Institute of Clinical Artificial Intelligence

Else Kröner Fresenius Center for Digital Health, TU Dresden

E-Mail: JanNiklas.Clusmann@ukdd.de

Project title: Machine learning for risk stratification of liver cancer in real-world data

Hepatocellular carcinoma is a deadly malignancy, often diagnosed at late stages. Current screening is limited to patients with liver cirrhosis, despite a variety of risk factors being characterized. This poses a challenge in risk stratification. The goal of my project is to train machine learning models on real-world data from large-scale biobanks such as the UK Biobank (n=500.000), integrating physical, serum, genomic, and metabolomic data. Models will be built for several, clinically relevant scenarios, comparing added information per data modality. Once the external validation on several other biobanks is performed, the goal is to bring AI-based risk prediction into clinical studies.

Department of Abdominal, Thoracic and Vascular Surgery

University Hospital Dresden, Dresden, Germany

E-Mail: Hannah_sophie.muti@tu-dresden.de
 

Project title: Developing diagnostic and prognostic biomarkers for gastrointestinal cancers with AI-assisted image analysis

Artificial intelligence (AI) can process large amounts of data in a very short time, making it suitable for analyzing complex relationships beyond human capabilities. In precision oncology, this can be utilized to derive biological and prognostic information from oncological imaging data. Gastrointestinal tumors are of particular interest due to their high prevalence and biological heterogeneity. Colon and gastric adenocarcinomas in particular have a high incidence worldwide. Some patients exhibit differences in long-term survival despite similar initial disease stages, which is not tackled in clinical routine. AI has the potential to enhance existing strategies for prognostic stratification of patients with gastrointestinal tumors. In our project, we aim to identify prognostic information in clinical imaging data of colon and gastric adenocarcinoma patients across different time points and disease stages. 

Department of Visceral, Thoracic and Vascular Surgery

Carl Gustav Carus University Hospital Dresden

E-Mail: Carla.portulano@ukdd.de

Project title: Modelling gastric cancer using stomach-specific inducible mouse models and patient 
derived organoids. 

Gastric cancer (GC) is the third leading cause of cancer deaths. Due to missing early clinical signs, GC is diagnosed at late stage cancer with distant metastases, resulting in incurable disease. Therefore, I aim to shed light on the pathological mechanisms of GC metastasis. Additionally, I aim to investigate the tumorigenesis of gastric adenosquamous cancer (ASC) which is characterized by aggressive tumor progression. Since the pathogenesis of ASC is unknown, it is difficult to define treatment strategies.
Therefore, I plan to identify the mutational pattern and transcriptomic profile of ASC. For both projects, I will use stomach-specific mouse models and patient derived organoids.

Department of Hematology, Cellular Therapy and Medical Oncology

University Hospital Carl Gustav Carus Dresden, Dresden

E-Mail: julien.subburayalu@ukdd.de

Project title: Characterization of the antitumoral potential of human genome-modified macrophages

Cancer immunotherapy based on macrophages are hindered by a postmitotic phenotype and a proclivity for repolarization by tumor cells. Chimeric antigen receptor (CAR)-bearing macrophages have recently been shown to effectively eat away at tumor cells. Our work shows that genetic modification in the absence of specific CARs in macrophages maintains antitumoral effects which enables applicability in tumors that have no identifiable CAR target.
Our macrophages are denoted by a continuous expansion of terminally differentiated macrophages in vitro, thus potentially enabling cellular therapy. We are committed to delivering a cell therapy product able to outsmart solid tumors even in instances where conventional therapies have failed.

