ESTRO 2021 Abstract Book

S1411

ESTRO 2021

Evaluate the correlations of tumour permeability parameters and apparent diffusion coefficient (ADC) in nasopharyngeal carcinoma (NPC). Materials and Methods Thirteen (13) patients with NPC (N for T1=3, T2=2, T3=4, T4=4) were examined with dynamic contrast- enhanced magnetic resonance imaging (DCE-MRI) and RESOLVE diffusion-weighted imaging (DWI). Tumour permeability parameters were quantitatively measured with Tofts compartment model. Volume transfer constant (K trans ), volume of extravascular extracellular space (EES) per unit volume of tissue (V e ), and the flux rate constant between EES and plasma (K ep ) from DCE-MRI scan were measured. The time-intensity curve (TIC) was plotted from the 60 dynamic phases (temporal resolution 4.7s) of DCE-MRI. Initial area under curve (iAUC60) for the first 60s of the contrast agent arrival was also calculated. They were compared with the ADC value from DWI with Pearson correlation analyses. Results Among the DCE-MRI permeability parameters in NPC tumours, V e is the most correlated parameter with positive correlationship with ADC value (r=0.887, p<0.001). K ep also has significant inverse correlationship with ADC value (r=-0.836, p<0.001). K trans (r=-0.596, p=0.032) and iAUC60 (r=-.0647, p=0.017) are less significant in correlationships with ADC in NPC patients.

Conclusion Nasopharyngeal tumour cellularity measuring by free water diffusability with ADC is significantly correlated to volume of EES per unit volume of tissue (V e ) and inversely correlated to the flux rate constant between EES and plasma (K ep ) measuring from permeability parameters in DCE-MRI scan. PO-1685 Application of radiomics feature captured from MRI for prediction of recurrence for glioma patients C. Liu 1 , Y. Li 2 , J. Wang 2 , C. Hu 2 1 Suzhou Dushu Lake Hospital, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Department of Radiation Oncology, Suzhou, China; 2 Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Department of Radiation Oncology, Shanghai, China Purpose or Objective This study aimed to develop and validate a recurrence prediction of glioma patients through a radiomics feature training and validation model. Materials and Methods In this study the prediction model was developed in a training cohort that consisted of 88 patients from January 2014 to July 2017 with pathologically confirmed gliomas. An independent validation cohort contained 41 consecutive patients from August 2017 to December 2018. Their pre-radiotherapy and recurrence brain magnetic resonance imaging (MRI) images were collected, and the radiomics features were extracted. Clinical