Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Imaging and Molecular Biology as Biomarkers for Lung Cancer
share announcement

Imaging and Molecular Biology as Biomarkers for Lung Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3279

Special Issue Editors

Prof. Dr. Salvatore Cappabianca

E-Mail Website
Guest Editor
Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
Interests: abdominal radiology; thoracic imaging; interventional radiology; radiation oncology; radiobiology; contrast media; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Umberto Malapelle

E-Mail Website
Guest Editor
Department of Public Health, University of Naples Federico II, Naples, Italy
Interests: predictive molecular pathology in solid tumors; next generation technologies in molecular biology; liquid biopsies; predictive oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

In the last decade, several developments and discoveries have completely changed the landscape of lung cancer management. More specifically, the knowledge of the genetic profile as well as the improvements in both diagnosis and therapies, often in a multimodal approach, have improved the prognosis of lung cancer patients. Despite that, lung cancer still represents an unfavorable malignancy, with only about one-fifth of patients still alive five years after diagnosis.

In this context, precision medicine is recognized as an approach that could provide far more tailored treatments, considering the characteristics that are unique for the patient. This concept in the field of cancer is known as precision oncology and requires the molecular profiling of tumors but can be used also in the context of imaging (both Radiology and Radiation Oncology) with the image-guided precision medicine that involves different imaging to evaluate and perform different interventions, including radiomics and artificial intelligence approaches. Molecular Biology and Imaging can be also reciprocally linked, with radiogenomics aiming to correlate imaging to genetic characteristics.

These two components are pivotal in the field of lung cancer, as both can provide useful biomarkers to improve the therapeutic ratio of lung cancer patients in all the stages of disease.

Therefore, this Special Issue will focus on both the components of precision oncology (imaging and molecular biology).  For this Special Issue, we welcome basic translational and clinical research papers, cancer biomarkers, professional opinions and reviews investigating the broad role of Molecular Biology and Imaging in the Clinical Management of Lung Cancer.

Prof. Dr. Salvatore Cappabianca
Dr. Umberto Malapelle
Guest Editors

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. Cancers 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 2900 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

  • radiomics
  • molecular biology
  • biomarkers
  • lung cancer
  • NSCLC
  • radiogenomics
  • precision medicine

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

13 pages, 1873 KiB  
Article
Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer
by Fabian Christopher Laqua, Piotr Woznicki, Thorsten A. Bley, Mirjam Schöneck, Miriam Rinneburger, Mathilda Weisthoff, Matthias Schmidt, Thorsten Persigehl, Andra-Iza Iuga and Bettina Baeßler
Cancers 2023, 15(10), 2850; https://doi.org/10.3390/cancers15102850 - 21 May 2023
Cited by 3 | Viewed by 1475
Abstract
Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study [...] Read more.
Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer. Full article
(This article belongs to the Special Issue Imaging and Molecular Biology as Biomarkers for Lung Cancer)
►▼ Show Figures

Figure 1

Other

Jump to: Research

15 pages, 1322 KiB  
Systematic Review
Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis
by Ting-Wei Wang, Ming-Sheng Hsu, Yi-Hui Lin, Hwa-Yen Chiu, Heng-Sheng Chao, Chien-Yi Liao, Chia-Feng Lu, Yu-Te Wu, Jing-Wen Huang and Yuh-Min Chen
Cancers 2023, 15(14), 3542; https://doi.org/10.3390/cancers15143542 - 08 Jul 2023
Cited by 2 | Viewed by 1351
Abstract
In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was [...] Read more.
In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87–4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell’s concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC. Full article
(This article belongs to the Special Issue Imaging and Molecular Biology as Biomarkers for Lung Cancer)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop