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Molecular Research on Lung Cancer: Translational Perspectives

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2156

Special Issue Editor


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Guest Editor
Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
Interests: obstructive airway diseases; immunology; microbiota

Special Issue Information

Dear Colleagues,

Lung cancer still represents a major global threat in terms of mortality and morbidity. Indeed, the majority of patients are diagnosed with metastatic lung cancer at a delayed stage, and such a delay severely impacts on the patients' prognosis. Nonetheless, the recent discovery of target molecular therapies and immune response modulators has significantly improved the prognosis of patients with advanced-stage disease. The demand for tissue analyses is constantly increasing in order to fully characterize cancer and customize treatment, despite clinicians’ need to limit the invasiveness of diagnostic strategies, especially in frail patients. Easily available biomarkers are now under evaluation to address the urgency of early lung cancer diagnosis and molecular/immunological stratification. Among these, c DNA, extracellular vesicles, and miRNAs have been investigated and, especially when integrated with clinical and radiological data, might represent promising future candidates. For this Special Issue, we invite researchers to contribute either with original research (both in vivo or in vitro studies) or review articles focusing on novel insights on the pathophysiology, treatment options and screening strategies of lung cancer.

  • Viral vectors in cancer treatment;
  • nanoparticles in lung cancer treatment;
  • extracellular vesicles in lung cancer phenotypization;
  • nucleic acids in lung cancer characterization;
  • liquid biopsy in lung cancer;
  • immune check-points in lung cancer;
  • novel molecular targets in non-small-cell lung cancer;
  • noninvasive diagnostic tools in lung cancer.

Dr. Mariaenrica Tinè
Guest Editor

Manuscript Submission Information

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Keywords

  • lung cancer
  • oncogene
  • tumor microenvironment

Published Papers (1 paper)

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Review

10 pages, 1875 KiB  
Review
Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer
by Mehdi Felfli, Yan Liu, Fadila Zerka, Charles Voyton, Alexandre Thinnes, Sebastien Jacques, Antoine Iannessi and Sylvain Bodard
Int. J. Mol. Sci. 2023, 24(14), 11433; https://doi.org/10.3390/ijms241411433 - 14 Jul 2023
Cited by 4 | Viewed by 1768
Abstract
Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT [...] Read more.
Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT radiomics-based models for predicting EGFR mutation status were retrieved. Forest plots and related statistical tests were performed to summarize the model performance and inter-study heterogeneity. The methodological quality of the selected studies was assessed via the Radiomics Quality Score (RQS). The performance of the models was evaluated using the area under the curve (ROC AUC). The range of the Risk RQS across the selected articles varied from 11 to 24, indicating a notable heterogeneity in the quality and methodology of the included studies. The average score was 15.25, which accounted for 42.34% of the maximum possible score. The pooled Area Under the Curve (AUC) value was 0.801, indicating the accuracy of CT radiomics-based models in predicting the EGFR mutation status. CT radiomics-based models show promising results as non-invasive alternatives for predicting EGFR mutation status in NSCLC patients. However, the quality of the studies using CT radiomics-based models varies widely, and further harmonization and prospective validation are needed before the generalization of these models. Full article
(This article belongs to the Special Issue Molecular Research on Lung Cancer: Translational Perspectives)
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