Radiomics in psoriatic arthritis and breast cancer: Assessing disease burden and predicting survival through MR image analysis

Sammendrag Radiomics i psoriasisartritt og brystkreft: Vurdering av sykdom og prediksjon av overlevelse gjennom MR-bildeanalyser Psoriasisartritt er en kronisk inflammatorisk leddsykdom som utvikler seg hos pasienter med hudpsoriasis. Sykdommen innebærer betennelse i ett eller flere ledd og er heterogen med ulike kliniske mønstre. Behandling rettes mot betennelsene og avhenger av sykdommens aggressivitet. Tidlig diagnose og målrettet behandling er viktig for å forhindre progressiv leddskade, deformormasjoner og funksjonshemming som oppstår ved vedvarende betennelse. Brystkreft er den vanligste kreftformen hos kvinner i Norge. Mens kvinner diagnostisert med brystkreft i et tidlig stadium har høy overlevelsesrate, innebærer diagnosen avansert brystkreft en dårligere prognose. Tidlig prognostisk informasjon kan ha betydning for valg av behandling og veilede oppfølging etter behandling. Magnetisk resonans (MR) avbildning kan bistå i diagnose av psoriasisartritt, og brukes t rutinemessig for å vurdere behandlingsrespons i brystkreft. Bildene vurderes kvalitativt ved visuell inspeksjon. Radiologisk tolkning av bilder krever lang trening, er tidkrevende og kan vise høy inter-rater-variasjon. Radiomics, uthenting av kvantitative egenskaper fra medisinske bilder, kan forenkle tolkningav MR-bildene. Det er viktig å finne kvantitative mål fra MR som er pålitelige, sensitive og spesifikke for diagnostiske, prediktive og prognostiske formål. Hensikten med dette prosjektet er å bidra til utvikling av kvantitative analysemetoder for MRbilder og oppnå objektive og kvantitative MR-bildebaserte mål for diagnostiske og prognostiske formål. De spesifikke målene med dette prosjektet var å implementere et rammeverk for behandling og analyse av longitudinelle data innhentet med forskjellige skannere og protokoller, etablere kvantitative MR-bildebaserte mål for subtile beinmargsødem i ryggrad og iliosakralledd hos pasienter med psoriasisartritt, og vurdere prognostisk verdi av tekstur-egenskaper ekstrahert fra dynamisk kontrastforsterkede MR-bilder av lokalavansert brystkreft. Den første artikkelen evaluerte terskling for kvantifisering av benmargsødem i ryggrad og iliosakralledd hos pasienter med psoriasisartritt, og sammenlignet de kvantitative målene fra terskling med et semi-kvantitativt scoringssystem etablert av spondyloarthritis research consortium of Canada (SPARCC). Kvantitative mål fra terskling viste en signifikant positi

Simulation of basal ganglia's physiology in Parkinson's disease through a detailed multi-layer computational model

107 σ.Τα βασικά γάγγλια παίζουν σημαντικό ρόλο σε πολλές κινητικές διαταραχές. Μία από αυτές είναι η νόσος του Πάρκινσον, που προκαλείται από τον εκφυλισμό των κυττάρων που παράγουν έναν ειδικό νευροδιαμορφωτή, την ντοπαμίνη. Ωστόσο, οι ακριβείς νευροπαθολογικοί μηχανισμοί που συντελούνται στη νόσο του Πάρκινσον δεν έχουν κατανοηθεί πλήρως. Αντικείμενο της παρούσας διπλωματικής εργασίας αποτελεί η εξερεύνηση και επέκταση ενός ρεαλιστικού πολυεπίπεδου υπολογιστικού μοντέλου των βασικών γαγγλίων με σκοπό τη μελέτη του ρόλου των βασικών γαγγλίων στη νόσο του Πάρκινσον. Η μελέτη επικεντρώνεται στην επίδραση της μεταβολής της ισχύος των μετασυναπτικών δυναμικών, η οποία θεωρείται ότι διαμορφώνεται από τη ντοπαμίνη, σε φυσιολογική και παρκινσονική κατάσταση. Πιο συγκεκριμένα, εξετάζεται η υπόθεση ότι η ντοπαμίνη διαμορφώνει την ισχύ των μετασυναπτικών δυναμικών, διαμορφώνοντας τα όχι μόνο σε πλάτος, αλλά και σε διάρκεια. Η υπόθεση αυτή επιβεβαιώνεται από τις προσομοιώσεις, καθώς η χαρακτηριστικότερη παρκινσονική έκφραση, η κορυφή στην βήτα περιοχή συχνοτήτων των φασμάτων των δυναμικών τοπικού πεδίου του υποθαλαμικού πυρήνα, εμφανίζεται αν και μόνο αν μεταβληθεί τόσο το πλάτος, όσο και η διάρκεια των μετασυναπτικών δυναμικών. Η υλοποίηση του υπολογιστικού μοντέλου και η διεξαγωγή των προσομοιώσεων έγιναν με την βοήθεια του περιβάλλοντος προσομοίωσης NEURON.The basal ganglia play a central role in several movement disorders. One of them is Parkinson’s disease, which is caused by the degeneration of the cells that produce a special neuromodulator called dopamine. However, the exact neuropathological mechanisms underlying Parkinson’s disease are yet to be completely understood. The subject of this thesis is to explore and extend a detailed multi-level computational model of the basal ganglia, in order to study their role in Parkinson's disease. The study focuses on the effect of changes in the power of postsynaptic potentials, which is considered to be formed by dopamine, both in normal and parkinsonian state. More specifically, the considered hypothesis claims that dopamine modulates postsynaptic potentials, by altering, not only their amplitude, but also their duration. This hypothesis is confirmed by simulations, since the characteristic expression of Parkinson’s disease, the peak at the beta frequency range of the power spectral density function of local field potentials of the subthalamic nucleus, occurs if, and only if, the amplitude and the duration of postsynaptic potentials are changed. NEURON simulation environment was used in order to develop the computational model and run simulations.Ιωάννα Ι. Χροναίο

