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The Genetic Analysis and Clinical Therapy in Lung Cancer: 2nd Edition

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1589

Special Issue Editors

Dr. Francesca Colombo

E-Mail Website
Guest Editor
Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via F.lli Cervi 93, Segrate, Milano, Italy
Interests: lung cancer; survival; GWAS; germline variants; immunotherapy; pharmacogenomics
Special Issues, Collections and Topics in MDPI journals
Dr. Frullanti Elisa

E-Mail Website
Guest Editor
Department of Medical Biotechnologies, University of Siena, Siena, Italy
Interests: cancer genetics; tumor progression; deep-next generation sequencing; liquid biopsy; circulating tumor DNA; personalized therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of a previous one on the topic of "The Genetic Analysis and Clinical Therapy in Lung Cancer” (https://www.mdpi.com/journal/cancers/special_issues/QWODF46B7Y).

While smoking stands as the predominant risk factor for lung cancer, genetics also play a significant role in predisposing patients to this malignancy, with germline variations potentially influencing disease progression. Over recent decades, molecular profiling has emerged as a critical tool for stratifying patients based on specific somatic mutations, enabling the utilization of multiple targeted therapies. The introduction of immunotherapy has marked a revolution in lung cancer treatment, substantially enhancing patient outcomes. However, disease progression, resistance to targeted drugs, and a lack of responsiveness to immunotherapy remain major challenges in NSCLC treatment, which still has low survival rates.

Our Special Issue seeks to unravel the intricate genetic aspects of this complex disease, exploring somatic mutations, biomarkers, germline variants, and the molecular mechanisms that drive lung cancer. We aim to bridge the gap between scientific discovery and clinical practice, with a particular focus on personalized medicine approaches tailored to individual patients. This Special Issue also aims to explore therapeutic innovations, from targeted therapies to immunotherapies, as well as effective strategies to monitor or predict cancer resistance, with the goal of improving patient outcomes and quality of life.

We eagerly await your upcoming scientific contributions, which will be published in Cancers (ISSN: 2072-6694; impact factor 5.2), as we work towards a future where lung cancer is better understood and more effectively treated.

In this Special Issue, original research articles and reviews are welcome.

Dr. Francesca Colombo
Dr. Frullanti Elisa
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

  • lung cancer
  • biomarkers
  • genetic variants
  • innovative technologies
  • targeted therapy
  • immunotherapy
  • personalized therapy
  • survival
  • liquid biopsy pharmacogenomics

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Published Papers (1 paper)

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Review

31 pages, 1529 KB  
Review
Artificial Intelligence-Enhanced Liquid Biopsy and Radiomics in Early-Stage Lung Cancer Detection: A Precision Oncology Paradigm
by Swathi Priya Cherukuri, Anmolpreet Kaur, Bipasha Goyal, Hanisha Reddy Kukunoor, Areesh Fatima Sahito, Pratyush Sachdeva, Gayathri Yerrapragada, Poonguzhali Elangovan, Mohammed Naveed Shariff, Thangeswaran Natarajan, Jayarajasekaran Janarthanan, Samuel Richard, Shakthidevi Pallikaranai Venkatesaprasath, Shiva Sankari Karuppiah, Vivek N. Iyer, Scott A. Helgeson and Shivaram P. Arunachalam
Cancers 2025, 17(19), 3165; https://doi.org/10.3390/cancers17193165 - 29 Sep 2025
Cited by 1 | Viewed by 1463
Abstract
Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and [...] Read more.
Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice. Full article
(This article belongs to the Special Issue The Genetic Analysis and Clinical Therapy in Lung Cancer: 2nd Edition)
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