A hybrid MRI-accelerator system makes it possible to acquire online MR images during treatment in order to correct for motion and deformation. To fully benefit from such a system, online delineation of the target volumes is necessary. The aim of this study is to investigate the accuracy of rigid, non-rigid and semi-automatic registrations of MR images for interfractional contour propagation in patients with cervical cancer. Registration using mutual information was performed on both bony anatomy and soft tissue. A B-spline transform was used for the non-rigid method. Semi-automatic registration was implemented with a point set registration algorithm on a small set of manual landmarks. Online registration was simulated by application of each method to four weekly MRI scans for each of 33 cervical cancer patients. Evaluation was performed by distance analysis with respect to manual delineations. The results show that soft-tissue registration significantly (P < 0.001) improves the accuracy of contour propagation compared to registration based on bony anatomy. A combination of user-assisted and non-rigid registration provides the best results with a median error of 3.2 mm (1.4-9.9 mm) compared to 5.9 mm (1.7-19.7 mm) with bone registration (P < 0.001) and 3.4 mm (1.3-19.1 mm) with non-rigid registration (P = 0.01). In a clinical setting, the benefit may be further increased when outliers can be removed by visual inspection of the online images. We conclude that for external beam radiation treatment of cervical cancer, online MRI imaging will allow target localization based on soft tissue visualization, which provides a significantly higher accuracy than localization based on bony anatomy. The use of limited user input to guide the registration increases overall accuracy. Additional non-rigid registration further reduces the propagation error and negates errors caused by small observer variations.

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At the UMC Utrecht, The Netherlands, we have constructed a prototype MRI accelerator. The prototype is a modified 6 MV Elekta (Crawley, UK) accelerator next to a modified 1.5 T Philips Achieva (Best, The Netherlands) MRI system. From the initial design onwards, modifications to both systems were aimed to yield simultaneous and unhampered operation of the MRI and the accelerator. Indeed, the simultaneous operation is shown by performing diagnostic quality 1.5 T MRI with the radiation beam on. No degradation of the performance of either system was found. The integrated 1.5 T MRI system and radiotherapy accelerator allow simultaneous irradiation and MR imaging. The full diagnostic imaging capacities of the MRI can be used; dedicated sequences for MRI-guided radiotherapy treatments will be developed. This proof of concept opens the door towards a clinical prototype to start testing MRI-guided radiation therapy (MRIgRT) in the clinic.

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This paper describes a system for in vitro cell migration analysis. Adult neural stem/progenitor cells are studied using time-lapse bright-field microscopy and thereafter stained immunohistochemically to find and distinguish undifferentiated glial progenitor cells and cells having differentiated into type-1 or type-2 astrocytes. The cells are automatically segmented and tracked through the time-lapse sequence. An extension to the Chan-Vese Level Set segmentation algorithm, including two new terms for specialized growing and pruning, made it possible to resolve clustered cells, and reduced the tracking error by 65%. We used a custom-built manual correction module to form a ground truth used as a reference for tracked cells that could be identified from the fluorescence staining. On average, the tracks were correct 95% of the time, using our new segmentation. The tracking, or association of segmented cells, was performed using a 2-state Hidden Markov Model describing the random behaviour of the cells. By re-estimating the motion model to conform with the segmented data we managed to reduce the number of tracking parameters to essentially only one. Upon characterization of the cell migration by the HMM state occupation function, it was found that glial progenitor cells were moving randomly 2/3 of the time, while the type-2 astrocytes showed a directed movement 2/3 of the time. This finding indicates possibilities for cell-type specific identification and cell sorting of live cells based on specific movement patterns in individual cell populations, which would have valuable applications in neurobiological research.

