Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

Authors

Elsa Parr, University of Nebraska Medical CenterFollow
Qian Du, University of Nebraska-LincolnFollow
Chi Zhang, University of Nebraska-LincolnFollow
Chi Lin, University of Nebraska Medical CenterFollow
Ahsan Kamal, University of Nebraska Medical CenterFollow
Josiah McAlister, University of Nebraska Medical CenterFollow
Xiaoying Liang, Proton Institute, University of FloridaFollow
Kyle Bavitz, University of Nebraska Medical CenterFollow
Gerard Rux, University of Nebraska Medical CenterFollow
Michael Hollingsworth, University of Nebraska Medical CenterFollow
Michael Baine, University of Nebraska Medical CenterFollow
Dandan Zheng, University of Nebraska Medical CenterFollow

ORCID IDs

Chi Lin

Michael Baine

Date of this Version

2020

Citation

Cancers 2020, 12, 1051; doi:10.3390/cancers12041051

Comments

© 2020 by the authors.

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.