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Big Data and Precision Medicine Series I: Lung Cancer Early...
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Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 21944

Special Issue Editor

Dr. Roberto Gasparri

E-Mail Website
Guest Editor
Department of Thoracic Surgery, European Institute of Oncology, Via Giuseppe Ripamonti 435, 20141 Milan, Italy
Interests: lung cancer; thoracic surgery; thoracic diseases; early diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lung cancer is the leading cause of cancer-related deaths and patients’ survival is directly correlated to disease stage. Therefore, it is mandatory to adopt a screening strategy that embraces, not only the population at risk, but improve overall survival in high risk populations of asymptomatic patients. Today, the concept of tumor has been remodeled and it has been defined as a disease that has its own genetic, biological and metabolic identity and with this new awareness, new screening methods should base. The analysis of new biomarkers (i.e., volatile organic compounds, microRNA-test and urine analysis) associated with a high specificity of new screening methods, that are non-invasive, safety, inexpensive and simple to perform, could allow a non-invasive approach that can determine a big change in the early diagnosis of cancer and survival rate. Please join us in presenting this Special Issue on the state-of-the-art research currently being performed on lung cancer screening and early diagnosis to gather our effort in order to get, as soon as possible, an early and effective diagnosis of lung cancer.

Dr. Gasparri Roberto
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Lung cancer
  • Screening
  • Early diagnosis
  • Prevention
  • Proteomics
  • MicroRNA
  • Volatile organic compounds

Published Papers (5 papers)

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Editorial

Jump to: Research, Review

2 pages, 145 KiB  
Editorial
Comment from the Editor to the Special Issue: “Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis”
by Roberto Gasparri, Giulia Sedda and Lorenzo Spaggiari
J. Clin. Med. 2018, 7(2), 28; https://doi.org/10.3390/jcm7020028 - 09 Feb 2018
Cited by 2 | Viewed by 3008
Abstract
With this Editorial we want to present the Special Issue “Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis” to the scientific community, which aims to gather experts on the early detection of lung cancer in order to implement common efforts [...] Read more.
With this Editorial we want to present the Special Issue “Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis” to the scientific community, which aims to gather experts on the early detection of lung cancer in order to implement common efforts in the fight against cancer. Full article
(This article belongs to the Special Issue Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis)

Research

Jump to: Editorial, Review

9 pages, 896 KiB  
Article
Relationship between Inflammatory and Biological Markers and Lung Cancer
by Füsun Şahin and Ayşe Feyza Aslan
J. Clin. Med. 2018, 7(7), 160; https://doi.org/10.3390/jcm7070160 - 25 Jun 2018
Cited by 39 | Viewed by 4366
Abstract
We seek to define inflammatory markers, lipid and protein profiles that may aid in distinguishing lung cancer cases from those who are healthy and to determine the relationships between these levels and cancer stage and cell type. Lung cancer patients (n = [...] Read more.
We seek to define inflammatory markers, lipid and protein profiles that may aid in distinguishing lung cancer cases from those who are healthy and to determine the relationships between these levels and cancer stage and cell type. Lung cancer patients (n = 140, Group 1) and healthy cases (n = 50, Group 2) were enrolled. We retrieved platelet, platelet-associated markers (plateletcrit (PCT), mean platelet volume (MPV), platelet distribution width (PDW)), neutrophil/lymphocyte ratio-NLR, platelet/lymphocyte ratio-PLR, lipids (total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), triglycerides), proteins (total protein (TP) and albumin), and C-reactive protein (CRP) from electronic records and compared the data from lung cancer patients with those from healthy controls. Platelet, PCT, neutrophil, NLR, PLR, triglycerides, VLDL, and CRP levels were significantly higher in Group 1 compared with Group 2. MPV, lymphocyte, albumin, and HDL levels were significantly lower in Group 1 compared with Group 2. No significant relationship was evident between histopathological types and the level of any marker. Compared to those with early-stage cancer, changes in marker levels in those with advanced-stage cancer were statistically significant. CRP and NLR were significantly higher; albumin and HDL were lower in metastatic patients. We found that platelet, PCT, NLR and PLR, albumin, HDL, and CRP levels aided in lung cancer diagnosis and the detection of late-stage disease. Furthermore, these inflammatory and biological markers are thought to be particularly useful in following the severity of lung cancer. Full article
(This article belongs to the Special Issue Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis)
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Review

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20 pages, 287 KiB  
Review
Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?
by Fiorella Calabrese, Francesca Lunardi, Federica Pezzuto, Francesco Fortarezza, Stefania Edith Vuljan, Charles Marquette and Paul Hofman
J. Clin. Med. 2019, 8(3), 414; https://doi.org/10.3390/jcm8030414 - 26 Mar 2019
Cited by 30 | Viewed by 4694
Abstract
Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as [...] Read more.
Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for “noninvasive” liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall. Full article
(This article belongs to the Special Issue Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis)
8 pages, 246 KiB  
Review
Sensors for Lung Cancer Diagnosis
by Rosamaria Capuano, Alexandro Catini, Roberto Paolesse and Corrado Di Natale
J. Clin. Med. 2019, 8(2), 235; https://doi.org/10.3390/jcm8020235 - 11 Feb 2019
Cited by 34 | Viewed by 3845
Abstract
The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population [...] Read more.
The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population screening, and then, it is strongly related to non-invasiveness and low cost. Sensor technology takes advantage of the microelectronics revolution, and then, it promises to develop devices sufficiently sensitive to detect lung cancer biomarkers. A number of biosensors for the detection of cancer-related proteins have been demonstrated in recent years. At the same time, the interest is growing towards the analysis of volatile metabolites that could be measured directly from the breath. In this paper, a review of the state-of-the-art of biosensors and volatile compound sensors is presented. Full article
(This article belongs to the Special Issue Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis)
14 pages, 1200 KiB  
Review
Deciphering the Molecular Profile of Lung Cancer: New Strategies for the Early Detection and Prognostic Stratification
by Elisa Dama, Valentina Melocchi, Tommaso Colangelo, Roberto Cuttano and Fabrizio Bianchi
J. Clin. Med. 2019, 8(1), 108; https://doi.org/10.3390/jcm8010108 - 17 Jan 2019
Cited by 8 | Viewed by 5395
Abstract
Recent advances in radiological imaging and genomic analysis are profoundly changing the way to manage lung cancer patients. Screening programs which couple lung cancer risk prediction models and low-dose computed tomography (LDCT) recently showed their effectiveness in the early diagnosis of lung tumors. [...] Read more.
Recent advances in radiological imaging and genomic analysis are profoundly changing the way to manage lung cancer patients. Screening programs which couple lung cancer risk prediction models and low-dose computed tomography (LDCT) recently showed their effectiveness in the early diagnosis of lung tumors. In addition, the emerging field of radiomics is revolutionizing the approach to handle medical images, i.e., from a “simple” visual inspection to a high-throughput analysis of hundreds of quantitative features of images which can predict prognosis and therapy response. Yet, with the advent of next-generation sequencing (NGS) and the establishment of large genomic consortia, the whole mutational and transcriptomic profile of lung cancer has been unveiled and made publicly available via web services interfaces. This has tremendously accelerated the discovery of actionable mutations, as well as the identification of cancer biomarkers, which are pivotal for development of personalized targeted therapies. In this review, we will describe recent advances in cancer biomarkers discovery for early diagnosis, prognosis, and prediction of chemotherapy response. Full article
(This article belongs to the Special Issue Big Data and Precision Medicine Series I: Lung Cancer Early Diagnosis)
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