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Proceedings Paper

Quantitative analysis of adipose tissue on chest CT to predict primary graft dysfunction in lung transplant recipients: a novel optimal biomarker approach
Author(s): Yubing Tong; Jayaram K. Udupa; Chuang Wang; Caiyun Wu; Gargi Pednekar; Michaela D. Restivo; David J. Lederer; Jason D. Christie; Drew A. Torigian
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Paper Abstract

In this study, patients who underwent lung transplantation are categorized into two groups of successful (positive) or failed (negative) transplantations according to primary graft dysfunction (PGD), i.e., acute lung injury within 72 hours of lung transplantation. Obesity or being underweight is associated with an increased risk of PGD. Adipose quantification and characterization via computed tomography (CT) imaging is an evolving topic of interest. However, very little research of PGD prediction using adipose quantity or characteristics derived from medical images has been performed.

The aim of this study is to explore image-based features of thoracic adipose tissue on pre-operative chest CT to distinguish between the above two groups of patients. 140 unenhanced chest CT images from three lung transplant centers (Columbia, Penn, and Duke) are included in this study. 124 patients are in the successful group and 16 in failure group. Chest CT slices at the T7 and T8 vertebral levels are captured to represent the thoracic fat burden by using a standardized anatomic space (SAS) approach. Fat (subcutaneous adipose tissue (SAT)/ visceral adipose tissue (VAT)) intensity and texture properties (1142 in total) for each patient are collected, and then an optimal feature set is selected to maximize feature independence and separation between the two groups. Leave-one-out and leave-ten-out crossvalidation strategies are adopted to test the prediction ability based on those selected features all of which came from VAT texture properties. Accuracy of prediction (ACC), sensitivity (SEN), specificity (SPE), and area under the curve (AUC) of 0.87/0.97, 0.87/0.97, 0.88/1.00, and 0.88/0.99, respectively are achieved by the method. The optimal feature set includes only 5 features (also all from VAT), which might suggest that thoracic VAT plays a more important role than SAT in predicting PGD in lung transplant recipients.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753K (27 February 2018); doi: 10.1117/12.2294050
Show Author Affiliations
Yubing Tong, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Chuang Wang, Univ. of Pennsylvania (United States)
Caiyun Wu, Univ. of Pennsylvania (United States)
Gargi Pednekar, Univ. of Pennsylvania (United States)
Michaela D. Restivo, Columbia Univ. Medical Ctr. (United States)
David J. Lederer, Columbia Univ. Medical Ctr. (United States)
Jason D. Christie, Univ. of Pennsylvania Ctr. for Clinical Epidemiology and Biostatistics (United States)
Drew A. Torigian, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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