Kriščiukaitis, Algimantas

To assess the effectiveness of the benzyl benzoate and benzyl alcohol (BABB) solution in transparent teeth preparation compared to methyl salicylate using comprehensive digital and radiographic quantitative values analysis.

Collagenous colitis (CC) is diagnosed histologically and is characterised by a thickened subepithelial collagen band together with inflammatory and epithelial changes. Although routine haematoxylin and eosin (H&E) staining is sufficient for diagnosis in most cases, visual assessment of the collagen band can be challenging in borderline or heterogeneous specimens. Additional stains may be required in diagnostically difficult situations.

Patient positioning, together with physical changes in soft tissues during the course of radiotherapy, significantly affects the irradiation of targeted tissues and adjacent structures. This leads to a high risk of under-dosage of the tumour and/or over-dosage of critical normal structures during the late sessions of treatment. Accurate and timely identification of irradiation-affected tissue regions is highly valuable for adaptive radiotherapy planning. We propose a method for the identification and evaluation of specific computed tomography (CT) attenuation changes that can reveal the affected tissue regions. The search for correlated CT attenuation changes in the tumour and surrounding tissues, based on principal component analysis of series of intensity values in each fixed voxel, can reveal the actual three-dimensional region of irradiation-affected tissues for radiotherapy control and replanning.

Ensuring selective irradiation of target tissues is one of the biggest challenges in radiotherapy. Methods and devices of Image-Guided Radiotherapy (IGRT) are elaborated with the aim of ensuring that the prescribed radiation dose is delivered accurately to the tumour while minimising exposure to surrounding healthy tissues. Technical solutions ensure a few-millimetre, or even sub-millimetre precision of the irradiation beam, while with currently used mechanical means of patient positioning, we can expect much bigger positioning deviations, reaching even centimetre range. Patient positioning deviation to a certain extent is related to changes in soft tissue density and volume, which change during the period of treatment. Therefore, the discovery of reliable reference structures in routinely performed daily Cone-Beam Computed Tomograms (CBCT) was one of the aims of this study. Having the reliable reference structures, we carried out the retrospective estimation of patient position deviation during the whole treatment cycle and evaluated possible dynamics of unwanted irradiation of tumour-surrounding critical organs. The study was conducted in patients with head and neck cancer treated in the Lithuanian University of Health Sciences Kaunas Clinics Affiliated Hospital of Oncology, Department of Radiotherapy. Patients’ positioning was evaluated using volumetric images obtained by the CBCT machine integrated into the Halcyon V3.1 linear accelerator (Varian Medical Systems, Palo Alto, CA, USA). Custom-made algorithms of hard tissue segmentation and actual patient position estimation were elaborated in MATLAB (MathWorks, USA) environment. The hard tissue structures in volumetric images, in particular the mandible and part of the skull, were segmented and adjusted using mathematical morphology algorithms. We found these structures as reliable reference landmarks for patient position estimation. We found the deviation of actual patient position ranging from 1 to 3,5 mm, which resulted in changes in irradiation ranging from 0,016 to 0,057 Gy/fraction in the planned target volume and in critical surrounding organs (e.g. larynx, parotid, etc.) as well. The values indicate that it can cause significant damage to the surrounding organs. In conclusion, we state that specially selected hard tissue structures can serve as reliable landmarks of patient position, while soft tissues eventually change. The development of more precise image-guided radiotherapy methods can significantly reduce the damage to tumour-surrounding organs.

Maximisation of irradiation accuracy of malignant tissues is a key challenge on the way to optimal radiotherapy. Strategies to improve irradiation accuracy should balance the expected clinical benefit against the feasibility and procedural demands of the method used. This pilot study marks an initial step toward retrospectively evaluating patient positioning accuracy, analysing CBCT images in relation to clinical outcomes, and estimating actual irradiation of target and surrounding tissues. The CBCT images were acquired from head and neck patients treated with the Halcyon V3.1 linear accelerator. The algorithms for precise alignment of images, which made it possible to estimate the detailed changes in tumour tissue density during treatment sessions were developed in the MATLAB. The recalculation of the actual dose showed that even small positioning errors can lead to significant changes in the delivered dose, especially in areas where critical organs are affected.

