Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth Uni...

Full description

Bibliographic Details
Main Author:	Mahon, Rebecca N
Format:	Others
Published:	VCU Scholars Compass 2018
Subjects:	machine learning radiomics MRI lung cancer convolutional neural networks Investigative Techniques
Online Access:	https://scholarscompass.vcu.edu/etd/5516 https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=6604&context=etd

Similar Items

Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis
by: Zheng Xiao, et al.
Published: (2021-05-01)

Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas
by: Min Gao, et al.
Published: (2020-09-01)

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning
by: Wei Zhao, et al.
Published: (2019-07-01)

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
by: Maikel Verduin, et al.
Published: (2021-02-01)

Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer
by: Bihong T. Chen, et al.
Published: (2021-03-01)

Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan
by: Sangwoo Lee, et al.
Published: (2020-09-01)

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
by: Francesca Coppola, et al.
Published: (2021-04-01)

A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI
by: Jiun-Lin Yan, et al.
Published: (2021-04-01)

Novel Nomogram for Preoperative Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma
by: Wenjie Liang, et al.
Published: (2018-09-01)

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate Cancer: High Grade vs. Low Grade
by: Jose M. Castillo T., et al.
Published: (2021-02-01)

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study
by: Ahmad Algohary, et al.
Published: (2020-08-01)

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer
by: Ying Liu, et al.
Published: (2021-03-01)

Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma
by: Bing Xiao, et al.
Published: (2021-04-01)

Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images
by: Runsheng Chang, et al.
Published: (2021-07-01)

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer
by: Xiangchun Liu, et al.
Published: (2021-02-01)

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging
by: Ying-Zhi Sun, et al.
Published: (2021-02-01)

MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma
by: Brancato, V., et al.
Published: (2022)

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients
by: Duo Hong, et al.
Published: (2021-05-01)

Establishing Correlations between Breast Tumor Response to Radio-Immunotherapy and Radiomics from Multi-Parametric Imaging: An Animal Study
by: Anis Ahmad, et al.
Published: (2020-09-01)

3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
by: Stefania Montemezzi, et al.
Published: (2021-04-01)

CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging
by: Yucheng Zhang, et al.
Published: (2020-02-01)

SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
by: Devinder Kumar, et al.
Published: (2019-01-01)

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
by: Fangyi Xu, et al.
Published: (2020-08-01)

Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
by: Fangyi Xu, et al.
Published: (2020-10-01)

Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data
by: Darcie A. P. Delzell, et al.
Published: (2019-12-01)

Corrigendum: Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data
by: Darcie A. P. Delzell, et al.
Published: (2020-06-01)

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas
by: Yang Zhang, et al.
Published: (2020-12-01)

Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma
by: Luyuan Zhang, et al.
Published: (2020-06-01)

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes
by: Wei Zhao, et al.
Published: (2019-10-01)

Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images
by: Lin Qi, et al.
Published: (2021-06-01)

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics
by: Jan C. Peeken, et al.
Published: (2021-04-01)

Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer
by: Benedetta Gui, et al.
Published: (2021-03-01)

Imaging-Based Individualized Response Prediction Of Carbon Ion Radiotherapy For Prostate Cancer Patients
by: Wu S, et al.
Published: (2019-10-01)

MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
by: Xiaoping Yi, et al.
Published: (2019-06-01)

Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
by: Jia Liu, et al.
Published: (2019-09-01)

Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases
by: Jing Huang, et al.
Published: (2021-09-01)

Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension
by: Sarv Priya, et al.
Published: (2021-04-01)

A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy
by: Elizabeth J. Sutton, et al.
Published: (2020-05-01)

Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?
by: Sarv Priya, et al.
Published: (2021-05-01)

The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics
by: Cyril Jaudet, et al.
Published: (2021-08-01)