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Cancers
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Imaging and Molecular Biology as Biomarkers for Lung Cancer
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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: closed (30 September 2024) | Viewed by 5745

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 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

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Published Papers (3 papers)

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22 pages, 3817 KiB  
Article
Enhancing Immunotherapy Response Prediction in Metastatic Lung Adenocarcinoma: Leveraging Shallow and Deep Learning with CT-Based Radiomics across Single and Multiple Tumor Sites
by Cécile Masson-Grehaigne, Mathilde Lafon, Jean Palussière, Laura Leroy, Benjamin Bonhomme, Eva Jambon, Antoine Italiano, Sophie Cousin and Amandine Crombé
Cancers 2024, 16(13), 2491; https://doi.org/10.3390/cancers16132491 - 8 Jul 2024
Cited by 1 | Viewed by 1150
Abstract
This study aimed to evaluate the potential of pre-treatment CT-based radiomics features (RFs) derived from single and multiple tumor sites, and state-of-the-art machine-learning survival algorithms, in predicting progression-free survival (PFS) for patients with metastatic lung adenocarcinoma (MLUAD) receiving first-line treatment including immune checkpoint [...] Read more.
This study aimed to evaluate the potential of pre-treatment CT-based radiomics features (RFs) derived from single and multiple tumor sites, and state-of-the-art machine-learning survival algorithms, in predicting progression-free survival (PFS) for patients with metastatic lung adenocarcinoma (MLUAD) receiving first-line treatment including immune checkpoint inhibitors (CPIs). To do so, all adults with newly diagnosed MLUAD, pre-treatment contrast-enhanced CT scan, and performance status ≤ 2 who were treated at our cancer center with first-line CPI between November 2016 and November 2022 were included. RFs were extracted from all measurable lesions with a volume ≥ 1 cm3 on the CT scan. To capture intra- and inter-tumor heterogeneity, RFs from the largest tumor of each patient, as well as lowest, highest, and average RF values over all lesions per patient were collected. Intra-patient inter-tumor heterogeneity metrics were calculated to measure the similarity between each patient lesions. After filtering predictors with univariable Cox p < 0.100 and analyzing their correlations, five survival machine-learning algorithms (stepwise Cox regression [SCR], LASSO Cox regression, random survival forests, gradient boosted machine [GBM], and deep learning [Deepsurv]) were trained in 100-times repeated 5-fold cross-validation (rCV) to predict PFS on three inputs: (i) clinicopathological variables, (ii) all radiomics-based and clinicopathological (full input), and (iii) uncorrelated radiomics-based and clinicopathological variables (uncorrelated input). The Models’ performances were evaluated using the concordance index (c-index). Overall, 140 patients were included (median age: 62.5 years, 36.4% women). In rCV, the highest c-index was reached with Deepsurv (c-index = 0.631, 95%CI = 0.625–0.647), followed by GBM (c-index = 0.603, 95%CI = 0.557–0.646), significantly outperforming standard SCR whatever its input (c-index range: 0.560–0.570, all p < 0.0001). Thus, single- and multi-site pre-treatment radiomics data provide valuable prognostic information for predicting PFS in MLUAD patients undergoing first-line CPI treatment when analyzed with advanced machine-learning survival algorithms. Full article
(This article belongs to the Special Issue Imaging and Molecular Biology as Biomarkers for Lung Cancer)
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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 6 | Viewed by 2048
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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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 - 8 Jul 2023
Cited by 3 | Viewed by 1860
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)
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