Multiparametric brain MRI and deep learning models applied to segmentation and prediction in patients with glioma

Avhandlingen undersøker metoder og algoritmer for hjernetumor (gliom) segmenter- ing, med prediksjon av histologisk gradering, og anatomisk profilering fra MR-bilder. En viktig motivasjon for dette arbeidet har vært betydningen for både klinisk praksis og grunnleggende biologisk forskning av deteksjon, kvantifisering og visualisering av hjernesvulster og distinkte svulstkomponenter, basert på informasjon som kan hentes fra de digitale 3-dimensjonale bildedata i en multiparametrisk MRI-undersøkelse av hodet og hjernen. En viktig begrunnelse for dette arbeidet er ny innsikt fra dyplærings- metodologi, multiparametrisk MRI, og bildebaseret deteksjon og oppfølging av pasien- ter med gliom sett i sammenheng med persontilpasset medisin og presisjonsavbildning. De viktigste bidragene fra denne avhandling er samlet i tre arbeider (papere) og tilhørende program-kode og kan tre-deles i henhold til hvert av disse delarbeider: (i) Dyplærings-basert segmentering av subregioner i gliomer (Paper I) (ii) Dyplærings-basert ikke-invasiv gradering av gliomer (Paper II) (iii) Metode for automatisert anatomisk lokalisering og profilering av gliom (Paper III) Studien er basert på flere tusen MRI-opptak fra internasjonale, vel kurerte databaser, der pasientene alle har gitt sitt informerte samtykke til å dele disse data til forskn- ingsformål, og er et eksempel på viktigheten av åpen vitenskap og tilrettelegging for reproduserbar forskning. Mer spesifikt representerer resultatene produsert i min avhandling gjennom Paper I, II, og III flere fremskritt innen muliggjørende teknologier for vurdering av gliom ved bruk av multiparametrisk MRI (mpMRI). Disse forbedringene er relatert til flere nøkkelområder: Avansert gliom-segmentering: Avhandlingen introduserer en modifisert U-Net arkitektur, kalt MEU-Net, for segmentering av gliom sub-regioner fra mpMRI-opptak. Dette representerer et teknologisk fremskritt i segmenteringsnøyaktighet og detaljer- ing, og muliggjør en mer presis analyse av multikompartment-svulstegenskaper og omkringliggende vev. Denne forbedringen kan bidra til persontilpasset behandlings- planlegging og gunstigere pasientutfall, da nøyaktig segmentering direkte påvirker forståelsen av svulstens karakteristikker og oppførsel. Automatisert gliom-gradering: Et annet betydelig bidrag er utviklingen av modeller for automatisert gliom-gradering ved bruk av avanserte maskinlæringsteknikker, spesielt konvolusjonelle nevrale nettverk. Denne tilnærmingen kan styrke radiologens diagnostikk og øke konsistensen og nøyaktigheten av diagnoser, noe som er spesielt viktig ved vurdering av lavgradige- versus høygradige gliomer. Denne muliggjørende teknologi representerer et betydelig fremskritt innen nevro-onkologisk behandling, da den assisterer den ikke-invasive vurderingen av svulstens aggressivitet og vil kunne gi nyttig informasjon ved valg av behandlingsstrategier. Presisjonsavbildning og persontilpassede behandlingsstrategier: Avhandlingens tilnærming til anatomisk profilering av lokalisering- og (makroskopisk) utbredelse av gliom, kan lede til betydelige forbedringer i presisjonsavbildning og personltilpassede behandlingsstrategier. Vår tilnærming komplementerer eksisterende verktøy som Raidionics, som bruker en enkelt MRI-kanal for tumor-segmentering. Arbeidet i Paper III bruker forhåndsberegnet gliom-segmentering (såkalt "ground-truth", eller en hvilken som helst state-of-the-art segmenteringsmetode) gjerne er basert på flere kanaler i et mpMRI-opptak, og gir dermed en rik spatial- og (grov) patofysiologisk oversikt over forskjellige svulst-regioner (ET, NCR og ED). Denne mangefasetterte tilnærmingen tillater dermd en mer nøyaktig og detaljert forståelse av svulstens anatomi og makroskopiske oppførsel. Fleksibilitet i bildeanalyse: Forskningen adresserer også praktiske utfordringer i avbildning, som tilfeller med manglende sekvenser eller vanskeligheter med bilde- registrering. Den tilpassede pipeline fungerer med kun T1c- og T2-sekvenser, og tilbyr et praktisk alternativ når den fullstendige multiparametriske protokollen av MRI-sekvenser ikke er mulig å bruke. Denne fleksibiliteten er avgjørende for praktisk- kliniske applikasjoner der avbildningsforholdene ikke alltid er ideelle. To-trinn pipeline for økt nøyaktighet: Den foreslåtte to-trinns pipeline segmenterer først svulstregionene og deretter bruker de kubiske "patchene" hentet fra disse re- gionene for grad-prediksjon. Denne tilnærmingen adresserer begrensningene ved bruk av spatialt komplette MRI-bilder for prediksjon, der modeller ellers kan gjøre bruk av ikke-svulstregioner i sin prediksjoner av gliomets gradering. Metoden forbedrer derved tolkbarheten og nøyaktigheten, og