Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders

The Safe and Effective Clinical Deployment of Artificial Intelligence Tools

18 million new cancer cases are diagnosed each year. Roughly half of these patients will be treated with radiation therapy, a complex technique that requires an interdisciplinary team of clinical staff and expensive equipment to be delivered safely. Cancer centers in Low- and Middle-Income Countries (LMIC) have an especially difficult time meeting the demands of radiation therapy as the complexity of treatment techniques increase, with only 37% of patients in these regions having access to the care they need. Artificial Intelligence (AI) based tools are being developed to simplify the treatment planning and quality assurance processes to increase the number of patients who can be treated, as well as improving the quality of their treatment plans. While AI techniques have shown great promise, with any new technology it is important to not only assess the potential benefits, but also the associated risk. To this end, we have performed a risk assessment of our in-house automated treatment planning system, the Radiation Planning Assistant, to identify points of risk and subsequently develop appropriate quality assurance and training resources to minimize patient risk. To identify points of risk, a failure mode and effects analysis was performed by a multidisciplinary team of clinicians and software developers. Changes were then made to limit the risk of 76% of high-risk failures. These risk points were then incorporated into hazard testing, and we found that 62% of errors could be detected before a plan was created in the RPA. The user interface was then modified to limit the number of errors that will be propagated into the automatic planning process. Following the changes made to optimize the safety of the user interface, the efficacy of error detection during the plan review process was assessed. A custom checklist was developed to guide the review of automatically generated treatment plans, based on the results of our FMEA and AAPM TG-275. During final physics plan checks, when utilizing the customized checklist, we found an increase in the rate of error detection by 20% for physicists and 17% for medical physics residents. An end-to-end test was then performed to evaluate the entirety of the RPA training and deployment procedure for new users. Users were asked to review training materials and generate 10 treatment plans, including all treatment sites available in the RPA. Following training, 100% of the errors present in these plans were detected and users reported that the developed training materials provided them with all information needed to generate safe, high-quality, treatment plans. Finally, a real-time contour monitoring system was developed to limit the risk of systematic errors and detect abnormalities in the contouring process that could be attributed to software error, off-label use, or automation bias. In conclusion, we have optimized the safety and efficacy of the RPA training, quality assurance, and deployment processes. This evaluation has allowed us to not only maximize the impact of our automated treatment planning tool, the RPA, but has also generated results that should be used to inform the development of safe AI software and clinical deployment procedures, in future clinical environments

Scaling Up Medical Visualization : Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication

