| Title: | A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer |
| Author(s): | Shaffie A; Soliman A; Fu XA; Nantz M; Giridharan G; van Berkel V; Khalifeh HA; Ghazal M; Elmaghraby A; El-Baz A; |
| Address: | "BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, USA. Department of Chemical Engineering, University of Louisville, Louisville, KY, USA. Department of Chemistry, University of Louisville, Louisville, KY, USA. Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA. Chemical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE. Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, UAE. Computer Science and Engineering Department, University of Louisville, Louisville, KY, USA. BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, USA. aselba01@louisville.edu" |
| DOI: | 10.1038/s41598-021-83907-5 |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| Abstract: | "This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules" |
| Keywords: | "Aged Aged, 80 and over Biomarkers, Tumor/analysis Breath Tests/methods Diagnosis, Computer-Assisted Early Detection of Cancer/methods Female Humans Lung Neoplasms/*diagnosis/diagnostic imaging Male Middle Aged Reproducibility of Results Retrospective Stud;" |
| Notes: | "MedlineShaffie, Ahmed Soliman, Ahmed Fu, Xiao-An Nantz, Michael Giridharan, Guruprasad van Berkel, Victor Khalifeh, Hadil Abu Ghazal, Mohammed Elmaghraby, Adel El-Baz, Ayman eng England 2021/02/27 Sci Rep. 2021 Feb 25; 11(1):4597. doi: 10.1038/s41598-021-83907-5" |
