Authors:
Yan Wu
;
Emmanuel Zachariah
;
Judith Amorosa
;
Anjani Naidu
;
Mina L. Labib
;
Jamil Shaikh
;
Donna Eckstein
;
Sinae Kim
;
John E. Langenfeld
;
Joseph Aisner
;
John L. Nosher
;
Robert S. DiPaola
and
David J. Foran
Affiliation:
The State University of New Jersey, United States
Keyword(s):
Support Vector Machine, Malignant Nodules, Benign Nodules, Pulmonary Nodules, Prediction Model.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Feature Recognition and Extraction Methods
;
Medical Imaging and Diagnosis
Abstract:
Lung cancer is the leading cause of cancer death in the United States and worldwide. Most patients are
diagnosed at an advanced stage, usually stage III or IV. Identification of lung cancer patients at an early
stage might enable oncologists to surgically remove the tumors. Currently, low dose CT scans are used to
identify the malignant nodules in high risk patients. However, screening CT scans yield a high rate of false-positive
results. A prediction model was developed for improved discrimination of malignant nodules from
benign nodules in patients who underwent lung screening CT. CT images and clinical outcomes of 39
patients were obtained from the National Lung Screening Trial (NLST), National Cancer Institute, National
Institute of Health. Images were analyzed to extract computational features relevant to malignancy
prediction. A Support Vector Machine (SVM) based model was developed to predict the malignancy of
nodules. During pilot studies, our model achieved the foll
owing prediction performance: accuracy of 0.74,
sensitivity of 0.85, and specificity of 0.61.
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