Medisinsk visualisering er en av de mest applikasjonsrettede områdene av visualiseringsforsking. Tett samarbeid med medisinske eksperter er nødvendig for å tolke medisinsk bildedata og lage betydningsfulle visualiseringsteknikker og visualiseringsapplikasjoner. Kreft er en av de vanligste dødsårsakene, og med økende gjennomsnittsalder i i-land øker også antallet diagnoser av gynekologisk kreft. Moderne avbildningsteknikker er et viktig verktøy for å vurdere svulster og produsere et økende antall bildedata som radiologer må tolke. I tillegg til antallet bildemodaliteter, øker også antallet pasienter, noe som fører til at visualiseringsløsninger må bli skalert opp for å adressere den økende kompleksiteten av multimodal- og multipasientdata. Dessuten er ikke medisinsk visualisering kun tiltenkt medisinsk personale, men har også som mål å informere pasienter, pårørende, og offentligheten om risikoen relatert til visse sykdommer, og mulige behandlinger. Derfor har vi identifisert behovet for å skalere opp medisinske visualiseringsløsninger for å kunne håndtere multipublikumdata. Denne avhandlingen adresserer skaleringen av disse dimensjonene i forskjellige bidrag vi har kommet med. Først presenterer vi teknikkene våre for å skalere visualiseringer i flere modaliteter. Vi introduserer en visualiseringsteknikk som tar i bruk små multipler for å vise data fra flere modaliteter innenfor et bildesnitt. Dette lar radiologer utforske dataen effektivt uten å måtte bruke flere sidestilte vinduer. I det neste steget utviklet vi en analyseplatform ved å ta i bruk «radiomic tumor profiling» på forskjellige bildemodaliteter for å analysere kohortdata og finne nye biomarkører fra bilder. Biomarkører fra bilder er indikatorer basert på bildedata som kan forutsi variabler relatert til kliniske utfall. «Radiomic tumor profiling» er en teknikk som genererer mulige biomarkører fra bilder basert på første- og andregrads statistiske målinger. Applikasjonen lar medisinske eksperter analysere multiparametrisk bildedata for å finne mulige korrelasjoner mellom kliniske parameter og data fra «radiomic tumor profiling». Denne tilnærmingen skalerer i to dimensjoner, multimodal og multipasient. I en senere versjon la vi til funksjonalitet for å skalere multipublikumdimensjonen ved å gjøre applikasjonen vår anvendelig for livmorhalskreft- og prostatakreftdata, i tillegg til livmorkreftdataen som applikasjonen var designet for. I et senere bidrag fokuserer vi på svulstdata på en annen skala og muliggjør analysen av svulstdeler ved å bruke multimodal bildedata i en tilnærming basert på hierarkisk gruppering. Applikasjonen vår finner mulige interessante regioner som kan informere fremtidige behandlingsavgjørelser. I et annet bidrag, en digital sonderingsinteraksjon, fokuserer vi på multipasientdata. Bildedata fra flere pasienter kan sammenlignes for å finne interessante mønster i svulstene som kan være knyttet til hvor aggressive svulstene er. Til slutt skalerer vi multipublikumdimensjonen med en likhetsvisualisering som er anvendelig for forskning på livmorkreft, på bilder av nevrologisk kreft, og maskinlæringsforskning på automatisk segmentering av svulstdata. Som en kontrast til de allerede fremhevete bidragene, fokuserer vårt siste bidrag, ScrollyVis, hovedsakelig på multipublikumkommunikasjon. Vi muliggjør skapelsen av dynamiske og vitenskapelige “scrollytelling”-opplevelser for spesifikke eller generelle publikum. Slike historien kan bli brukt i spesifikke brukstilfeller som kommunikasjon mellom lege og pasient, eller for å kommunisere vitenskapelige resultater via historier til et generelt publikum i en digital museumsutstilling. Våre foreslåtte applikasjoner og interaksjonsteknikker har blitt demonstrert i brukstilfeller og evaluert med domeneeksperter og fokusgrupper. Dette har ført til at noen av våre bidrag allerede er i bruk på andre forskingsinstitusjoner. Vi ønsker å evaluere innvirkningen deres på andre vitenskapelige felt og offentligheten i fremtidige arbeid.Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data. This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition. Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.Doktorgradsavhandlin

Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities from Thursday, October 26, 2023

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities on October 26, 2023.https://corescholar.libraries.wright.edu/celebration_abstract_books/1001/thumbnail.jp