Quantifying bone marrow inflammatory edema in the spine and sacroiliac joints with thresholding

Abstract Background Psoriatic Arthritis (PsA) is a chronic inflammatory arthritis that develops in patients with psoriasis. Inflammatory edema in the spine may reflect subclinical disease activity and be a predictor of radiographic progression. A semi-quantitative method established by the spondyloarthritis research consortium of Canada (SPARCC) is commonly used to assess the disease activity in MR images of the spine. This study aims to evaluate thresholding for quantification of subtle bone marrow inflammation in the spine and the sacroiliac (SI) joints of patients with PsA and compare it with the SPARCC scoring system. Methods Short tau inversion recovery (STIR) MR images of the spine (N = 85) and the SI joints (N = 95) of patients with PsA (N = 41) were analyzed. A threshold was applied to visible bone marrow in order to mask areas with higher signal intensity, which are consistent with inflammation. These areas were considered as inflammatory lesions. The volume and relative signal intensity of the lesions were calculated. Results from thresholding were compared to SPARCC scores using linear mixed-effects models. The specificity and sensitivity of thresholding were also calculated. Results A significant positive correlation between the volumes and mean relative signal intensities, which were calculated by thresholding analysis, and the SPARCC scores was detected for both spine (p < 0.001) and SI joints (p < 0.001). For the spine, thresholding had sensitivity and specificity of 83% and 76% respectively, while for the SI joints the values were 51% and 88% respectively. Conclusions Thresholding allows quantification of subtle bone marrow inflammatory edema in patients with psoriatic arthritis, and could support SPARCC scoring of the spine. Improved image processing and inclusion of automatic segmentation are required for thresholding of STIR images to become a rapid and reliable method for quantitative measures of inflammation. Trial registration NCT02995460 (December 14, 2016) – Retrospectively registered

Evaluating the Impact of High Intensity Interval Training on Axial Psoriatic Arthritis Based on MR Images

High intensity interval training (HIIT) has been shown to benefit patients with psoriatic arthritis (PsA). However, magnetic resonance (MR) imaging has uncovered bone marrow edema (BME) in healthy volunteers after vigorous exercise. The purpose of this study was to investigate MR images of the spine of PsA patients for changes in BME after HIIT. PsA patients went through 11 weeks of HIIT (N = 19, 4 men, median age 52 years) or no change in physical exercise habits (N = 20, 8 men, median age 45 years). We acquired scores for joint affection and pain and short tau inversion recovery (STIR) and T1-weighted MR images of the spine at baseline and after 11 weeks. MR images were evaluated for BME by a trained radiologist, by SpondyloArthritis Research Consortium of Canada (SPARCC) scoring, and by extraction of textural features. No significant changes of BME were detected in MR images of the spine after HIIT. This was consistent for MR image evaluation by a radiologist, by SPARCC, and by texture analysis. Values of textural features were significantly different in BME compared to healthy bone marrow. In conclusion, BME in spine was not changed after HIIT, supporting that HIIT is safe for PsA patients

Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer.

Background The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. Purpose To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. Material and Methods This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. Results Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan–Meier analysis. Conclusion This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification