On January 23rd 2024 at 16.30, Ine Dirks will defend their PhD entitled “COMPUTER-AIDED DIAGNOSIS AND DECISION SUPPORT USING MEDICAL IMAGE ANALYSIS – CONTRIBUTIONS TO MALIGNANT MELANOMA AND COVID-19”.
Everybody is invited to attend the presentation at the Room I.0.02, or digitally via this link.
In medicine, the high volume of available data and the expanded number of treatment options have rendered it increasingly complex to determine the appropriate therapy for a specific patient. Precision medicine is a promising and emerging approach to tailor disease prevention and treatment by considering individual patient characteristics. Computer-aided diagnosis (CAD) systems can support physicians by performing fast, objective and reproducible medical image analyses and by extracting parameters that allow for more personalised disease assessment and response prediction. These features can then be used in a clinical decision support (CDS) system to guide therapeutic decisions. In this work, we investigate CAD and CDS methods for two pathologies: malignant melanoma and COVID-19.
Malignant melanoma is the most lethal form of skin cancer. Treatment planning and monitoring are generally performed using combined positron emission tomography/computed tomography (PET/CT) with fluorine-18 fluorodeoxyglucose ([18F]FDG) and regular testing of blood values. Recently, survival chances have increased due to advances in immunotherapy and targeted therapies. Nonetheless, a considerable part of this population demonstrates progressive disease. If patients with a poor prognosis can be identified before the start of therapy, a more aggressive treatment pathway could be considered to improve the survival chances.
A fully automated system was developed for lesion detection and segmentation on whole-body [18F]FDG PET/CT to extract information on the tumour load from the imaging data. We further demonstrated the feasibility of using these automatically derived imaging features in survival analysis through a comparative study with the manual method. The automated approach led to very similar results and could therefore enable the use of these parameters in clinical routine and future clinical trials.
A second pathology investigated is COVID-19, which presented great challenges for the medical sector worldwide. During the pandemic, intensive care units were overwhelmed and proper resource allocation became problematic. During the periods of high prevalence, there was an urgent need for computer-aided systems to support decisions in diagnosis, treatment and resource allocation.
In a large research collaboration, automated tools were developed to alleviate the situation. The resulting methods allow to segment lung lesions and extract relevant parameters. In addition, a model was developed to predict disease severity at one month. Its performance was validated in the context of an international challenge and proved robust through evaluation on different, multicentre datasets.
Our work demonstrated the potential of CAD and CDS systems in the field but also revealed pitfalls and shortcomings. Several challenges remain before such systems can be used readily in clinical routine, including thorough validation and medical certification. Still, important contributions were made to help in the shift towards precision medicine.