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

모세관 현상 기반의 패터닝 기법을 활용한 고효율 삼차원 면역세포 항암효능 평가 플랫폼

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 전누리.Organs-on-chips have been developed for recapitulating human organ functions in in vitro as microfabrication techniques meet biology since the early 2000s. Specifically, polydimethylsiloxane (PDMS) based microfluidic devices enabled to mimic organ functions by providing spatially compartmented cell patterning for culturing cells with in vivo like layout. The selective cell patterning enabled 3D cell culture and spatiotemporal analysis which were challenging to conduct with conventional cell culturewares such as petri-dishes, flasks, and well-plates. However, traditional organs-on-chips have limitations in salability, experimental throughput, and absence of standard due to their closed channel designs based on PDMS. Here, we introduce two capillarity guided patterning (CGP) methods by integrating microstructures with conventional cell culturewares. First, we fabricated micropillar arrays on open polystyrene (PS) surfaces and the micropillars can capture liquids swept over the surface. Using the devices, we demonstrated 3D culture applications, single cell capturing and retrieval and multiple cell co-culture. Second, we integrated rail-structures with microplate. Beneath a rail-structure, hydrogel precursors can selectively remain according to meniscus dynamics when the pre-loaded precursors are aspirated. These two CGP methods can be produced with injection molding and provide enhanced experimental throughput. Using the rail-based CGP method, we developed a 3D cytotoxicity assay for cancer immunotherapy based on an injection molded plastic culture (CACI-IMPACT) device to assess killing abilities of cytotoxic lymphocytes in 3D microenvironment through a spatiotemporal analysis of the lymphocytes and cancer cells embedded in 3D extra cellular matrix (ECM). Owing to the aspiration-mediated patterning, hydrogel precursors can be patterned in 12 wells within 30 s. For functional evaluation of the cytotoxic lymphocytes engineered for cancer immunotherapy, HeLa cells encapsulated by collagen matrix were patterned beneath low rails and NK-92 cells were loaded into the channel formed by the collagen matrix. We observed infiltration, migration and killing activity of NK-92 cells against HeLa cells in collagen matrix. Through image-based analysis, we found ECM significantly influences migration and cytotoxicity of lymphocytes. Hence, the CACI-IMPACT platform has the potential to be used for pre-clinical evaluation of ex vivo engineered cytotoxic lymphocytes for cancer immunotherapy against solid tumors, and the CGP methods are expected to accelerate the commercialization of organs-on-chips.장기모사칩은 2000년대 초부터 마이크로 공정 기술이 생물학적 연구에 활용됨에 따라 인간 장기 기능을 모사하기 위해 개발되었다. 구체적으로, polydimethylsiloxane (PDMS) 기반 미세유체 장치는 공간적으로 구분된 세포 패터닝을 가능케 함으로써 생체와 유사한 구조로 세포를 배양할 수 있게 해주었다. 이러한 세포 패터닝은 페트리 디쉬, 플라스크, 혹은 웰플레이트와 같은 기존의 세포 배양 도구에서는 수행하기 어려운 삼차원 세포 배양과 그 안에서의 시공간적 분석을 가능하게 하였다. 하지만, 종래의 장기모사칩은 PDMS에 기반한 닫힌 형태의 채널 설계로 인해 낮은 생산성, 낮은 실험 효율, 낮은 장비 호환성을 갖는다. 