Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Structural changes in the innercortex cells of soybean root nodules are induced by short-term exposure to high salt or oxygen concentrations

After a 2 h exposure of intact soybean nodules to high concentrations of NaCl (100mol m_?3) or oxygen (8OkPa O₂), morphometric computations carried out using an image analysis technique on semi-thin sections showed that both treatments induced a decrease in the area of the inner-cortex cells, which were then characterized by a tangential elongation. In contrast, no significant change in area occurred in the middle-cortex cells although their elongation decreased. Electron microscopic observations showed that in the inner-cortex cells changes included the presence of wall infoldings, an enlarged periplasmic space and a lobate nucleus whose chromatin distribution differed from that of the control. Structural changes also occurred in the endoplasmic reticulum, microbodies, mitochondria and plastids. From several of these changes, which are similar to those noted in osmocontractil cells in response to external stimuli, it can be hypothesized that the inner cortex may provide a potential mechanism for the control of oxygen diffusion through the nodules.     

Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis

Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules.     

A biomolecular recognition approach for the functionalization of cellulose with gold nanoparticles

Materials with new and improved functionalities can be obtained by modifying cellulose with gold nanoparticles (AuNPs) via the in situ reduction of a gold precursor or the deposition or covalent immobilization of pre‐synthesized AuNPs. Here, we present an alternative biomolecular recognition approach to functionalize cellulose with biotin‐AuNPs that relies on a complex of 2 recognition elements: a ZZ‐CBM3 fusion that combines a carbohydrate‐binding module (CBM) with the ZZ fragment of the staphylococcal protein A and an anti‐biotin antibody. Paper and cellulose microparticles with AuNPs immobilized via the ZZ‐CBM3:anti‐biotin IgG supramolecular complex displayed an intense red color, whereas essentially no color was detected when AuNPs were deposited over the unmodified materials. Scanning electron microscopy analysis revealed a homogeneous distribution of AuNPs when immobilized via ZZ‐CBM3:anti‐biotin IgG complexes and aggregation of AuNPs when deposited over paper, suggesting that color differences are due to interparticle plasmon coupling effects. The approach could be used to functionalize paper substrates and cellulose nanocrystals with AuNPs. More important, however, is the fact that the occurrence of a biomolecular recognition event between the CBM‐immobilized antibody and its specific, AuNP‐conjugated antigen is signaled by red color. This opens up the way for the development of simple and straightforward paper/cellulose‐based tests where detection of a target analyte can be made by direct use of color signaling.     

Validation of a deformable image registration produced by a commercial treatment planning system in head and neck

In recent years one of the areas of interest in radiotherapy has been adaptive radiation therapy (ART), with the most efficient way of performing ART being the use of deformable image registration (DIR). In this paper we use the distances between points of interest (POIs) in the computed tomography (CT) and the cone beam computed tomography (CBCT) acquisition images and the inverse consistence (IC) property to validate the RayStation treatment planning system (TPS) DIR algorithm. This study was divided into two parts: Firstly the distance-accuracy of the TPS DIR algorithm was ascertained by placing POIs on anatomical features in the CT and CBCT images from five head and neck cancer patients. Secondly, a method was developed for studying the implication of these distances on the dose by using the IC. This method compared the dose received by the structures in the CT, and the structures that were quadruply-deformed. The accuracy of the TPS was 1.7 ± 0.8 mm, and the distance obtained with the quadruply-deformed IC method was 1.7 ± 0.9 mm, i.e. the difference between the IC method multiplied by two, and that of the TPS validation method, was negligible. Moreover, the IC method shows very little variation in the dose-volume histograms when comparing the original and quadruply-deformed structures. This indicates that this algorithm is useful for planning adaptive radiation treatments using CBCT in head and neck cancer patients, although these variations must be taken into account when making a clinical decision to adapt a treatment plan.     

Molecular dynamics simulations of alpha2 --> 8-linked disialoside: conformational analysis and implications for binding to proteins.

Computational methods have played a key role in elucidating the various three-dimensional structures of oligosaccharides. Such structural information, together with other experimental data, leads to a better understanding of the role of oligosaccharide in various biological processes. The disialoside Neu5Ac-alpha2-->8-Neu5Ac appears as the terminal glycan in glycoproteins and glycolipids, and is known to play an important role in various events of cellular communication. Neurotoxins such as botulinum and tetanus require Neu5Ac-alpha2 --> 8-Neu5Ac for infecting the host. Glycoconjugates containing this disialoside and the enzymes catalyzing their biosynthesis are also regulated during cell growth, development, and differentiation. Unlike other biologically relevant disaccharides that have only two linkage bonds, the alpha2-->8-linked disialoside has four: C2-O, O-C8', C8'-C7', and C7'-C6'. The present report describes the results from nine 1 ns MD simulations of alpha2-->8-linked disialoside (Neu5Ac-alpha2-->8-Neu5Ac); simulations were run using GROMOS96 by explicitly considering the solvent molecules. Conformations around the O-C8' bond are restricted to the +sc/+ap regions due to stereochemical reasons. In contrast, conformations around the C2-O and C8'-C7' bonds were found to be largely unrestricted and all the three staggered regions are accessible. The conformations around the C7'-C6' bond were found to be in either the -sc or the anti region. These results are in excellent agreement with the available NMR and potential energy calculation studies. Overall, the disaccharide is flexible and adopts mainly two ensembles of conformations differing in the conformation around the C7'-C6' bond. The flexibility associated with this disaccharide allows for better optimization of intermolecular contacts while binding to proteins and this may partially compensate for the loss of conformational entropy that may be incurred due to disaccharide's flexibility.