PREDICTION OF EARLY STAGE LUNG CANCER PROGNOSIS AFTER SURGERY USING A NEW CAD-GENERATED IMAGING MARKER

BOYAPALLY, Venkata Sai Chandra

PREDICTION OF EARLY STAGE LUNG CANCER PROGNOSIS AFTER SURGERY USING A NEW CAD-GENERATED IMAGING MARKER

dc.contributor.advisor	Zheng, Bin
dc.contributor.author	BOYAPALLY, Venkata Sai Chandra
dc.contributor.committeeMember	Qiu, Yuchen
dc.contributor.committeeMember	Cheng, Samuel
dc.date.accessioned	2018-05-11T15:38:58Z
dc.date.available	2018-05-11T15:38:58Z
dc.date.issued	2018-05-11
dc.date.manuscript	2018-05-05
dc.description.abstract	Due to cancer heterogeneity, identifying new clinical markers to more effectively predict prognosis of cancer patients plays an important role to improve efficacy of cancer treatment. Objective of this study is to develop and test a new quantitative imaging (QI) marker to predict prognosis of early stage non-small cell lung cancer (NSCLC) patients after surgery. For this purpose, this study includes following research tasks or steps. First, a new computer-aided detection (CAD) scheme was developed to automatically segment lung and tumor regions from the chest computed tomography (CT) images of all the slices simultaneously using an adaptive pixel value thresholding and/or region growing method. Next, CAD scheme was used to compute a large number of image features related to tumor shape, size, circularity, density heterogeneity, and lung background tissue patterns. Then, a machine learning approach was applied to build a multi-feature fusion based prediction model, which enables to produce a CAD-generated quantitative image (QI) marker for predicting diseases-free survival (DFS) of the NSCLC patients within 3 years after surgery. In order to achieve more robust result of training and testing the machine learning model, a leave-one-case-out (LOCO) cross-validation method was used. A feature selection process using a correlation-based feature subset evaluator and a synthetic minority oversampling technique (SMOTE) were embedded in LOCO based training process. Finally, prediction performance of the QI marker or prediction model was evaluated using the receiver operating characteristic (ROC) and other statistical data analysis. In summary, the goal of this study is to select more effective image features computed from both segmented lung tumors and emphysema related background regions for producing a new CAD-generated QI marker and demonstrate the feasibility of applying this new QI marker to yield higher performance in predicting prognosis of early stage NSCLC patients	en_US
dc.identifier.uri	https://hdl.handle.net/11244/299889
dc.language	en_US	en_US
dc.subject	Engineering, Electronics and Electrical.	en_US
dc.subject	Image Processing	en_US
dc.subject	Lung Cancer	en_US
dc.subject	Computer Aided Detection	en_US
dc.subject	Predictive model	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	PREDICTION OF EARLY STAGE LUNG CANCER PROGNOSIS AFTER SURGERY USING A NEW CAD-GENERATED IMAGING MARKER	en_US
ou.group	College of Engineering::School of Electrical and Computer Engineering	en_US
shareok.nativefileaccess	restricted	en_US