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In daily clinical practice, implanted fiducial markers are used to correct for prostate motion, but not for prostate deformation. The aim of this study is to investigate the variation in target and rectum dose due to the deformation of the prostate gland (without seminal vesicles). Therefore, we performed five to six MRI scans of eight healthy volunteers that exhibited large variation in rectal volume and thus prostate deformation. Prostate motion was corrected by a mask-based rigid registration which uses the delineation as well as the internal structures of the prostate gland. Per MRI scan, one IMRT plan with a PTV margin of 4 mm was created, resulting in 41 IMRT plans. The dose distribution of the IMRT plan based on the MRI scan with the minimum rectal volume was applied to the other rigidly registered MRI scans to evaluate the impact of prostate deformation. In conclusion, pre-treatment planning on the minimum rectal volume can cause a fraction dose increase (up to 15%) to the rectum due to prostate deformation. The impact on the total dose increase to the rectum depends on the intrapatient rectum variation during treatment, but is negligible with the currently used PTV margins in a fractionated treatment.

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When comparing delineations it is often useful to obtain a local measure of distance between the volume surfaces. Commonly used methods for analysing local distance exhibit fundamental drawbacks which may cause overestimation of the distance or lead to asymmetry in the measure. This paper describes a new method that aims to solve these problems. The new method finds corresponding points between two delineations by traversing a vector field based on the combined gradient of the distance transforms. The proposed method provides a fundamentally more reliable, symmetric measure of distance. This is supported by an illustrative example of observer variation in prostate delineation. An implementation of the method is available on request to the author.

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Developing an analytical separation procedure for an unknown mixture is a challenging issue. An important example is the separation and quantification of a new drug and its impurities. One approach to start method development is the screening of the mixture on dissimilar chromatographic systems, i.e. systems with large selectivity differences. After screening, the most suited system is retained for further method development. In a step prior to such strategy dissimilar chromatographic systems need to be selected. In this paper the performance of different chemometric selection approaches, described in the literature, was visually evaluated and compared. Additionally, orthogonal projection approach (OPA) was tested as another potential selection method. All techniques, including the OPA method, were able to select (a set of) dissimilar chromatographic systems and many similarities between the selections were observed. However, the Kennard and Stone algorithm performed best in selecting the most dissimilar systems in the earliest steps of the selection procedure. The generalized pairwise correlation method (GPCM) and the auto-associative multivariate regression trees (AAMRT) were also performing well. OPA and weighted pair group method using arithmetic averages (WPGMA) are less preferable.

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In the literature an increasing interest in quantitative structure-retention relationships (QSRR) can be observed. After a short introduction on QSRR and other strategies proposed to deal with the starting point selection problem prior to method development in reversed-phase liquid chromatography, a number of interesting papers is reviewed, dealing with QSRR models for reversed-phase liquid chromatography. The main focus in this review paper is put on the different modelling methodologies applied and the molecular descriptors used in the QSRR approaches. Besides two semi-quantitative approaches (i.e. principal component analysis, and decision trees), these methodologies include artificial neural networks, partial least squares, uninformative variable elimination partial least squares, stochastic gradient boosting for tree-based models, random forests, genetic algorithms, multivariate adaptive regression splines, and two-step multivariate adaptive regression splines.

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A quantitative structure-retention relationship analysis was performed on the chromatographic retention data of 90 peptides, measured by gradient elution reversed-phase liquid chromatography, and a large set of molecular descriptors computed for each peptide. Such approach may be useful in proteomics research in order to improve the correct identification of peptides. A principal component analysis on the set of 1726 molecular descriptors reveals a high information overlap in the descriptor space. Since variable selection is advisable, the retention of the peptides is modeled with uninformative variable elimination partial least squares, besides classic partial least squares regression. The Kennard and Stone algorithm was used to select a calibration set (63 peptides) from the available samples. This set was used to build the quantitative structure-retention relationship models. The remaining 27 peptides were used as independent external test set to evaluate the predictive power of the constructed models. The UVE-PLS model consists of 5 components only (compared to 7 components in the best PLS model), and has the best predictive properties, i.e., the average error on the retention time is less than 30 s. When compared also to stepwise regression and an empirical model, the obtained UVE-PLS model leads to better and much better predictions, respectively.