따라서, 본 연구는 대중적인 세포 배양 장치들에 마이크로 구조물을 통합한 두가지 모세관 현상 기반의 패터닝 방법을 제시한다. 첫번째 방법은 페트리 디쉬나 polystyrene (PS) 필름과 같이 개방된 PS 표면에 마이크로 기둥 어레이를 제작하여 그 위에서 액체가 쓸려 지나갈 때 기둥 구조물들 사이에 액체를 포획하는 방식이다. 마이크로 기둥 어레이의 배치에 따라 나노리터부터 마이크로리터에 이르는 액체를 빠르게 패터닝할 수 있게 한다. 이러한 기둥 구조를 활용하면 다양한 세포의 배치 및 배양이 가능하여, 본 연구에서는 삼차원 환경에서의 단일세포 배양과 다세포 공배양 플랫폼으로의 활용 가능성을 제시하였다. 두번째 방법은 마이크로 레일 형태의 마이크로구조물을 표준화된 마이크로 플레이트의 웰과 통합하여 고효율 삼차원 배양 플랫폼을 제시한다. 레일 구조의 아래에 주입된 액체가 빨아들여질 때 구조물에 의해 형성된 액체-기체 계면들의 순차적 이동을 활용하여 특정 레일의 아래에만 액체를 남기는 기술을 개발하였다. 이 두가지 모세관 현상 기반 패터닝 방법을 위한 장치들은 사출성형으로 대량생산이 가능하고 우수한 실험 효율을 갖는다. 이 중 레일 구조를 활용한 흡인 기반의 패터닝 방법을 이용하여 면역세포치료제의 성능 평가를 위한 사출 성형된 플라스틱 어레이 배양 장치 (CACI-IMPACT)를 개발하였다. 흡인 기반 패터닝 덕분에 20 μl 파이펫으로 빨아들인 하이드로젤 용액을 30 초 이내에 12개의 웰에 패터닝 할 수 있었다. 면역세포치료제의 기능적 평가를 위해, 콜라겐 젤에 포함된 HeLa 세포를 패터닝하고 NK-92 세포의 콜라겐 매트릭스 내부로의 침투, 매트릭스 내부에서의 이동 및 암세포 살해 활동을 관찰하였다. 이를 통해 세포외기질이 세포 독성 림프구의 이동 및 세포 독성에 상당히 영향을 미친다는 것을 확인할 수 있었다. 따라서, 암세포와 세포 독성 림프구의 고효율 삼차원 공동 배양을 가능하게 하는 본 플랫폼은 고형 종양에 대한 면역 치료를 위해 개발된 세포 독성 림프구의 전임상 평가에 사용될 가능성이 있으며, 본 연구에서 개발 및 사용된 모세관 현상 기반 패터닝 기술들은 장기모사칩의 상용화를 가속화시킬 것으로 기대한다.Chapter 1. Introduction 1 1.1. History of organs-on-chips 1 1.2. Challenges in current organs-on-chips 4 1.3. Models for cancer immunotherapy 7 1.4. Purpose of research 8 Chapter 2. Microstructure-guided multi-scale liquid patterning on open surface 11 2.1. Introduction 11 2.2. Materials and Methods 13 2.2.1. Fabrication of the microstructured PS surface 13 2.2.2. Single cell isolation and retrieval of single colony 16 2.2.3. In vitro vasculogenesis 17 2.2.4. Visualization of the in vitro blood vessel 19 2.3. Results and discussion 18 2.3.1. Liquid patterning process 18 2.3.2. Comparison of microliquid trapping with a micropillar array and microwells 30 2.3.3. Arrangement of micropillars for controlling the volume and shape of patterned liquids 33 2.3.4. Single cell culture & recovery platform 37 2.3.5. Sequential patterning for co-culture in a 3D microenvironment 42 2.4. Conclusions 46 Chapter 3. Aspiration-mediated microliquid patterning using rail-based open microfluidics 47 3.1. Introduction 47 3.2 Materials and Methods 50 3.2.1. Fabrication of open microfluidic devices 50 3.2.2. Cell culture 50 3.2.3. Hydrogel micropatterning 51 3.2.4. Image analysis 52 3.3. Results 53 3.3.1. Microstructures for aspiration-mediated patterning 53 3.3.2. Theoretical analysis of microchannel formation 56 3.3.3. Formation of multiple discrete microchannels 63 3.3.4. An application for screening vasculogenic capacities 70 3.4. Conclusions 75 Chapter 4. High-throughput microfluidic 3D cytotoxicity assay for cancer immunotherapy 77 4.1. Introduction 77 4.2. Materials and Methods 81 4.2.1. Cell culture 81 4.2.2. Fluorescent labeling of live and dead cells 81 4.2.3. 3D cytotoxicity assay using gel patterned device 82 4.2.4. Image analysis 83 4.2.5. 2D cytotoxicity assay 84 4.3. Results 84 4.3.1. Design and fabrication of devices 84 4.3.2. Cytotoxicity assay in 3D ECM environment 89 4.3.3. 3D ECM reduce cytotoxicity 94 4.3.4. Dense ECM impede migration of CLs 98 4.4. Conclusions 104 Chapter 5. Concluding Remarks 110 Bibliography 113 Abstract in Korean 124Docto

