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NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS

Prasanna, Prateek
http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524

Abstract Details

2017, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
Cancer is not a bounded, self-organized system. Most malignant tumors have heterogeneous growth, leading to disorderly proliferation well beyond the surgical margins. In fact, the impact of certain tumors is observed not just within the visible tumor, but also in the immediate peritumoral, as well as in seemingly normal-appearing adjacent field. Visual inspection is often not a reliable instrument in cancer diagnosis, providing only qualitative analysis of an image, thereby missing subtle disease signatures. These, and other imaging limitations can lead to unnecessary surgical interventions. Computerized image analysis has shown promise in comprehending disease heterogeneity through quantification and detection of sub-visual patterns. In this work, we present novel radiomic tools to identify subtle radiologic cues (radiomic descriptors) and address clinical challenges in cancer diagnosis, prognosis, and treatment-evaluation. The developed tools and techniques are modality- and domain-agnostic. They can be applied in a pan-cancer setting to mine information from radiographic images and discover associations with underlying molecular (radio-genomics) or histological (radio-pathomics) characteristics to provide a holistic characterization of disease. We have demonstrated their efficacy in addressing problems in prognosis and treatment management of brain tumors. The challenges we target specifically include (1) inability to estimate survival at a pre-treatment stage and (2) inability to avoid highly-invasive surgeries in patients with radiation-induced treatment changes that mimic tumor recurrence. Underlying heterogeneity is linked to poor prognosis and tumor recurrence. Cellular level differences associated with the distinct physiological pathways might also manifest at the radiographic (i.e. MRI) length scale. We present two radiomic descriptors, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) and radiographic-Deformation and Textural Heterogeneity (r-DepTH), which attempt to capture voxel-level textural and structural heterogeneity associated with brain tumors on MRI. These radiomic features are extracted not only from the solid tumor regions, but also from the adjacent tumor habitat and the healthy parenchyma. Subsequently, they are used in a machine learning setting to predict survival on treatment-naive imaging and characterize radiation-induced effects on post-treatment MRI. Further, via human-machine comparison experiments, we demonstrate the utility of radiomic-based frameworks as a second read decision support in cancer management.
Anant Madabhushi (Advisor)
Pallavi Tiwari (Committee Chair)
David Wilson (Committee Member)
Lisa Rogers (Committee Member)
Charles Lanzieri (Committee Member)
201 p.
Biomedical Engineering; Biomedical Research
Radiomics; Texture; Brain; Necrosis; Recurrence; Machine Learning; Radiogenomics; GBM; CoLlAGe; Cancer; Image analysis; Habitat; Survival; Prognosis; Diagnosis; treatment evaluation

Recommended Citations

Citations

  • Prasanna, P. (2017). NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524

    APA Style (7th edition)

  • Prasanna, Prateek. NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS. 2017. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

    MLA Style (8th edition)

  • Prasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS." Doctoral dissertation, Case Western Reserve University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524

    Chicago Manual of Style (17th edition)

case149624929700524
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© 2017, some rights reserved.Creative Commons License NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS by Prateek Prasanna 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 Case Western Reserve University School of Graduate Studies and OhioLINK.