gir trolig et mer pålitelig diagnostisk verktøy enn "enkeltoppgave-metoder" fokusert kun på ende-til-ende gliom-gradering. Disse bidragene representerer samlet sett nye fremskritt og økt lokal kompetense innen dette forskningsfeltet, og kan potensielt lede til mer effektive og persontilpassede behandlingsstrategier for pasienter med gliom.This thesis investigates methods and algorithms for brain tumor (glioma) segmentation, grade prediction, and anatomical profiling from magnetic resonance (MR) images. A major motivation for this work has been the importance to both clinical practice and basic biological research of the detection, quantification and visualisation of brain tumors and distinct tumor components embedded in multiparametric brain MRI recordings. A major rationale for this work is new insight from deep learning methodologies, multiparametric MRI, and imaging-based detection and follow-up of patients with glioblastoma in the context of personalized and precision medicine. In short, the main contributions of this thesis are threefold: (i) Deep learning based segmentation of glioma sub-regions (Paper I) (ii) Deep learning-based non-invasive grading of glioma (Paper II) (iii) A method for automated anatomical localization and profiling of glioma (Paper III) More specifically, the results produced in my thesis through Papers I, II, and III represent several advancements in enabling technologies for glioma assessment using multiparametric MRI (mpMRI). These improvements are related to several key areas: Advanced Glioma Segmentation: The thesis introduces a modified U-Net archi- tecture, termed MEU-Net, for the segmentation of glioma sub-regions from mpMRI scans. This represents a technological advancement in segmentation accuracy and detail, enabling precise analysis of multicompartment tumor features and surrounding tissue. This improvement is crucial for better treatment planning and patient out- comes, as accurate segmentation directly influences the understanding of the tumor’s characteristics and behavior. Automated Glioma Grading: Another significant contribution is the development of models for automated glioma grading using advanced machine learning techniques, particularly convolutional neural networks. This automation can empower radiologists and increase the consistency and accuracy of diagnoses, which is especially vital in distinguishing between low-grade and high-grade gliomas. The technological leap of mpMRI-based glioma grading represents a significant step forward in neuro- oncological care, as it aids in the non-invasive assessment of the tumor’s aggressiveness and, together with light microscopy, informs appropriate treatment strategies. Precision Imaging and Personalized Treatment Strategies: The thesis’ approach to glioma profiling offers substantial improvements in precision imaging and personalized treatment strategies. Our approach complements existing tools like Raidionics, which rely on a single MRI channel for tumor segmentation. This work uses pre-computed tumor segmentations (the ground truth, or any state-of-the-art method) that depend on multiple channels in mpMRI, providing a comprehensive view of different tumor compartments (enhancing tumor, necrotic tumor core, edema). This multi-faceted approach allows for an accurate and detailed understanding of the tumor’s anatomy and behavior. Flexibility in Imaging Analysis: The research also addresses practical challenges in imaging, such as cases with missing sequences or image registration difficulties. The adapted pipeline works with just two MRI (T1c and T2) sequences, offering a practical alternative when the full suite of MRI sequences is not feasible to use. This flexibility is crucial for real-world applications where imaging conditions are not always ideal. Two-Phase Pipeline for Increased Accuracy: The proposed two-phase pipeline first segments the tumor regions and then uses the cubic patches extracted from these regions for grade prediction. This approach addresses the limitations of using full MRI images for grade prediction, where models might otherwise make predictions using non-tumor regions. This method enhances interpretability and accuracy, providing a more reliable diagnostic tool than single-task methods focused only on glioma grading. The thesis also makes use of several thousand MRI recordings from international, well-curated databases, where all the patients have given their informed consent to share these data for research purposes. It is an example of the importance of open science and facilitation for reproducible research. Collectively, these advancements represent important steps in the field, potentially leading to more effective and personalized treatment strategies for glioma patients.Doktorgradsavhandlin

