miRNAs as Non-Invasive Biomarkers for Lung Cancer Diagnosis

This article reviews the literature and summarizes single institution experience of applying different diagnostic algorithms for lung cancer. All diagnostic methods can be divided into three groups: non-invasive; minimally invasive and invasive. The non-invasive methods include clinical examination; imaging methods for anatomical, functional and multimodal visualization; sputum cytological, analysis of the exhaled breath, detection of various blood and sputum markers. Minimally invasive methods include endoscopy, percutaneous fine-needle and core-needle biopsy. Invasive methods include diagnostic thoracoscopy and laparoscopy, mediastinoscopy, parasternal mediastinotomy and diagnostic thoracotomy. While creating an individual diagnostic plan for each patient it is necessary to carefully analyze the effectiveness, safety, sensitivity, specificity and of different methods available among wide range of modern diagnostic techniques. Optimization of lung cancer diagnosis methods, which includes early cancer detection, is one of priority areas of modern oncology. Many aspects of this problem remain unresolved and require further research

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Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy

Molecular Cancer ◽

10.1186/s12943-021-01323-9 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Kezhong Chen ◽

Jianlong Sun ◽

Heng Zhao ◽

Ruijingfang Jiang ◽

Jianchao Zheng ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Liquid Biopsy ◽

Non Invasive ◽

Diagnosis And Prognosis ◽

Lung Cancer Diagnosis ◽

Cancer Diagnosis And Prognosis

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Non‐invasive lung cancer diagnosis by detection of GATA 6 and NKX 2‐1 isoforms in exhaled breath condensate

EMBO Molecular Medicine ◽

10.15252/emmm.201606382 ◽

2016 ◽

Vol 8 (12) ◽

pp. 1380-1389 ◽

Cited By ~ 14

Author(s):

Aditi Mehta ◽

Julio Cordero ◽

Stephanie Dobersch ◽

Addi J Romero‐Olmedo ◽

Rajkumar Savai ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Exhaled Breath Condensate ◽

Exhaled Breath ◽

Non Invasive ◽

Lung Cancer Diagnosis ◽

Breath Condensate

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Non-invasive approaches for lung cancer diagnosis

Indian Journal of Thoracic and Cardiovascular Surgery ◽

10.1007/s12055-017-0600-4 ◽

2017 ◽

Vol 34 (S1) ◽

pp. 11-19

Author(s):

Aditi Mehta ◽

Guillermo Barreto

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Non Invasive ◽

Lung Cancer Diagnosis

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COMPUTERIZED RADIAL ARTERY PULSE SIGNAL CLASSIFICATION FOR LUNG CANCER DETECTION

Facta Universitatis Series Mechanical Engineering ◽

10.22190/fume170504021z ◽

2017 ◽

Vol 15 (3) ◽

pp. 535

Author(s):

Zhichao Zhang ◽

Anton Umek ◽

Anton Kos

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Pressure Sensors ◽

Pulse Signal ◽

Main Diagnosis ◽

Pulse Diagnosis ◽

Lung Cancer Patients ◽

Non Invasive ◽

Lung Cancer Diagnosis ◽

Diagnosis Method

Pulse diagnosis, the main diagnosis method in traditional Chinese medicine, is a non-invasive and convenient way to check the health status. Doctors usually use three fingers to feel three positions; Cun, Guan, and Chi of the wrist pulse, to diagnose the body’s healthy status. However, it takes many years to master the pulse diagnosis. This paper aims at finding the best position for acquiring wrist-pulse-signal for lung cancer diagnosis. In our paper, the wrist-pulse-signals of Cun, Guan, and Chi are acquired by three optic fiber pressure sensors of the same type. Twelve features are extracted from the signals of these three positions, respectively. Eight classifiers are applied to detect the effectiveness of the signal acquired from each position by classifying the pulse signals of healthy individuals and lung cancer patients. The results achieved by the proposed features show that the signal acquired at Cun is more effective for lung cancer diagnosis than the signals acquired at Guan and Chi.

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Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy

10.1101/2020.08.20.20179044 ◽

2020 ◽

Author(s):

Kezhong Chen ◽

Jianlong Sun ◽

Heng Zhao ◽

Ruijingfang Jiang ◽

Jianchao Zheng ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Prognostic Markers ◽

Lung Cancer Screening ◽

Protein Marker ◽

Non Invasive ◽

Diagnosis And Prognosis ◽

Lung Cancer Diagnosis ◽

Mutational Status ◽

Cancer Diagnosis And Prognosis

Chest LDCT provides an effective approach for lung cancer screening, yet has been found to generate a large number of false positives during practice due to excessive diagnosis of pulmonary lesions of indeterminate clinical significance. In this study, we performed comprehensive genetic and epigenetic profiling of cfDNA from lung cancer patients and individuals bearing benign lung lesions, using ultra-deep targeted sequencing and targeted bisulfite sequencing. We found that cfDNA mutation profile alone has relatively limited power in distinguishing malignant from benign plasma, while cfDNA methylation profiling showed a better performance for classification of the two groups and combination of genetic and epigenetic features of cfDNA along with serum protein marker further improved the classification accuracy. We also identified novel methylation-based prognostic markers and showed that an integrated model that combined cfDNA mutational status and methylation-based prognostic markers improved prediction for lung cancer survival. Our results highlight the potential of the multi-analyte assay for non-invasive lung cancer diagnosis and prognosis.

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