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

Organotypische Schnittkulturen aus Glioblastomgewebe als präklinisches Testsystem

Glioblastoma multiforme (GBM) ist der häufigste bösartige Hirntumor bei Erwachsenen. Unbehandelt liegt das mediane Überleben bei circa drei Monaten. Mithilfe maximal möglicher Resektion des Tumors und anschließender aggressiver kombinierter Radiochemotherapie, bestehend aus Bestrahlung und dem Zytostatikum Temozolomid, wird das mediane Überleben auf circa 15 Monate nach Diagnosestellung angehoben. Trotz intensiver Forschung ist über die Entstehung des GBMs wenig bekannt, der einzige bisher bestätigte prädisponierende Faktor ist eine Bestrahlung des Kopfes, insbesondere im Kindes- und Jugendalter. Ein charakteristisches Merkmal des GBMs ist seine große Heterogenität sowohl innerhalb des Tumors eines Patienten als auch zwischen den Tumoren verschiedener Patienten. Dadurch werden die erfolgreiche Behandlung und eine mögliche Heilung erschwert, da sich bis heute nicht zuverlässig vorhersagen lässt, wie gut ein Patient von der Standardtherapie profitieren wird. Das infiltrative Wachstum von GBMs entlang von Nervenbahnen in der gesunden weißen Substanz oder mithilfe der Blutgefäße macht es nahezu unmöglich, die gesamte Tumormasse chirurgisch zu entfernen, was eine hohe Rezidivrate zur Folge hat. Ein größeres Verständnis für die Entstehungsmechanismen des GBMs und seiner Therapieresistenzen ist essenziell für die Entwicklung besserer Therapiemöglichkeiten und verlangt dringend nach geeigneten Modellen für deren Erforschung. In der Krebsforschung bedient man sich häufig an Zellkultur- oder Tiermodellen. Zellkulturen bieten den Vorteil, dass sie preisgünstig in der Unterhaltung sind und sich in relativ kurzer Zeit große Datenmengen durch einen hohen experimentellen Durchsatz erzielen lassen. Nachteilig ist, dass jeglicher Gewebeverband fehlt und das Modell daher nicht die reale Situation in einem ganzheitlichen Organismus widerspiegelt. Im Tiermodell ist der Organismus mitsamt verschiedenen Zelltypen, extrazellulärer Matrix und Blutkreislauf gegeben, jedoch gibt es mitunter gravierende Interspeziesunterschiede, die eine erfolgreiche klinische Translation der Ergebnisse aus Tierversuchen in das humane System erschweren. Patient-derived xenografts, also Transplantate aus Patientengewebe, machen sich den Organismus des Versuchstieres zunutze, erhalten aber dabei auch die Charakteristik des ursprünglichen Tumors weitgehend. Um eine Abstoßung des transplantierten Tumorgewebes zu verhindern, werden zumeist immundefiziente Tiere verwendet, bei denen die immunologische Komponente fehlt, was das Modell artifizieller macht. Zudem ist das erzeugte Tierleid ein nicht zu unterschätzender Faktor, denn Überlebenszeitanalysen mit dem Tod des Versuchstieres als Endpunkt, spielen eine wesentliche Rolle in der onkologischen Forschung. Um das Tierleid in wissenschaftlichen Experimenten zu verringern, wurde 1959 erstmals das 3R-Prinzip (Reduction, Replacement, Refinement) definiert, wonach Tierversuche möglichst komplett ersetzt, Tierzahlen reduziert und die Bedingungen für Versuchstiere verbessert werden sollen. Diesem Prinzip folgend wurden im Institut für Anatomie der Universität Leipzig die organotypischen Schnittkulturen aus Patientengewebe als Alternative zum Tierversuch etabliert. Hierbei wird operativ entnommenes Tumorgewebe von Patienten mithilfe eines Tissue Choppers in 350 µm dünne Scheiben geschnitten und auf Membranen an einer Luft-Medium-Grenze kultiviert. Gewebe aus humanem GBM kann auf diese Weise bis zu zwei Wochen vital erhalten und für Versuche verwendet werden. In der hier vorliegenden Promotionsarbeit wurden Schnittkulturen aus GBM-Gewebe von 25 Patienten angelegt und der Standardbehandlung aus Temozolomid und Bestrahlung unterzogen. Anschließend wurde das Gewebe histologisch aufgearbeitet, um einerseits die Qualität des Gewebeerhalts mittels klassischer Färbungen mit Hämatoxylin und Eosin beurteilen und um andererseits Marker für Proliferation (Ki67) und Apoptose (TUNEL-Assay) anfärben und quantifizieren zu können. In der Vergangenheit beschränkte sich die Auswertung solcher Färbungen vorrangig auf die manuelle Quantifizierung, was zeitintensiv und abhängig von der durchführenden Person zu abweichenden Ergebnissen führt. Im Rahmen dieser Arbeit gelang die automatisierte quantitative Auswertung