ESTRO 37 Abstract book

S1158

ESTRO 37

EP-2102 Correlating MRI radiomics with biology during therapy in cancer models: a tool for drug development? H. Woodruff 1 , F.X. Blé 2 , A. Jochens 3 , R.T.H. Leijenaar 3 , A. Ibrahim 3 , K.M. Brindle 4 , K. Heinzmann 5 , D.J.O. McIntyre 4 , P. Lambin 3 1 MAASTRO Clinic, Radiation Oncology, Maastricht, The Netherlands 2 Imaging Labs- Precision Medicine and Genomics- AstraZeneca, IMED Biotech Unit, Cambridge, United Kingdom 3 The D-Lab: Decision Support for Precision Medicine- GROW - School for Oncology and Developmental Biology- Maastricht Comprehensive Cancer Centre- Maastricht University Medical Centre, Oncology, Maastricht, The Netherlands 4 University of Cambridge, CRUK Cambridge Institute, Cambridge, United Kingdom 5 Imperial College London, Department of Surgery and Cancer, London, United Kingdom Purpose or Objective To investigate whether MRI radiomic signatures extracted from fast spin echo (FSE) images, diffusion weighted (DW) images, and apparent diffusion coefficient (ADC) maps as imaging biomarkers could be used to monitor therapy- dependent changes in tumor biology in preclinical models of pancreatic and lung cancer. Material and Methods A total of 89 mice from two centers (Cancer Research UK (CRUK), AstraZeneca (AZ)) underwent therapy and MR images were acquired at varying time points and doses. After the final imaging session, mice were euthanized and the tumour processed for histological evaluation. CRUK: 45 mice, including genetically engineered mice with spontaneous pancreatic adenocarcinoma (KPC, N=13) driven by mutant KRAS and p53 and mice with subcutaneous allografts of a KPC-derived cell line (K8484, N=32) were treated with vehicle or gemcitabine at 100 mg/kg. AZ: 44 immunodeficient mice with PC9 lung adenocarcinoma xenografts were treated daily with vehicle or gefitinib at 6.25 mg/kg. For both datasets features consisting of first-order statistics, shape, local intensity, texture, fractal dimension, multi-scale filters and textures were extracted from FSE (CRUK only), DW images (AZ only) as well as ADC maps. Radiomics features were correlated with tumor type (CRUK only), drug dosage at varying time points as well as histopathology results (AZ only) for Hematoxylin/Eosin (H&E), pEGFR, cleaved Caspase 3 (CC3), and Ki67 staining. Drug response stratification models were built using random forest (RF) classification. The value of MRI biomarkers was explored by correlating radiomics features with histopathology via multiple linear regression models, including RF and support vector machines. Feature selection and a ten-fold cross validation were performed to evaluate model performance and to avoid overfitting. Results CRUK: An RF classifier model was able to differentiate between the tumor types from pre-treatment images (AUC FSE =0.97, AUC ADC =0.92) as well as between the treated and control arms for the responsive K8484 tumors after treatment (AUC FSE =0.65, AUC ADC =0.77), while no significant difference due to treatment could be established for unresponsive KPC mice (AUCs~0.5). AZ: An RF model was able to separate the treated and control groups after 2 doses of gefitinib (AUC DWI,b700 =0.88, AUC ADC =0.75), while tumour volume changes were observed only after 3 doses of treatment. Linear regression models of ADC features exhibited significant (p<0.05) correlation with Ki67 (Pearson r = 0.71), H&E cell density and % necrotic area (r = 0.62 and 0.66, respectively), and pEGFR (r = 0.45) while no correlation was found for the CC3 histology score (r = 0.2, p = 0.47) in the AZ dataset.

Conclusion Radiomic signatures from FSE and DW images, including ADC maps, can correlate with tumor type, therapy regimes and histopathology metrics. These imaging biomarkers have great potential for evaluating tumor responses in the development of new cancer therapies. EP-2103 The Value of CBCT-Based Tumor Volume and Density Variations in Prediction of Early Response to NSCLC Q. Wen 1 , Z. Jian 2 , W. Linlin 1 , S. Xindong 1 , Y. Yong 2 , Y. Jinming 1 1 Shandong Cancer Hospital Affiliated to Shandong University- Shandong University, Radiation Oncology, Jinan, China 2 Shandong Cancer Hospital Affiliated to Shandong University- Shandong University, Radiation Physics, Jinan, China Purpose or Objective To investigate the correlations between physical density changes and primary tumor volume (TV) variations by kilovoltage cone-beam computed tomography (KV-CBCT). The second aim is to assess whether these could be valuable to predict chemoradiation therapy (CRT) response in non-small cell lung cancer (NSCLC) patients Material and Methods Fifty-four patients with inoperable and locally advanced NSCLC who received CRT and CBCT were involved in this study. Primary tumor were manually delineated on CBCT images on 1st, 6th, 11th, 16th, 21st, 26th, and 32nd fractions. TV and CT numbers (CTN) were measured on each of the seven observation points. Variation ratios of TV (cm3) and CTN mean values (Hu) during the treatment course were analyzed and correlated with clinical outcomes evaluated by RECIST criteria. Pearson test was used for correlation analysis, t-test, and ROC curve were applied to assess the prediction abilities of CTN and TV for CRT outcomes. Results TV reduction was observed in primary tumors for all patients from Day 1 (D1) to Day 32 (D32) CBCTs with a median shrinkage ratio of 28.28 % (-15.57% - 61.67%) and CTN was reduced with a mean value of 24.91±12.34 Hu