ESTRO 2022 - Abstract Book

S1562

Abstract book

ESTRO 2022

Conclusion We have demonstrated a powerful framework to rigorously model localised, in-vivo radiation response effects in longitudinal datasets, while accounting for anatomical changes over time. We demonstrated the ability to model imaging changes and reproduce the expected result. We also saw large variations between patients, indicating a need for accurate or composite, multimodal imaging biomarkers which the framework is capable of handling.

PO-1758 Performance assessment of radiogenomics machine learning models for stratifying prostate cancer risk

N. Payan 1 , R.G. Murphy 1 , S. Jain 2,1 , A.R. Hounsell 3,1 , S. Osman 1,3 , J.M. O'Sullivan 2,1 , K.M. Prise 1 , C.K. McGarry 3,1

1 Queen's University, Patrick G. Johnston Centre for Cancer Research , Belfast, United Kingdom; 2 Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Department of Clinical Oncology, Belfast, United Kingdom; 3 Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Department of Radiotherapy Physics, Belfast, United Kingdom Purpose or Objective We have previously established the potential of planning CT-based (pCT) radiomic models for prostate cancer (PCa) risk stratification [1]. In this study, we investigated the additional value of combining pCT-based radiomic with genomic data for PCa risk group (RG) classification and Gleason Score (GS). Materials and Methods We included 184 patients with prostate cancer in this study. We extracted 5983 radiomics features from pCT images, and combined them with 19453 gene expression features from microarray. Following our previously published methodology [1], GS and RG classifications were performed using logistic regression. Models were created using radiomic (R) and genomic