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Cancers
Special Issues
Imaging and Molecular Biology as Biomarkers for Lung Cancer
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Special Issue "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 2023 | Viewed by 535

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 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

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

Published Papers (1 paper)

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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
Viewed by 346
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)
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