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.
Highlights
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
The prohibitive costs associated with repeated radiographic scans and the morbidity due to unnecessary invasive procedures for benign nodules necessitate the development of new diagnostic modality that can detect malignant pulmonary nodules
The research protocol was approved by the Institutional Review Board (IRB) at the University of Louisville and all methods were performed in accordance with the relevant guidelines and regulations
Summary
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. Despite requiring multiple serial CT scans for indeterminant pulmonary nodules over two years, these methods have a low classification accuracy for early diagnoses of lung cancer because they: (1) do not account for large deformations in lung tissue due to breathing and beating of the native heart; and (2) do not use the 3D shape and appearance of detected nodules in conjunction with estimated nodule growth rate. The proposed CAD system is non-invasive, requiring only a single CT scan and a breath test to rapidly and accurately diagnose lung cancer (a few days compared to two years), with the potential to greatly reduce lung cancer diagnosis costs and increase the patient survival rate
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