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI

Background Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results For CE, median Dice scores were 0.81 (95% CI 0.71–0.83) and 0.82 (95% CI 0.74–0.84) for operator-1 and operator-2, respectively. For NE, median Dice scores were 0.65 (95% CI 0.56–0,69) and 0.63 (95% CI 0.57–0.67), respectively. Comparing volume sizes, we found excellent intra-class correlation coefficients of 0.90 (P .01) for non-responders, and 0.80 (P = .05) for intermediate/mixed responders. Conclusions HD-GLIO was feasible for RT target delineation and MRI tumor volume assessment. CE/NE tumor-compartment growth correlation showed potential to predict clinical response to treatment.publishedVersio

OUTCOME OF STAGE T1 RENAL CELL CARCINOMA TREATED WITH PARTIAL NEPHRECTOMY: INITIAL EXPERIENCES FROM A TEACHING HOSPITAL IN BANGLADESH

Background: Renal cell carcinoma accounts for 85% of all solid tumors of the kidney. For many years, radical nephrectomy was the stan¬dard treatment for RCC. Partial nephrectomy has gradual¬ly replaced radical nephrectomy over the past decade, es¬pecially for T1 stage renal cell carcinoma. However, the benefit of partial nephrectomy on oncolog¬ic outcomes is not well known. Objective: to investigate the clinical outcome of partial nephrectomy on T1 renal cell carcinoma. Methods: This prospective observational study was conducted in a single unit of urology department of Dhaka Medical College Hospital, Bangladesh from the period September 2014 to September 2017. Fourteen patients underwent partial nephrectomy during this period with renal mass based on eligibility criteria. Two follow up was done at three months and six months. Result: Mean age of the patients undergoing surgery was 52.0± 3.8 (46.0 to 57.0 years) years. For the majority of the patients, tumour size was in a range of 3-7 cm. Average operative time was 90 minutes and mean ischaemic time was 16.5 ± 4.6 minutes (14.5 to 21.0 minutes). Histopathological reports correlated with clinical diagnosis and showed adequate surgical clear margin in every case. There was no recurrence of tumour noticed during the two follow up periods. The different investigation did not reveal the impaired renal functional test during the follow-up period. Conclusion: The clinical outcome of partial nephrectomy was found better in this study. Partial nephrectomy has the potential to replace radical nephrectomy for managing T1 tumours. However, there are some controversies regarding the post-operative oncological outcome. More studies are recommended to investigate the effect of partial nephrectomy for T1 tumours

An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

Impact of MRI technology on Alzheimer's disease detection

Theoretical thesis.Bibliography: pages 54-60.Statement of Originality -- Abstract -- Table of contents -- List of figures -- List of tables -- Acknowledgement -- Alzheimer's Disease Neuroimaging Initiative (ADNI) Acknowledgement -- 1. Introduction -- 2. Background and related works -- 3. AD Diagnostic models -- 4. Data and experimental work -- 5. Result and discussion -- 6. ConclusionAlzheimer's disease (AD) can be detected using magnetic resonance imaging (MRI) based features and supervised classifiers. The subcortical and ventricular volumes change for AD patients. These volumes can be extracted from MRI by tools such as Free Surfer and multi-atlas-based likelihood fusion (MALF) algorithm. Medical imaging centers typically use MRI protocols for brain scanning.These protocol differences include different scanner models with various operating parameters. The scanner models can have the same or different field strengths. A key factor in classifying multicentric MR subject images having different protocols is how different scanner models affect the extraction of features, and subsequent classification performance of a supervised classifier. We have investigated the classification performance of FreeSurfer and MALF based volume features together with Radial Basis Function Support Vector Machine and Extreme Learning Machine across different imaging protocols. We have also investigated both FreeSurfer and MALF, whose defined regions of the brain are most effective for the detection of the disease over different protocols. Our study result indicates marginal differences in classification performance across scanner models with the same or different field strengths when differentiating AD, Mild Cognitive Impairment, and Normal Controls.We have also observed differences in ranking order of the most effective regions.1 online resource (69 pages

Twin SVM-Based Classification of Alzheimer’s Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA

Alzheimer’s disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer’s causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods

Diabetes Mellitus: Insights from Epidemiology, Biochemistry, Risk Factors, Diagnosis, Complications and Comprehensive Management

Diabetes mellitus has become a serious and chronic metabolic disorder that results from a complex interaction of genetic and environmental factors, principally characterized by hyperglycemia, polyuria, and polyphagia. Uncontrolled high blood sugar can result in a host of diabetic complications. Prolonged diabetes leads to serious complications some of which are life-threatening. The prevalence of diabetes patients is rising at epidemic proportions throughout the world. Every year, a major portion of the annual health budget is spent on diabetes and related illnesses. Multiple risk factors are involved in the etiopathogenesis of the disease and turning the disease into an epidemic. Diabetes, for which there is no cure, apparently can be kept under control by maintaining self-care in daily living, effective diabetes education, with comprehensive improvements in knowledge, attitudes, skills, and management. In this review, we focused on the biochemical aspects of diabetes, risk factors including both environmental and genetic, disease complications, diagnosis, management, and currently available medications for the treatment of diabetes

