ESTRO 37 Abstract book

S1153

ESTRO 37

absolute values of Dx were 2.66 and 1.69 at D 5

(p =

(p = 0.048), 2.86 and 1.11 at

0.755), 3.22 and 1.67 at D 10

(p = 0.018), 2.00 and 0.77 at D 20

(p = 0.048), 1.32 and

D 15

(p = 0.106), 0.96 and 0.75 at D 30

(p = 0.343),

0.71 at D 25

and 0.74 and 0.72 at D 35 (p = 1.000) for ≥2 and <2 previous treatments, respectively, of lesions other than SBRT-treated lesions.

Conclusion Previous treatment significantly influences the parametric discrepancy between DFH and DVH. EP-2094 Ultra high-field MRI for identifying complete response after neoadjuvant therapy for rectal cancer S. Hoendervangers 1 , C.S. Arteaga de Castro 1 , A.M. Couwenberg 1 , M.M. Lacle 2 , W.M.U. Van Grevenstein 3 , M.P.W. Intven 1 , M.E.P. Philippens 1 , H.M. Verkooijen 1 1 UMC Utrecht, Radiotherapy, Utrecht, The Netherlands 2 UMC Utrecht, Pathology, Utrecht, The Netherlands 3 UMC Utrecht, Surgery, Utrecht, The Netherlands Purpose or Objective In patients with complete response following neoadjuvant therapy for rectal cancer, the question arises whether extensive surgery is necessary or whether a wait-and-see approach is justified. However, selection of patients for such an organ sparing approach is challenging, as small residual tumor might be missed on conventional imaging. High-field ( 7 Tesla) MRI, allowing increased resolution and functional imaging, may be valuable in improving identification of complete response after neoadjuvant therapy for rectal cancer. Here we present the first results of protocol optimization of 7 Tesla MRI for rectal cancer. Material and Methods 5 patients with rectal cancer and 5 resected specimens of patients with rectal cancer were scanned on a 7 Tesla MRI (Philips). Possibilities for improving resolution on anatomical MR sequences and the use of functional imaging were explored. We were particularly interested in chemical exchange saturation transfer (CEST), a new endogenous contrast reflecting acidity, which was acquired using 2D gradient echo with 45 offsets (10 ppm Although higher resolutions could be achieved at 7 Tesla, no clear benefit was found when compared to 3 Tesla in terms of tumor contrast. Nevertheless, with 7 Tesla MRI we could investigate endogenous contrasts, which differed between tumorous and non-tumorous tissue. For example, ATP metabolism was measured, showing an increased CEST effect in some areas, possibly caused by active tumor cells. range). Results

Conclusion Metabolic imaging on7 Tesla MRI is a feasible new modality for accurate preoperative detection of small residual tumor tissue following neoadjuvant therapy. It has the potential to improve the selection of patients with rectal cancer who could benefit from omission of surgery. The pathological validation, using whole mount pathology, is still ongoing. Future research will focus on functional modalities of 7 Tesla MRI, such as chemical exchange (CEST), diffusion weighted imaging (DWI) and spectroscopy. EP-2095 Perturbing single images as a surrogate for radiomic feature robustness test-retest experiments A. Zwanenburg 1,2,3,4 , S. Leger 1,4 , E.G.C. Troost 1,2,3,4,5,6 , C. Richter 1,4,6 , S. Löck 1,4,5 1 OncoRay – National Center for Radiation Research in Oncology- Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden- Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Germany 2 National Center for Tumor Diseases NCT, partner site Dresden, Dresden, Germany 3 German Cancer Research Center DKFZ, Heidelberg, Germany 4 German Cancer Consortium DKTK, partner site Dresden, Dresden, Germany 5 Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Department of Radiotherapy and Radiation Oncology, Dresden, Germany 6 Helmholtz-Zentrum Dresden – Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany Purpose or Objective Radiomics is the high-throughput, machine-learning based analysis of medical images for model-based treatment decisions. It relies on image characteristics (features), which quantify aspects of a volume of interest, such as its mean intensity, volume and texture heterogeneity. Features used for modelling should be robust against perturbations, induced e.g. by patient positioning, image acquisition and contouring; otherwise resulting radiomics