Projekte

Project descpription:

Endometriosis affects approximately 10% of fertile women, causing significant pain and infertility, yet non-invasive diagnostic tools remain inadequate. […] This project aims to address these challenges by developing a novel diagnostic approach using Positron Emission Tomography (PET). PET offers higher resolution and sensitivity for detecting and characterizing tissues. Recent research has shown that endometriotic lesions are rich in relaxed fibronectin (Fn), particularly in the stroma and areas of reactive fibrosis. Preliminary studies have successfully stained Fn in endometriotic tissues, suggesting it could serve as a promising target for PET imaging. The project proposes a detailed analysis of several extracellular matrix proteins, including relaxed fibronectin, fibroblast activation protein (FAP), matrix metalloproteinases, and integrins, using immunohistochemistry. These markers will be correlated with MRI and ultrasound findings, as well as intraoperative observations and histopathology results. The goal is to identify an optimal target for PET imaging that can differentiate active, painful endometriotic tissue from scar tissue, thereby improving preoperative lesion localization and surgical planning. This approach could potentially allow for earlier, more accurate diagnosis, better patient management, and improved pain relief after surgery.

About Bastian Schultz:

Bastian Schulz, 31, has been working as an assistant physician at the Institute of Legal Medicine in Zurich since 2020. In 2023, he completed a research year in radiology at Kantonsspital Baden, during which he published three papers and one case report as first author and contributed to a industrial whitepaper. Since June 2024, he has been in a rotational year in pathology at Kantonsspital Baden. Alongside his work in pathology, he is involved in the Endometriosis Project, a multidisciplinary collaboration between the departments of radiology, nuclear medicine, and gynecology at Kantonsspital Baden, in partnership with PSI and ETH Zürich.

Partner Hospital: Kantonsspital Baden
Research Partner: Paul Scherrer Institut PSI & ETH Zürich

Project descpription:

Bronchiectasis is a chronic lung condition where the airways become abnormally widened, often due to previous lung infections, immune system disorders, or genetic conditions. This widening leads to persistent coughing, excess mucus production, and frequent respiratory infections like pneumonia. In addition to pneumonia, patients with bronchiectasis are prone to recurrent exacerbations, where symptoms suddenly worsen, increasing the risk of lung damage and hospitalization. Identifying patients at high risk for pneumonia and exacerbations is crucial for timely intervention and better outcomes. Despite advances in medical imaging, current methods do not accurately predict which patients are most likely to develop pneumonia or experience exacerbations. This project aims to address this gap by developing artificial intelligence (AI) tools to predict these risks in bronchiectasis patients. By analyzing clinical data—including patient histories, comorbidities, and microbiological profiles—alongside detailed chest CT scans, the AI will identify patterns that indicate a higher likelihood of complications. […] As Switzerland prepares for a national lung cancer screening program, which will increase the number of chest CT scans, the AI tools developed in this project will help radiologists and clinicians manage the growing demand while focusing on the most vulnerable patients. […]

About Tician Schnitzler:

Dr. Tician Schnitzler is a radiology resident and postdoctoral researcher at Kantonsspital Aarau, specializing in thoracic imaging and artificial intelligence (AI) applications in medical imaging. His expertise has been shaped by a research fellowship at the University of California, San Francisco (UCSF), where he focused on integrating AI into clinical radiology. He holds an MD from RWTH Aachen University, Germany, and is currently pursuing a Master’s in Biomedical Informatics and Data Science at the University of Mannheim to further enhance his skills in data-driven medical research.

Dr. Schnitzler will lead the project "Predicting Recurrent Pneumonias and Exacerbations in Bronchiectasis: Clinical and Imaging Phenotypes for Risk Stratification and Algorithm-Assisted Management." In this role, he applies his specialized skills to develop predictive models that integrate clinical data with chest CT imaging, aiming to identify bronchiectasis patients at high risk for pneumonia and exacerbations. This project represents a unique collaboration between Kantonsspital Aarau and the Paul Scherrer Institute (PSI) with the goal to advance AI-driven tools that enhance patient management—particularly in the context of Switzerland’s upcoming national lung cancer screening program.
 
