Decision support systems for personalized and participative radiation oncology.

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

In vivo optical imaging of MMP2 immuno protein antibody: tumor uptake is associated with MMP2 activity.

Matrix metalloproteinase-2 (MMP2) is important in tumorigenesis, angiogenesis and tumor invasion. In this study, we investigated if the Cy5-tagged small immuno protein targeting the catalytic domain of human MMP2 (aMMP2-SIP) detects MMP2 in tumors non-invasively. For this purpose, we generated MMP2 expressing (empty vector EV) and knock-down (KD) HT1080, U373 and U87 cells, which were injected subcutaneously in the lateral flank of NMRI-nu mice. Optical imaging (Optix MX2) performed at 0.5, 2, 4, 8, 24 and 48 hour post injection (h.p.i.) of Cy5 tagged aMMP2-SIP, indicated significantly lower tumor to background ratios at both 24 (P = 0.0090) and 48 h.p.i. (P < 0.0001) for the U87 MMP2-KD compared to control tumors. No differences were found for HT1080 and U373 models. U87 MMP2-KD tumors had significantly lower MMP2 activity (P < 0.0001) than EV tumors as determined by gelatin zymography in tumor sections and lysates, while no differences were observed between EV and MMP2-KD in HT1080 and U373. In line with these data, only U87 MMP2-KD tumors had a reduced tumor growth compared to control tumors (P = 0.0053). aMMP2-SIP uptake correlates with MMP2 activity and might therefore be a potential non-invasive imaging biomarker for the evaluation of MMP2 activity in tumors.

Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells.

BACKGROUND AND PURPOSE: The central hypothesis of "radiomics" is that imaging features reflect tumor phenotype and genotype. Until now only correlative studies have been performed. The main objective of our study is to determine whether a causal relationship exists between genetic changes and image features. The secondary objective is to assess whether the combination with radiotherapy (RT) influences these image features. MATERIAL AND METHODS: HCT116 doxycycline (dox) inducible GADD34 cells were grown as xenografts in the flanks of NMRI-nu mice. GADD34 overexpression decreases hypoxic fraction. Radiomics analyses were performed on computed tomography images obtained at 40kVp and again at 80kVp for validation, before radiotherapy at a volume of 200mm(3), 4days post RT (10Gy) and 500mm(3). To select reproducible features test-retest experiments were performed at baseline. RESULTS: Gene induction and/or irradiation translated into significant changes in radiomics features. Post irradiation, 17 features for 40kVp and 9 features for 80kVp differed significantly between dox+ and dox- combined with RT. 8 and 4 of these features remained consistent for 40 and 80kVp, respectively. CONCLUSION: Radiomics is able to identify early effects of changed gene expression combined with radiation treatment in tumors with similar volumes which are not visible to human eye.