In 1959, the Rector of Kaunas Medical Institute (KMI), Zigmas Januškevičius, wanted to expand the application areas of formal sciences and to encourage the use of mathematical statistics methods in medical scientific research. He asked Vilnius University Academician, Professor Jonas Kubilius to recommend a promising graduate for work in KMI. In response, Professor Jonas Kubilius recommended the talented and enterprising young mathematician Jonas Sapagovas [1]. After graduating from the Faculty of Physics and Mathematics of Vilnius University, Jonas Sapagovas began working as a research associate in the Scientific Laboratory of Kaunas Medical Institute. He became the first graduate mathematician to systematically engage in the process of medical studies and science. In 1962, the professor started to work at the Department of Physics of KMI and became its head in 1979 [1]. He led the department successfully for many years, significantly contributing to its activities and development. The main scientific area of the Professor was the application of mathematical statistics in analyzing health data. Systematically collaborating with medics, he published more than 50 scientific publications and actively presented research results in conferences. The Professor was also a long-time member of the Lithuanian Mathematical Society and the Statistical Union. The Professor was an exceptional educator who put his heart and soul into teaching. He had the ability to explain complex mathematical theories clearly and understandably. The Professor taught courses such as Basics of Higher Mathematics and Probability Theory, Mathematical Statistics and Informatics to students of mostly all faculties of KMI (now Lithuanian University of Health Sciences). In addition to theoretical lectures, he always conducted seminars, during which students learned to practically apply the acquired knowledge in assessing the effectiveness of treatment methods and interpreting the results of scientific research. The Professor was a co-author of 6 methodological teaching books and two textbooks for students of higher education institutions. In addition to academic and scientific work, the Professor was actively involved in institutional life: he was a member of the Senate, a member of numerous dissertation defense committees, and was active in developing several study programs. On his initiative, the first IBM personal computer class was established at the university, intended for students’ practical work. This conference presentation is intended to honor the contribution of Professor Jonas Sapagovas in introducing mathematical statistical methods in medical studies and science in Lithuania.

Gene expression is a quantitative estimate reflecting how much the information encoded in a gene is turned into a functional “outcome”, usually proteins or RNA molecules. Evaluation of gene expression levels can reveal peculiarities of cellular processes related with particular pathogenic mechanisms. The construction of biomarkers of certain diseases can be based on gene expression estimates. However, processing, analyzing and generalizing the gene expression data is challenging due to the inescapable variation of technical conditions in sophisticated biochemical process. It results in so called “batch effects” in estimates, when artifact differences appear not due to the sought genetic differences, but due to the variety of technical conditions. The normalization of gene expression levels is the solution and a crucial step in obtaining reliable results. The expression of so called „House-keeping“ genes, ensuring the basic functions of the cells can serve as normalizing values in such cases. We propose Principal Component Analysis [1] approach to concentrate the correlated variety of gene expression values into uncorrelated principal components. In that way variety of gene expression estimates concentrated in the same principal component as the ones from “House

• keeping” genes will be concerned as technical artifacts, while the others will reflect the sought epigenetic phenomena. The proposed method was used to validate expression levels of targeted gene set related to various complexity brain tumors. Data were collected by Oxford Nanopore [2] direct RNA Sequencing. Potential biomarker genes were identified as showing statistically different in Glioblastoma vs Low-grade-glioma cases.