histologischer Färbungen von kultivierten Gewebeschnitten und deren Veröffentlichung. Durch die Automatisierung kann die Analyse deutlich schneller erfolgen, ist objektiver und damit auch geeigneter für eine klinische Anwendung. Zusätzlich zur histologischen Aufarbeitung des Gewebes wurde aus den Schnittkulturen RNA extrahiert, um Behandlungseffekte auf Expressionsebene untersuchen zu können. Für einen Patienten gelang der Vergleich zwischen Tumorgewebe und angrenzendem Tumorzugangsgewebe, da von beiden Gewebetypen Schnittkulturen angelegt und die Behandlung durchgeführt werden konnte. Mit einer Sequenziertiefe von bis zu 368 Millionen Reads pro Probe, wurden 1888 Gene identifiziert, die im Vergleich zum angrenzendem Gewebe im Tumorgewebe signifikant herunterreguliert waren. Fast 2400 Gene waren entsprechend hochreguliert. Zwischen behandeltem und unbehandeltem Tumorgewebe gab es über 3400 Transkripte, die signifikant unterschiedlich exprimiert wurden. Die Signalweganalyse mit der IPA Software (Qiagen) ergab eine reduzierte Proliferation in behandeltem GBM-Gewebe, was sich mit den Befunden aus der Quantifizierung der Ki67-Färbung deckte. Eine Subgruppenanalyse ergab, dass Gewebekulturen von langzeitüberlebenden Patienten (Gesamtüberleben > 24 Monate) besser auf die Behandlung anzusprechen scheinen, was sich in einer signifikant erhöhten Apoptoserate im Vergleich zu Patienten mit kurzem Überleben zeigte. Schnittkulturen aus Patienten mit einem progressionsfreien Überleben (PFS) von mehr als 7 oder 12 Monaten zeigten eine signifikant höhere Proliferation als Patienten mit einem PFS von unter 7 Monaten. Begründbar ist das mit einer höheren Suszeptibilität von proliferierendem Gewebe gegenüber Schäden durch Bestrahlung und Zytostatika. Die Expressionsanalyse aller 25 Patientenproben ergab eine Hochregulierung von 58 proteinkodierenden Genen. 32 Gene waren im Vergleich zu den unbehandelten Kontrollen im behandelten Gewebe herunterreguliert. Durch die funktionelle Analyse dieser differentiell exprimierten Gene konnte gezeigt werden, dass der p53-Signalweg, die Zellzykluskontrolle, sowie mit DNA-Schäden und deren Reparatur assoziierte Gene und Signalwege nach der Behandlung vermehrt aktiviert sind. Insgesamt zeigen die Ergebnisse der vorliegenden Arbeit, dass Schnittkulturen aus GBM-Gewebe nicht nur histologisch aufgearbeitet werden können, sondern dass es zudem möglich ist, weitreichende molekulare Untersuchungen und Genexpressionsanalysen erfolgreich durchzuführen. Weiterhin sieht man eine gute Korrelation der aus den Kulturen gewonnenen Ergebnisse mit dem klinischen Verlauf der jeweiligen Patienten, was den Rückschluss zulässt, dass die Schnittkulturen ein gutes Abbild der tatsächlichen Situation im Patienten darstellen. Damit wird die Nutzbarkeit des Modells als Alternative zum Tierversuch weiter erhöht und klinisch interessant. Die Robustheit der Methode zeigt sich dadurch, dass RNA-Analysen aus den 25 Patienten umgesetzt werden konnten, obwohl es zum Teil gravierende Unterschiede in der Qualität des kultivierten Gewebes gab. Die inter- und intratumorale Heterogenität des GBMs stellt eine große Herausforderung dar, die mit der Verwendung von biologischen und technischen Replikaten adressiert wurden. Die Korrelationsanalyse der einzelnen Replikate zeigte, dass zumindest die intratumorale Heterogenität weitgehend ausgeglichen werden konnte. Die Heterogenität zwischen den einzelnen Patienten blieb jedoch erhalten und erschwerte allgemeine Aussagen und generelle Rückschlüsse. Auch im GBM besteht daher der dringende Bedarf an individualisierten und auf den einzelnen Patienten ausgerichteten Therapieansätzen. Hierfür bedarf es zukünftig weiterer Forschung an potenziellen Biomarkern mit größeren Patientenkohorten. Gewebekulturen können hierfür sowohl für die Untersuchung von Patientengewebe als auch für die Testung neuartiger Therapieansätze eine Rolle spielen.:Einleitung 3 Glioblastoma multiforme 3 Standardtherapie und MGMT 4 Immuntherapie 5 Heterogenität im GBM 6 Individualisierte Therapie 7 RNA-Sequenzierung 8 Modelle in der Krebsforschung 10 Schnittkulturen aus Patientengewebe 11 Zielstellung der Arbeit 13 Publikation I 14 Publikation II 33 Zusammenfassung 63 Referenzen 66 Darstellung des eigenen Beitrags 72 Erklärung über die eigenständige Abfassung der Arbeit 76 Lebenslauf 77 Publikationen 78 Vorträge 78 Danksagung 7