ArticleScientific reports2021
A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives.Cancer metastasis reviews · 2025Review
- Chest CT signs and serum homocysteine levels can effectively diagnose chronic heart failure.American journal of translational research · 2025Article
- The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review.Cureus · 2022Review
- [Advances on Collection and Analysis of Volatile Organic Compounds in the Diagnosis of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.