Christian Doppler Laboratory for Image and knowledge dRivEN prEcision radiation oncology
The objective of the Christian Doppler Laboratory (CDL) for Image and knowledge dRivEN prEcision radiation oncology (IRENE)", funded by the Christian Doppler Society, is to improve cancer care by increasing the precision of radiation oncology at two levels.
Enhancing the geometric precision (precision I ) will tackle beam delivery accuracy aspects due to inter- and intra-fractional movements, while maximally sparing the surrounding healthy tissue. Software and workow procedures for real-time tumour tracking and position prediction will be validated in phantom studies with subsequent clinical implementation. The final aim is tumour position tracking and real-time treatment adaptation based on in-room imaging. For automated quality assurance and improved treatment parameter correlation, treatment-record and imaging based dose reconstruction will be streamlined for clinical implementation.
Adapting radiation oncology treatments according to the patients' individual tumour characteristics (precision II) via biological factors and tumour response are another pillar of the CDL. More specifically tumour response will be assessed by repetitive morphological and functional magnetic resonance imaging during radiotherapy. Quantitative and qualitative imaging information will be the basis for target volume adaptation and artificial intelligence-based image analysis, which will be investigated within prospective clinical phase II studies.
Data of patients treated with innovative concepts as part of clinical routine at the Department of Radiation Oncology, Medical University of Vienna/General Hospital Vienna and in the above described clinical studies will be collected in a comprehensive data base, which in turn serves as basis for knowledge-based treatment and outcome data evaluation. Big data management will be facilitated by a new format, which enables direct and swift access of all relevant medical data. This will pave the way towards developing and implementing a data pipeline that can be directly connected to artificial intelligence-based tools supporting clinical decisions. In consequence, a closed loop system will be developed, that continuously triggers further improvements by linking realworld data including imaging parameter, dosimetric parameters, outcome data from a physician's perspective as well as patient related outcome measures.
The CDL IRENE aims at the integration of two major pillars in contemporary personalized radiation oncology: Technological improvement (WP1 – Image driven (real-time) treatment adaptation) will be combined with modern radiobiology-driven adaptive treatment concepts (WP2 – MR-driven response assessment and outcome modelling) together with innovative forms of prospective automatised data collection and outcome assessment (WP3 – Real-world data-driven learning in radiation oncology). The overall vision is to create a learning healthcare system, which allows to link, apply and directly learn from each step in the chain of image-guided adaptive radiation oncology.
More information on the Christan Doppler Laboraty can be found on the homepage of the Christian Doppler Society (hyperlink: https://www.cdg.ac.at/forschungseinheiten/labor/bild-und-erkenntnisbasierte-praezisionsstrahlentherapie)