Else Kroener Fresenius Center for Digital Health, 
Technical University Dresden, Dresden, Germany
and Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

E-Mail: Isabella.wiest@tu-dresden.de

Project title: Beyond Traditional Methods: Leveraging Large Language Models to Unveil Hidden Gems in Oncology Data

The field of oncology heavily relies on the transfer of information between healthcare providers and researchers, a process often hampered by the unstructured nature of medical data. Key insights remain buried within free-text formats, eluding traditional quantitative analysis. Large Language Models (LLMs) present a solution to these longstanding challenges, surpassing classical natural language processing (NLP) techniques in both scope and depth. This project aims to employ LLMs to systematically decode and organize vast amounts of text data, which, due to time-consuming processing requirements, have been underutilized at scale for broader informational gains in oncology. The goal is to enhance oncological data management, facilitating a more scalable and efficient approach for information extraction that supports improvements in diagnosis and treatment strategies.

Department of Neurosurgery

University Hospital Carl Gustav Carus Dresden, Dresden, Germany

E-Mail: andrenorbertjosef.sagerer@ukdd.de

Project title: Characterization of the tumor microenvironment of brain metastases

Metastases are the most common tumors of the central nervous system. Malignant melanoma and bronchial carcinoma show the highest prevalence of cerebral metastases. With the advancement of targeted and immunotherapy, better treatment outcomes can now be achieved. Unfortunately, patients receiving such therapies often develop resistance and experience recurrence or progress. Current data suggest that the tumor-specific microenvironment has a significant influence on the development of resistance. The aim of the present project is to immunologically and molecularly characterize the tumor microenvironment of brain metastases.

Department of Positron Emission Tomography, Institute of Radiopharmaceutical Cancer Research, Helmholtz Zentrum Dresden Rossendorf & Research Group Modeling and Biostatistics in Radiation Oncology, OncoRay – National Center for Radiation Research in Oncology

Dresden, Germany

E-Mail: m.vacha@hzdr.de

Project title: Harmonizing PET-based Image Analysis for Improved Outcome Prediction in Radiation Oncology

Since positron emission tomography (PET) imaging plays a crucial role in cancer diagnosis and contains information about tumor metabolism, there have been efforts to identify PET-based image biomarkers for treatment personalization and survival prediction. While conventional biomarkers, such as SUVmax, seem to be generalizable, the more complex, radiomic biomarkers often fail in validation. That may be caused by a lack of image harmonization. My aim is to compare the conventional and radiomics approaches within a single study and evaluate whether novel harmonization techniques can improve their reliability. This may lead to workflow optimization and development of new, robust biomarkers.

Department of Nuclear Medicine

University Hospital Carl Gustav Carus at the Technische Universität Dresden

E-Mail: AndreaCarolina.LunaMass@ukdd.de





Project title: Multi-Parameter Approach to Tailored Radioligand Therapy: Optimizing mCRPC Treatment Through Tumor Metrics and Patient Biology

Our study aims to implement algorithmic personalization of radioligand therapy in mCRPC by integrating liquid biopsy ctDNA analysis with conventional biomarkers for dose selection and response evaluation. By correlating circulating tumor DNA dynamics, PSMA expression, and hematological parameters with clinical outcomes, we seek to establish a precision medicine framework for radioligand therapy optimization. This translational approach represents a paradigm shift from standardized dosing toward patient-specific radiopharmaceutical administration, potentially revolutionizing treatment protocols while establishing validated predictive models for therapeutic efficacy.

Department of Visceral, Thoracic and Vascular Surgery

University Hospital Carl Gustav Carus Dresden

E-Mail: rayan.younis@ukdd.de




Project title: Multimodal skill transfer between humans and robots in minimally invasive surgery

In our rapidly changing society, growing surgical staff shortages pose a serious threat to surgical patient care worldwide. Less surgeons face pressure to perform surgery on more patients with decreasing training time. My project aims to digitalize surgical skills with novel sensors in order to improve learning for surgeons and for robots. To achieve this, I acquire multimodal surgical sensor data and use AI to better understand how surgical gestures are performed. With that understanding, I co-develop and validate new assistance systems. My vision is to achieve immersive mentoring that accelerates training and to transfer skills to autonomous robots that alleviate surgical staff shortages.