Predicting and Designing Epitope Ensemble Vaccines against HTLV-1

The infection mechanism and pathogenicity of Human T-lymphotropic virus 1 (HTLV-1) are ambiguously known for hundreds of years. Our knowledge about this virus is recently emerging. The purpose of the study is to design a vaccine targeting the envelope glycoprotein, GP62, an outer membrane protein of HTLV-1 that has an increased number of epitope binding sites. Data collection, clustering and multiple sequence alignment of HTLV-1 glycoprotein B, variability analysis of envelope Glycoprotein GP62 of HTLV-1, population protection coverage, HLA-epitope binding prediction, and B-cell epitope prediction were performed to predict an effective vaccine. Among all the predicted peptides, ALQTGITLV and VPSSSTPL epitopes interact with three MHC alleles. The summative population protection coverage worldwide by these epitopes as vaccine candidates was found nearly 70%. The docking analysis revealed that ALQTGITLV and VPSSSTPL epitopes interact strongly with the epitope-binding groove of HLA-A*02:03, and HLA-B*35:01, respectively, as this HLA molecule was found common with which every predicted epitope interacts. Molecular dynamics simulations of the docked complexes show they form stable complexes. So, these potential epitopes might pave the way for vaccine development against HTLV-1

The polymorphic landscape analysis of GATA1 exons uncovered the genetic variants associated with higher thrombocytopenia in dengue patients.

The current study elucidated an association between gene variants and thrombocytopenia through the investigation of the exonic polymorphic landscape of hematopoietic transcription factor-GATA1 gene in dengue patients. A total of 115 unrelated dengue patients with dengue fever (DF) (N = 91) and dengue hemorrhagic fever (DHF) (N = 24) were included in the study. All dengue patients were confirmed through detection of NS1 antigen, IgM, and IgG antibodies against the dengue virus. Polymerase chain reaction using specific primers amplified the exonic regions of GATA1 while Sanger sequencing and chromatogram analyses facilitated the identification of variants. Variants G>A (at chX: 48792009) and C>A (at chX: 4879118) had higher frequency out of 13 variants identified (3 annotated and 10 newly recognized). Patients carrying either nonsynonymous or synonymous variants had significantly lower mean values of platelets compared to those harboring the reference nucleotides (NC_000023.11). Further analyses revealed that the change in amino acid residue leads to the altered three-dimensional structure followed by interaction with neighboring residues. Increased stability of the protein due to substitution of serine by asparagine (S129N at chX: 48792009) may cause increased rigidity followed by reduced structural flexibility which may ultimately disturb the dimerization (an important prerequisite for GATA1 to perform its biological activity) process of the GATA1 protein. This, in turn, may affect the function of GATA1 followed by impaired production of mature platelets which may be reflected by the lower platelet counts in individuals with such variation. In summary, we have identified new variants within the GATA1 gene which were found to be clinically relevant to the outcome of dengue patients and thus, have the potential as candidate biomarkers for the determination of severity and prognosis of thrombocytopenia caused by dengue virus. However, further validation of this study in a large number of dengue patients is warranted. Trial Registration: number SLCTR/2019/037

Immunogenicity of a killed bivalent whole cell oral cholera vaccine in forcibly displaced Myanmar nationals in Cox's Bazar, Bangladesh.

After the large influx of Rohingya nationals (termed Forcibly Displaced Myanmar National; FDMN) from Rakhine State of Myanmar to Cox's Bazar in Bangladesh, it was apparent that outbreaks of cholera was very likely in this setting where people were living under adverse water and sanitation conditions. Large campaigns of oral cholera vaccine (OCV) were carried out as a preemptive measure to control cholera epidemics. The aim of the study was to evaluate the immune responses of healthy adults and children after administration of two doses of OCV at 14 days interval in FDMN population and compare with the response observed in Bangladeshi's vaccinated earlier. A cross-sectional immunogenicity study was conducted among FDMNs of three age cohort; in adults (18+years; n = 83), in older children (6-17 years; n = 63) and in younger children (1-5 years; n = 80). Capillary blood was collected at three time points to measure vibriocidal antibodies using either plasma or dried blood spot (DBS) specimens. There was a significant increase of responder frequency of vibriocidal antibody titer at day 14 in all groups for Vibrio cholerae O1 (Ogawa/Inaba: adults-64%/64%, older children-70%/89% and younger children-51%/75%). There was no overall difference of vibriocidal antibody titer between FDMN and Bangladeshi population at baseline (p = 0.07-0.08) and at day 14, day 28 in all age groups for both serotypes. The seroconversion rate and geometric mean titer (GMT) of either serotype were comparable using both plasma and DBS specimens. These results showed that OCV is capable of inducing robust immune responses in adults and children among the FDMN population which is comparable to that seen in Bangladeshi participants in different age groups or that reported from other cholera endemic countries. Our results also suggest that the displaced population were exposed to V. cholerae prior to seeking shelter in Bangladesh