Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9

Haidy Nasief, William Hall, Cheng Zheng, Susan Tsai, Liang Wang, Beth Erickson, X. Allen Li

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Recently we showed that delta radiomics features (DRF) from daily CT-guided chemoradiation therapy (CRT) is associated with early prediction of treatment response for pancreatic cancer. CA19-9 is a widely used clinical biomarker for pancreatic cancer. The purpose of this work is to investigate if the predictive power of such biomarkers (DRF or CA19-9) can improve by combining both biomarkers. Daily non-contrast CTs acquired during routine CT-guided neoadjuvant CRT for 24 patients (672 datasets, in 28 daily fractions), along with their CA19-9, pathology reports and follow-up data were analyzed. The pancreatic head was segmented on each daily CT and radiomic features were extracted from the segmented regions. The time between the end of treatment and last follow-up was used to build a survival model. Patients were divided into two groups based on their pathological response. Spearman correlations were used to find the DRFs correlated to CA19-9. A regression model was built to examine the effect of combining CA19-9 and DRFs on response prediction. C-index was used to measure model effectiveness. The effect of a decrease in CA19-9 levels during treatment vs. failure of CA19-9 levels to normalize on survival was examined. Univariate- and multivariate Cox-regression analysis were performed to determine the effect of combining CA19-9 and DRFs on survival correlations. Spearman correlation showed that CA19-9 is correlated to DRFs (Entropy, cluster tendency and coarseness). An Increase in CA19-9 levels during treatment were correlated to a bad response, while a decline was correlated to a good response. Incorporating CA19-9 with DRFs increased the c-index from 0.57 to 0.87 indicating a stronger model. The univariate analysis showed that patients with decreasing CA19-9 had an improved median survival (68 months) compared to those with increasing levels (33 months). The 5-years survival was improved for the decreasing CA19-9 group (55%) compared to the increasing group (30%). The Cox-multivariate analysis showed that treatment related decrease in CA19-9 levels (p = 0.031) and DRFs (p = 0.001) were predictors of survival. The hazard-ratio was reduced from 0.73, p = 0.032 using CA19-9 only to 0.58, p = 0.028 combining DRFs with CA19-9. DRFs-CA19-9 combination has the potential to increasing the possibility for response-based treatment adaptation.

Original languageEnglish (US)
Article number1464
JournalFrontiers in Oncology
StatePublished - Jan 8 2020
Externally publishedYes


  • CA19-9
  • biomarkers
  • chemo-radiation therapy
  • pancreatic cancer
  • response assessment
  • survival

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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