Radiomics-based outcome prediction for pancreatic cancer following stereotactic body radiotherapy

Elsa Parr, Qian Du, Chi Zhang, Chi Lin, Ahsan Kamal, Josiah McAlister, Xiaoying Liang, Kyle Bavitz, Gerard Rux, Michael Hollingsworth, Michael Baine, Dandan Zheng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

Original languageEnglish (US)
Article number1051
JournalCancers
Volume12
Issue number4
DOIs
StatePublished - Apr 2020

Keywords

  • Clinical model
  • Pancreatic cancer
  • Prognosis prediction
  • Radiomics
  • SBRT
  • The combined model
  • The radiomic model

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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