- Author
-
T. Konert
- Title
- Towards worldwide use of FDG PET/CT applications for optimal treatment of lung cancer
- Supervisors
-
J.-J. Sonke
- Co-supervisors
-
W.V. Vogel
M.A.J. Stokkel - Award date
- 13 November 2020
- Number of pages
- 196
- ISBN
- 9789493184718
- Document type
- PhD thesis
- Faculty
- Faculty of Medicine (AMC-UvA)
- Abstract
-
In low- and middle-income countries, management of patients with locally advanced non-small cell lung cancer (NSCLC) can be improved. Centers that are suitably equipped to provide nuclear medicine and radiation oncology services have minimal experience in the multidisciplinary use of a hybrid imaging technique called PET/CT for concurrent chemoradiotherapy, resulting in many patients receiving suboptimal therapy selection and treatment delivery. The work in this thesis focused on improved patient selection for concurrent chemoradiotherapy, and increased and standardized treatment accuracy using PET/CT applications, with the goal to improve survival in patients with NSCLC. Our work, which was in collaboration with the International Atomic Energy Agency, contributed to broader multidisciplinary use of PET/CT in 9 centers from Brazil, Estonia, India, Jordan, Pakistan, Turkey, and Vietnam. We developed pragmatic standardized guidelines to accurately delineate tumor, practical and online training sessions on PET/CT based concurrent chemoradiotherapy over a year, and provided evidence on the effectiveness of PET/CT based concurrent chemoradiotherapy through a multi-center trial. Currently, the choice of treatment is largely dependent on the disease stage and patient condition, and this is not always accurate enough for optimal treatment selection in locally advanced NSCLC. This thesis also contributed to the debate of improving therapy selection in locally advanced NSCLC patients by studying the prognostic accuracy of quantitative imaging features from PET, called PET radiomics features. Future studies should investigate if the prognostic accuracy of current prognostic models can be further improved by combining imaging features from multiple imaging modalities with clinical and genomic data.
- Persistent Identifier
- https://hdl.handle.net/11245.1/f716a6eb-ddd1-40a2-8410-22ae1388e02e
- Downloads
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