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Full text release has been delayed at the author's request until January 20, 2025

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Evaluating Artificial Intelligence Radiology Models for Survival Prediction Following Immunogenic Regimen in Brain Metastases

Gidwani, Mishka
http://rave.ohiolink.edu/etdc/view?acc_num=case1668099306231271

Abstract Details

, Doctor of Philosophy, Case Western Reserve University, Molecular Medicine.
Novel therapeutic regimens which spur the endogenous immune system to kill cancer cells, such as stereotactic radiosurgery (SRS) and immune checkpoint inhibition (ICI), are heterogeneously effective. Understanding causal factors of response is vital to guide risk assessment and treatment decisions. In this thesis, I evaluate the ability of three methods to prognosticate survival for brain metastases patients following SRS and ICI treatment. These include the clinically utilized response assessment in neuro-oncology for brain metastases (RANO-BM) protocol, as well as investigational computational methods such as radiomic feature analysis and convolutional neural network (CNN) image analysis. I find that easing the 10mm RANO-BM diameter threshold for measurable disease allows new lesions to be discovered as proof of progression in ICI-treated metastases. Further, I find that the trajectory of RANO-BM diameter can be more instructive for risk prediction than the ratio-change and that neither volume nor number of metastases, nor RANO-BM diameter can significantly predict survival until a year after treatment. Reproducing common radiomic methodology flaws observed in the published literature, I demonstrate that inconsistent partitioning, or the improper division of radiomic feature data into Training, Validation, Test, and External test sets, can provide a 1.4x performance boost to reported accuracy (AUROC) for predictive models. Additionally, I highlight how spurious correlations with biological variables can overstate the importance of radiomic features. Leveraging the conclusions from my radiomic reproduction study, I assess the ability of radiomic features and convolutional neural networks (CNNs) to predict overall survival in the largest ICI-treated brain metastases cohort assembled to date, comprising 175 patients from three institutions in two countries. I find that neither radiomic features nor any architecture of the survival AI model MetsSurv is capable of predicting survival. By comparing collaborative learning approaches for AI model development, I conclude that institutional and imaging differences are large enough to hinder the generalizability of a centralized model, and that the best survival model is a local model developed at each institution. Taken together, these insights lead me to recommend RANO-BM diameter trajectory for risk assessment until a larger, multi-modal dataset can be assembled, at which point the approaches considered herein may warrant further study.
Jacob Scott (Advisor)
Brian Rubin (Committee Chair)
Elizabeth Gerstner (Committee Member)
Anant Madabhushi (Committee Member)
Jayashree Kalpathy-Cramer (Advisor)
Nathan Pennell (Committee Member)
133 p.
Artificial Intelligence; Computer Science; Immunology; Medical Imaging; Molecular Biology; Neurology; Oncology; Radiology
Artificial Intelligence; AI; Radiomics; Brain Metastases; Survival; RANO; Neurooncology; Oncology; Radiation oncology; immunotherapy; ICI; CNN; medical imaging; deep learning; DL; tumor microenvironment; Brain; Neurology

Recommended Citations

Citations

  • Gidwani, M. (2023). Evaluating Artificial Intelligence Radiology Models for Survival Prediction Following Immunogenic Regimen in Brain Metastases [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1668099306231271

    APA Style (7th edition)

  • Gidwani, Mishka. Evaluating Artificial Intelligence Radiology Models for Survival Prediction Following Immunogenic Regimen in Brain Metastases. 2023. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1668099306231271.

    MLA Style (8th edition)

  • Gidwani, Mishka. "Evaluating Artificial Intelligence Radiology Models for Survival Prediction Following Immunogenic Regimen in Brain Metastases." Doctoral dissertation, Case Western Reserve University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=case1668099306231271

    Chicago Manual of Style (17th edition)

case1668099306231271
© 2023, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.