ESTRO 36 Abstract Book

S233 ESTRO 36 _______________________________________________________________________________________________

response can be quantified and predicted using Tumor Metabolic Ratio (TMR) matrix obtained during the early treatment weeks from multiple FDG-PET imaging. Material and Methods FDG-PET/CT images of 15 HN cancer patients obtained pre- and weekly during the treatment were used. TMR was constructed following voxel-by-voxel deformable image registration. TMR of each tumor voxel, v , was a function of the pre-treatment SUV and the delivered dose, d , such as TMR( v , d ) = SUV( v , d )/SUV( v , 0). Utilizing all voxel values of TMR in the controlled tumor group at the last treatment week, a bounding function between the pre- treatment SUV and TMR was formed, and applied in early treatment days for all tumor voxels to model a tumor voxel control probability (TVCP). At the treatment week k , TVCP of each tumor voxel was constructed based on its pre- treatment SUV and TMR obtained at the week k using the maximum likelihood estimation on the Poisson TCP model for all dose levels. The DPF at the week k was created selecting the maximum TVCP at each level of the pre- treatment SUV and TMR measured at the week k . In addition, 150Gy was used as an upper limit for the target dose. Results TVCP estimated in the early treatment week, i.e. week 2, had their D 50 =13~65Gy; g 50 = 0.56~1.6 respectively with respect to TMR = 0.4~1.2; Pre-treatment SUV = 3.5~16. Figure 1 shows the TVCP estimated using the TMR measured at the week 2 with different levels of pre- treatment SUV, as well as TVCP at different weeks, the week 2 ~ week 4. Large dose will be required to achieve the same level of tumor control for the same level of TMR appeared in the later week of treatment. Figure 2 shows the corresponding DPF for the week 3 TMR, as well as the prescribed tumor dose distribution for the 3 failures.

Figure 2

Conclusion DPF can be estimated and constructed adaptively voxel- by-voxel in human tumor using multiple FDG-PET imaging obtained during the treatment course. DPF provides a potential quantitative objective for adaptive DPbN to plan the best clinical dose, escalate or de-escalate, in human tumor based on its own radiosensitivity or radioresistance. OC-0442 Intensity based synthetic CT generation from standard T2-weighted MR images with three MR scanners L. Koivula 1 , L. Wee 2 , J. Dowling 3 , P. Greer 4 , T. Seppälä 1 , J. Korhonen 1 1 Comprehensive Cancer Center- Helsinki University Central Hospital, Department of radiation oncolocy, Helsinki, Finland 2 Danish Colorectal Cancer Center South, Vejle Hospital, Vejle, Denmark 3 Commonwealth Scientific and Industrial Research Organisation CSIRO, CSIRO ICT Centre, Brisbane, Australia 4 Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia Purpose or Objective Recent studies have shown feasibility t o conduct the entire radiotherapy treatment planning workflow relying solely on magnetic resonance imaging (M RI). Yet, few hospitals have implemented the MRI-only workflow into clinical routine. One limiting issue is the requisite construction of a synthetic computed tomography (sCT) image. The majority of published sCT generation methods necessitate inclusion of extra sequences into the simulation imaging protocol. This study aims to develop an intensity-based sCT generation method that relies only on image data from standard T2-weighted sequence. The work includes images derived from three different manufacturers’ MR scanners. The primary target group was prostate, for which T2-weighted images are already used as standard target delineation images. Material and Methods The study utilized a total of 30 standard T2-weighted images acquired for prostate target delineation in three different clinics. The imaging was conducted with MR scanners (GE Optima 1.5T, Philips Ingenia 1.5T, and Siemens Skyra 3.0T) of each participating clinic by using their typical clinical settings. Intensity value variations of the obtained images were studied locally, and compared to corresponding Hounsfield units (HUs) of a standard CT image. The data of 21 of the 30 prostate patients was used to generate conversion models for bony and soft tissues to transform the MR image into sCT. The models were optimized separately for the images obtained by each MR platform. The sCT generation was tested for 9 of the 30

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