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Several chemometric techniques were compared for their performance to determine the orthogonality and similarity between chromatographic systems. Pearson's correlation coefficient (r) based color maps earlier were used to indicate selectivity differences between systems. These maps, in which the systems were ranked according to decreasing or increasing dissimilarities observed in the weighted-average-linkage dendrogram, were now applied as reference method. A number of chemometric techniques were evaluated as potential alternative (visualization) methods for the same purpose. They include hierarchical clustering techniques (single, complete, unweighted-average-linkage, centroid and Ward's method), the Kennard and Stone algorithm, auto-associative multivariate regression trees (AAMRT), and the generalized pairwise correlation method (GPCM) with McNemar's statistical test. After all, the reference method remained our preferred technique to select orthogonal and identify similar systems.

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In order to select chromatographic starting conditions to be optimized during further method development of the separation of a given mixture, so-called generic orthogonal chromatographic systems could be explored in parallel. In this paper the use of univariate and multivariate regression trees (MRT) was studied to define the most orthogonal subset from a given set of chromatographic systems. Two data sets were considered, which contain the retention data of 68 structurally diversive drugs on sets of 32 and 38 chromatographic systems, respectively. For both the univariate and multivariate approaches no other data but the measured retention factors are needed to build the decision trees. Since multivariate regression trees are used in an unsupervised way, they are called auto-associative multivariate regression trees (AAMRT). For all decision trees used, a variable importance list of the predictor variables can be derived. It was concluded that based on these ranked lists, both for univariate and multivariate regression trees, a selection of the most orthogonal systems from a given set of systems can be obtained in a user-friendly and fast way.

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Multivariate adaptive regression splines (MARS) and a derived method two-step MARS (TMARS) were used for modelling the gastro-intestinal absorption of 140 drug-like molecules. The published absorption values for these molecules were used as response variable and calculated molecular descriptors as potential explanatory variables. Both methods were compared and their potential use in quantitative structure-activity relationship (QSAR) context evaluated. The predictive abilities of the models were studied using different sequences of Monte Carlo cross validation (MCCV). It was shown that both types of models had good predictive abilities and that for the data used, MARS gave better results than TMARS. It could be concluded that both methods could be valuable for QSAR modelling.

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The multivariate adaptive regression splines (MARS) methodology was applied to build quantitative structure-retention relationships (QSRRs). The response (dependent variable) in the MARS models consisted of the logarithms of the extrapolated retention factors (log k(w)) of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. A set of 266 molecular descriptors was used as predictor (independent) variables in the MARS model building. The optimal MARS model uses 34 basis functions to describe the retention and has acceptable predictive properties for new objects. The molecular descriptors included in the model describe hydrophobicity, molecular size, complexity, shape and polarisability. Some additional MARS models were created using alternative strategies. These include models with log P as the single predictor and models obtained with only the three most important molecular descriptors. The use of classification and regression trees (CART) as feature selection technique for predictor variables used in the MARS model was also investigated. Further, it is also studied whether allowing quadratic terms instead of interaction terms might lead to better MARS models.

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The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-retention relationship (QSRR) context on a data set consisting of the retentions of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. The response (dependent variable) in the tree models consisted of the predicted rention factor (log kw) of the solutes, while a set of 266 molecular descriptors was used as explanatory variables in the tree building. Molecular descriptors related to the hydrophobicity (log P and Hy) and the size (TPC) of the molecules were selected out of these 266 descriptors in order to describe and predict retention. Besides the above mentioned, CART was also able to select hydrogen-bonding and molecular complexity descriptors. Since these variables are expected from QSRR knowledge, it demonstrates the potential of CART as a methodology to understand retention in chromatographic systems. The potential of CART to predict retention and thus occasionally to select an appropriate system for a given mixture was also evaluated. Reasonably good prediction, i.e. only 9% serious misclassification, was observed. Moreover, some of the misclassifications probably are inherent to the data set applied.

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