Computational imaging and multiomic biomarkers for precision medicine: characterizing heterogeneity in lung cancer.
type: CreativeWork
description: Lung cancer is the leading cause of cancer deaths and is the third most diagnosed cancer in both men and women in the United States. Non-small cell lung cancer (NSCLC) accounts for 84% of all lung cancer cases. The inherent intra-tumor and inter-tumor heterogeneity in lung tumors has been linked with adverse clinical outcomes. A well-rounded characterization of tumor heterogeneity by personalized biomarkers is needed to develop precision medicine treatment strategies for cancer. Large-scale genome-based characterization poses the disadvantages of high cost and technical complexity. Further, a histopathological sample from a tumor biopsy may not be able to fully represent the structural and functional properties of the entire tumor. Medical imaging is now emerging as a key player in the field of personalized medicine, due to its ability to non-invasively characterize the anatomical and physiological properties of the tumor regions. The studies included in this thesis introduce analytical tools developed thorough machine learning and bioinformatics and use information from diagnostic images and other “omic” sources, to develop computational imaging and multiomic biomarkers to characterize intratumor heterogeneity. A novel radiomic biomarker, that integrates with PDL1 expression, ECOG status, BMI, and smoking status, to enhance the ability to predict progression-free survival in a preliminary cohort of patients with stage 4 NSCLC, treated with first-line anti-PD1/PDL1 checkpoint inhibitor therapy PEMBROLIZUMAB. This study also showed that mitigation of the heterogeneity introduced by voxel spacing and image acquisition parameters improves the prognostic performance of the radiomic phenotypes. We further performed a detailed investigation of the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. The results of this second study indicated that accounting for heterogeneity in image param…
abstract: Lung cancer is the leading cause of cancer deaths and is the third most diagnosed cancer in both men and women in the United States. Non-small cell lung cancer (NSCLC) accounts for 84% of all lung cancer cases. The inherent intra-tumor and inter-tumor heterogeneity in lung tumors has been linked with adverse clinical outcomes. A well-rounded characterization of tumor heterogeneity by personalized biomarkers is needed to develop precision medicine treatment strategies for cancer. Large-scale genome-based characterization poses the disadvantages of high cost and technical complexity. Further, a histopathological sample from a tumor biopsy may not be able to fully represent the structural and functional properties of the entire tumor. Medical imaging is now emerging as a key player in the field of personalized medicine, due to its ability to non-invasively characterize the anatomical and physiological properties of the tumor regions. The studies included in this thesis introduce analytical tools developed thorough machine learning and bioinformatics and use information from diagnostic images and other “omic” sources, to develop computational imaging and multiomic biomarkers to characterize intratumor heterogeneity. A novel radiomic biomarker, that integrates with PDL1 expression, ECOG status, BMI, and smoking status, to enhance the ability to predict progression-free survival in a preliminary cohort of patients with stage 4 NSCLC, treated with first-line anti-PD1/PDL1 checkpoint inhibitor therapy PEMBROLIZUMAB. This study also showed that mitigation of the heterogeneity introduced by voxel spacing and image acquisition parameters improves the prognostic performance of the radiomic phenotypes. We further performed a detailed investigation of the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. The results of this second study indicated that accounting for heterogeneity in image param…
author: Singh, Apurva
language: en
keywords: Engineering
date published: 2024
date modified: 2026-07-24T03:45:38Z
provider: University of Pennsylvania
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Pennsylvania