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Full text release has been delayed at the author's request until August 16, 2026

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Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Treatment Regimen in Rectal Cancers Via Pre-Treatment MRI

Bao, Leo
http://orcid.org/0000-0003-0996-4378
http://rave.ohiolink.edu/etdc/view?acc_num=case1714665344155371

Abstract Details

2024, Master of Engineering, Case Western Reserve University, Biomedical Engineering.
Radiomic analysis of individual regions or acquisitions has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI. We present a novel multi-plane, multi-region radiomics framework for exploiting intuitive clinical and biological aspects of rectal tumor response on MRI. Using a multi-institutional cohort of 151 baseline T2-weighted axial and coronal rectal MRIs, 2D texture features were extracted from multiple regions of interest (tumor, tumor-proximal fat) across both axial and coronal planes, with machine learning analysis to identify descriptors predictive of complete response to neoadjuvant therapy. Our multi-plane, multi-region radiomics model was found to significantly outperform single-plane or single-region feature sets with a discovery area under the ROC curve (AUC) of 0.765±0.054, and hold-out validation AUCs of 0.700 and 0.759. This suggests multi- region, multi-plane radiomics could enable detailed phenotyping of treatment response on MRI and thus personalization of therapeutic and surgical interventions in rectal cancers.
Satish Viswanath (Committee Chair)
Amit Gupta (Committee Member)
Juhwan Lee (Committee Member)
29 p.
Artificial Intelligence; Bioinformatics; Biomedical Engineering; Biomedical Research; Medical Imaging
radiomics, multi-plane, MRI, rectal cancer, treatment response

Recommended Citations

Citations

  • Bao, L. (2024). Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Treatment Regimen in Rectal Cancers Via Pre-Treatment MRI [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1714665344155371

    APA Style (7th edition)

  • Bao, Leo. Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Treatment Regimen in Rectal Cancers Via Pre-Treatment MRI. 2024. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1714665344155371.

    MLA Style (8th edition)

  • Bao, Leo. "Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Treatment Regimen in Rectal Cancers Via Pre-Treatment MRI." Master's thesis, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1714665344155371

    Chicago Manual of Style (17th edition)

case1714665344155371
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.