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Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography

Pattiam Giriprakash, Pavithran
http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398

Abstract Details

2021, Master of Science in Biomedical Engineering, Cleveland State University, Washkewicz College of Engineering.
The usage of radiomics for extracting high-dimensional features from radiographic imaging to quantify subtle changes in tissue structure and heterogeneity has shown great potential for disease diagnosis and prognosis. However, radiomic features are known to be impacted by acquisition-related changes (e.g., dose and reconstruction variations in CT scans) as well as technical variations between cohorts (i.e., batch effects due to varying dosage and tube currents). Using features which are not resilient to such imaging variations can result in poor performance of the downstream radiomics classifier models. In this study, we present a framework to systematically identify radiomic features that are resilient to both batch effects and acquisition differences, as well as evaluate the impact of such variations on radiomic model performance. We demonstrate the utility of our approach in the context of distinguishing active Crohn’s disease (CD) from healthy controls using a uniquely accrued cohort of 164 CTE scans accrued from a single institution, which included (a) batch effects due to variations in effective dosage and tube current, as well as (b) scans simultaneously acquired at multiple doses and reconstructions (3 variations per patient). Our framework involves systematically evaluating the impact of acquisition variations (based on feature robustness to explicit dose/acquisition changes) and batch effects (based on feature stability to implicit dosage/current variations). Resilient radiomic features identified after accounting for both types of variations yielded the best random forest classifier performance across both discovery (AUC=0.819 ± 0.043) and validation (AUC=0.787) cohorts when using full-dose images; also found to be significantly more generalizable than features that were not optimized for such variations (AUC=0.419 in validation). This subset of radiomic features that were both robust and stable (resilient) also maintained their performance when evaluated for diagnosing CD using the reduced dose images reconstructed using Filtered Back Projection (FBP) or Safire based Iterative Reconstruction. Optimizing radiomic features to be stable to batch effects was found to improve classifier model performance beyond optimizing them to be robust to acquisition variations alone. Thus, building diagnostic radiomic models to be both resilient and generalizable may require accounting for their sensitivity to acquisition variations as well as batch effects.
Satish E. Viswanath (Committee Chair)
Hongkai Yu (Committee Member)
Moo-Yeal Lee (Advisor)
65 p.
Biology; Biomedical Engineering; Biomedical Research; Medical Imaging; Radiology
Radiomics, CT Enterography, Machine learning, Crohn's disease, Feature reproducibility, Feature stability, AUC, Cross-validation, MATLAB, R studio, Python, Image processing, Medical image analysis

Recommended Citations

Citations

  • Pattiam Giriprakash, P. (2021). Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography [Master's thesis, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398

    APA Style (7th edition)

  • Pattiam Giriprakash, Pavithran. Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography. 2021. Cleveland State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398.

    MLA Style (8th edition)

  • Pattiam Giriprakash, Pavithran. "Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography." Master's thesis, Cleveland State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398

    Chicago Manual of Style (17th edition)

csu1629542175523398
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© 2021, some rights reserved.Creative Commons License Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography by Pavithran Pattiam Giriprakash is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Cleveland State University and OhioLINK.