Partner Hospital: Kantonsspital Aarau 
Research Partner: Paul Scherrer Institut PSI

Project descpription: 

ProSPECT-Q aims to establish a more objective approach to tracking radioligand therapy (RLT) in prostate cancer patients treated with Lutetium-177 (Lu-PSMA), an innovative therapy that targets prostate cancer cells. Lu-PSMA has already shown strong results in extending the survival of patients with advanced prostate cancer, typically as a last-resort option after chemotherapy. However, as the tendency is to introduce Lu-PSMA earlier in the treatment course, a more precise method to assess which patients will benefit from the therapy and to monitor the therapy’s effects over time are required. Currently, assessments rely mainly on visual evaluations and laboratory results, such as Prostate Specific Antigen (PSA) levels, which may miss subtle changes in tumoural activity. This project proposes the use of quantitative SPECT/CT imaging, which offers more accurate measurement of tumour volume and response than traditional visual assessments. However, interpreting this data in a clinical context is complex, so ProSPECT-Q seeks to develop new tools for analysing imaging data in combination with clinical information, using artificial intelligence. The project will collect and analyse data from past patients, measuring tumour characteristics, therapy outcomes and side effects. Advanced techniques like machine learning will be used to identify patterns and predict outcomes such as therapy success, defined as therapy completion or serious adverse effects, like bone marrow damage or decrees of renal function. By creating models that predict these risks early, doctors could better balance the risks and benefices of the treatment, helping patients avoid unnecessary side effects and improving their long-term outcomes. […]

About Sabin-George Pop:

Sabin-George Pop is currently a resident in nuclear medicine at the Kantonsspital Baden (KSB). During his medical studies, he developed a keen interest in the application of machine learning solutions to clinical challenges, and was further immersed in the subject through the KTH Institute's HelloAI programme. All this deepened his passion for AI applications in medical imaging and materialised in his thesis on "Automatic Assessment of Bone Age using Artificial Intelligence" and further in the current project ProSPECT-Q, which aims to combine nuclear medicine expertise with the power of machine learning to improve the risk-benefit assessment and enable more personalised treatment for patients suffering from prostate cancer.

Partner Hospital: Kantonsspital Baden
Research Partner: Paul Scherrer Institut PSI

Project descpription:

While not all patients are the same, treatment also differs. Such differences in care can be explained by patient factors but might also be the result of incognizant biases in the care teams’ minds. Especially when it comes to interventions in intensive care units (ICU), this may lead to disparities. So called causal inference frameworks offer a way to examine the fair use of treatments. We wanted to investigate the use of invasive mechanical ventilation, renal replacement therapy and vasoactive medications to identify disparities in outcomes between cancer patients with sepsis compared to non-cancer patients with sepsis. For this, we want to use a novel and powerful statistical method called targeted maximum likelihood estimation which is able to simulate a what-if world in which each patient will be treated and then compared to this digital twin who is not being treated. [… ] Detecting differences in treatment outcomes would help physicians to rethink their treatment policies and inform policy makers to place institutional checks. If there were no differences, our proposed framework could be deployed to other datasets, questions, or disparity checks at other institutions.

About Tristan Struja:

Tristan Struja, the son of a Swiss mother and Croatian father, is an endocrinologist and senior physician at the Kantonsspital Aarau (KSA), with a strong background in clinical research and public health. Born in 1984, he grew up in Aargau and completed his studies in medicine at the University of Zurich, where he also completed a dissertation under the supervision of Prof. Sabine Rohrmann. After graduating in 2013, he worked as an assistant and later senior physician at the Medical University Hospital of the KSA, alongside continuing research.

In 2016, he had a 15-month interlude at the Triemli City Hospital in Zurich as a senior physician in the Clinic for Internal Medicine. He returned to the KSA in 2018, where he advanced his training as an endocrinologist and researcher. During this time, he completed a joint Master's in Clinical Research at the TU Dresden and Harvard School of Public Health. In 2022, he spent time at the Massachusetts Institute of Technology (MIT) in Boston, conducting research at the Laboratory for Computational Physiology under Prof. Leo Celi. There, he worked on large-scale databases from intensive care units across the US, with a focus on causal inference. He also completed a Master of Public Health at Harvard during this period.

Tristan Struja will lead the project Causal Inference to Detect Disparities in Cancer Patients – The CID Project.

Partner Hospital: Kantonsspital Aarau 
Research Partner: ETH Zürich