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Multi Modal PET/CT Imaging for Therapy Response Early Prediction and Therapy Monitoring

M Hatt
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P Lambin

P Kinahan

M Hatt1*, P Lambin2*, P Kinahan3*, (1) INSERM, Brest, Finistere, (2) Maastro Clinic, Maastricht, The Netherlands, (3) University of Washington, Seattle, WA

TU-A-141-1 Tuesday 8:00AM - 9:55AM Room: 141

Monitoring a patient's response during treatment plays an important role in optimizing personalized treatment strategy. It may offer the possibility to predict future response early during treatment and adapt therapy. It calls for an optimal use of existing and under development imaging technologies as well as image and data processing and analyses for characterizing tumor response. This lecture will provide an overview of three complementary points related to therapy response monitoring using PET/CT.

The first one concerns the growing interest and potential future role of multi modal PET/CT imaging in response to therapy studies, from a clinical point of view. This will help the audience identifying which combinations of disease, associated treatments and radiotracers are concerned. The second point is associated with the technical issues and methodological requirements associated with implementing response to therapy monitoring in clinical studies, in terms of standardization and homogeneity of acquisition protocols (reproducibility) and optimal use of existing image reconstruction methods. Finally, the third point concerns the way produced images are exploited, processed and manually or semi-automatically analyzed then integrated in multifactorial Decision Support Systems. Current and future trends in the development of advanced methods dedicated to facilitate processing and analysis of multi modal and sequential images PET/CT (and PET/MRI) will be discussed, as well as their potential future role for response to therapy monitoring based on multi modal imaging integrated with non imaging biomarkers of predictors.

Learning objectives
1. Review the current and future role of multi modal PET/CT imaging in early prediction and monitoring of response to therapy in several clinical oncology and radiotherapy applications.
2. Understand the requirements regarding standardization of imaging protocols and image generation in order to exploit multi modal image-derived indices to characterize response to therapy.
3. Envision the future directions in developing semi automated methods for multi modal images analysis and multifactorial decision support systems integrating imaging and non imaging biomarkers.

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