Background. Educational simulation using synthetic organ phantoms becomes very popular in training of wide range medical professionals. However, in some cases only specially processed natural hard tissue preparations could help to gain particular valuable skills for future practices. One of major features of such preparations is translucency enabling comprehensive control of the educational process, e.g., instrument manipulations. Aim. The aim of this study was to elaborate a method for evaluation of optical properties of the preparations enabling assessment of its suitability and optimisation of the preparation process. Methods. This particular study was designed to compare optical properties of natural teeth preparations made using two chemical methods – a) the traditional, using methyl ester of salicylic acid, giving satisfactory results, but toxic, vs b) using benzyl benzoate and benzyl alcohol, from the first impression giving also satisfactory results, but being much less toxic. Translucent light images of 32 teeth were captured using a collimated white light source and a digital camera (C-P8, Optika, Italy), with the teeth positioned on a dental glass slab. A small piece of prosthodontic impression material (A-silicone Elite Transparent, Zhermack, Italy) was placed nearby to normalise exposure and white balance across all images. The optical properties of tooth translucency were analysed using custom-made image processing algorithms in the MatLab environment. The algorithm segmented the apical part of each tooth using superpixel technique. Optical properties of segmented apical part of each tooth were estimated in HSV colour scheme. Results. We did not find any statistical difference in optical properties of apical parts prepared by both methods. Statistical analysis (Mann–Whitney U test, p > 0.2) showed no significant difference in hue, saturation, nor in value between the two groups. Conclusions. The optical properties of the translucent teeth prepared by both methods were similar, so we advise to use less toxic method using benzyl benzoate and benzyl alcohol. The principle of evaluation of optical properties could be used also for assessment of other tissues and other processing methods.

Background. Microscopic colitis (MC) is an inflammation that affects the inner lining of the colon. This could lead to chronic and watery diarrhea, abdominal pains, faecal incontinence, and weight loss. The symptoms of the disease affect overall quality of life for the patient. Diagnosis of MC is done via biopsies examination by pathologist who checks the tissue under a microscope and looks for signs of the disease (increased inflammatory infiltrate in the lamina propria without significant crypt architectural distortion and intraepithelial lymphocytosis). The diagnosis is subjective and could lead to diagnostic discrepancies between evaluators and treatment plan failures. This study focuses on the development of an algorithm for pathologist to assist them in MC diagnosis. Aim. The aim of the current study was to develop an algorithm for MC localisation from microscopic images. Methods. Six hundred histological microscopic images (10 patients) were used to train the elaborated algorithm. Image pre-processing was done by dividing each image into small image elements (superpixels), using simple linear iterative clustering algorithm. After this procedure we applied machine learning approach to classify the pre-processed elements to disease and healthy tissues groups. Machine learning algorithm was pretrained using images, annotated by three pathologists. The proposed algorithm together with image pre-processing was implemented in MatLab development environment. Results. The model was tested on additional microscopic images from five patients (300 images). It showed accuracy of correct classification of analysed microscopic images equal to 0.81, sensitivity — 0.8 and specificity — 0.81. Conclusion. The proposed algorithm can assist pathologists in MC diagnosis and make it more objective.

Collagenous colitis (CC) is an inflammatory disease of the large bowel that causes chronic watery diarrhoea, abdominal pain, faecal incontinence, nightly defecation, and weight loss, resulting in significantly impaired quality of life. The diagnosis of CC is rather challenging as it can only be diagnosed upon histological examination of colonic biopsies taken from normal or near normal appearing mucosa. Current routine histological interpretation of biopsies involves subjective evaluation leading to inter-rater variability discrepancies in diagnosis and treatment plan. The aim of this study was to develop an algorithm for robust segmentation of light microscopy images of histological specimen slides identifying key areas containing important diagnostic features of CC. Images of histological specimens from 10 patients (~60 images per patient) were pre-segment-ed into superpixels using a simple linear iterative clustering algorithm. The areas containing candidate diagnostic features for identification of CC, in particular, the thickened subepithelial collagen layer – the essen-tial diagnostic feature, were marked by the experts. The feed – forward neural network containing three hidden layers with ten neurons in each was trained to identify the superpixels containing sought diagnostic fea-tures. The model was tested on 250 images from 5 patients not used for training and showed accuracy of 0.807, sensitivity – 0.801 and specificity – 0.813. The shown neural network’s ability to segment histology images could be used for assisted diagnostic process emphasising areas with candidate key features for identification of collagenous colitis.