A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer

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.

Figure 1

Lung nodule classification framework.The framework was generated by Microsoft PowerPoint 2019 (https://www.microsoft.com/zh-cn/microsoft-365/powerpoint).

Figure 2

Shape approximation for malignant and benign nodules.The figure was created in MATLAB R2018B (https://www.mathworks.com/products/matlab.html).

Figure 3

A sample of benign (rst) and malignant (second-row) nodules (a), their 3D visualization of HU values (b), and their Gibbs energy which shows high energy for (brighter) for benign and less energy for malignant (darker) (c). The figure was created in MATLAB R2018B (https://www.mathworks.com/products/matlab.html).

Figure 4

(a) Schematic setup for capture of carbonyl VOCs in exhaled breath, (b) photo of the breath collection system, (c) A microfabricated microchip with fused silica tubes attached to inlet and outlet ports; (d) optical picture of the microchip created by DRIE; (e) SEM micrograph of the micropillar array within the preconcentrator.