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Normalizing Delta Radiomics for Early Prediction of Treatment Response During Chemoradiation Therapy of Pancreatic Cancer

H Nasief*, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI


(Tuesday, 7/14/2020) 1:00 PM - 3:00 PM [Eastern Time (GMT-4)]

Room: Track 2

Purpose: we showed that CT delta-radiomic features (DRF) could predict treatment response in mid-treatment during preoperative chemoradiation therapy (CRT) of pancreatic cancer. This study aims to investigate possibility of earlier prediction by normalizing DRF (NDRF) with respect to those from healthy pancreas.

Methods: analyses were performed with dataset consisting of 1) non-contrast CTs acquired in 28 fractions during routine CT-guided preoperative CRT of 40 patients with pancreatic tumors along with their pathological response data, and 2) daily non-contrast CTs acquired during radiotherapy of 20 patients with other abdominal tumors but with healthy pancreas. DRFs were extracted from pancreatic heads and T-test and coefficient of variance (COV) were used to assess the differences in DRFs between normal-(NP) and diseased-(DP) pancreas. NDRFs for DRFs with significant t-test p-value and COV>5% were calculated. T-test and linear-mixed-effect model were used to assess NDRF correlation to response. Bayesian classifier with leave-one-out cross-validation was used to identify NDRFs with improved response predictions. Performance was judged using the AUC of the ROC curve.

Results: expected, DRFs of NP had much narrow variations compared to DRFs for DP. A total of 23 DRFs were found to be significantly different between their NP and DP values, of which 48% had COV> 5% and were selected to calculate NDRFs. Eight NDRFs passed t-test and linear mixed effect (p<0.05) and showed significant changes as early as at fractions 5-21 during CRT between good and bad response groups. This is an improvement from the fractions 14-18 when using the DRFs. Bayesian classifier AUC increased from 0.93 with using 3 DRFs (kurtosis, contrast, coarseness) combination to 0.97 for using the corresponding NDRFs.

Conclusion: delta radiomics to the corresponding healthy structure can reduce variability and improve performance of DRF. NDRFs can predict treatment response two weeks earlier than DRFs during CRT for pancreatic cancer.


ROC Analysis, CT, Radiation Therapy


IM- CT: Radiomics

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