Open AccessA Mini Review of Trends towards Automated and Non-Invasive Techniques for Early Detection of Lung Cancer: From Radiomics through Proteogenomics to Breathomics
Author(s) -
Funmilayo S. Moninuola,
Emmanuel Adetiba,
Oluwadamilola Oshin,
Anthony A. Atayero,
Ademola Adeyeye
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1378/3/032010
Subject(s) - radiomics , lung cancer , medicine , radiology , false positive paradox , proteogenomics , cancer , magnetic resonance imaging , radiography , gold standard (test) , lung , pathology , artificial intelligence , computer science , biology , biochemistry , genomics , genome , gene
Carcinoma of the Lung is one of the most common cancers in the world and the leading cause of tumor-related deaths. Less than 15% of patients survive 5 years post diagnosis due to its relatively poor prognosis. This has been ascribed to lack of effective diagnostic methods for early detection. Different medical imaging techniques such as chest radiography, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are used in routine clinical practice for tumor detection. These techniques are medically unsatisfactory and inconvenient for patients due to poor diagnostic accuracy. Endobronchial biopsies are the gold standard for diagnosis but have the inherent risk of full or partial invasive procedures. Thus, diagnostic technology that uses data mining algorithms with medical image analysis, generally known as radiomics emerged. Radiomics extracts complex information from conventional radiographic images and quantitatively correlates image features with diagnostic and therapeutic outcomes. In spite of the benefits, radiomics is prone to high false positives and there is no established standard for acquisition of parameters. Further efforts towards outcome improvement led to the proteomic and genomic (proteogenomic) approach to lung cancer detection. Although proteogenomic has a diagnostic edge over traditional techniques, variations in bio-specimen and heterogeneity of lung cancer still possess a major challenge. Recent findings have established that changes normally occur in the gene or protein due to tumor growth in the lungs and this often leads to peroxidation of cell membrane that releases Volatile Organic Compounds (VOCs) through the breath of Lung Cancer patients. The comprehensive analysis of breath VOCs, which is tagged Breathomics in the literature, unveils opportunities for noninvasive biomarker discovery towards early detection. Breathomics has therefore become the current pace-setter in medical diagnostics research because of its non-invasiveness and cost effectiveness. This paper presents a mini survey of trends in early lung cancer detection from radiomics, through proteogenomic to breathomics.