Lung Cancer: Screening, Diagnosis and Management: 2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1466

Special Issue Editor

1. Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
2. School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
Interests: lung cancer; diagnosis; prognosis; radiomics; texture analysis
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Special Issue Information

Dear Colleagues,

With the growing usage and popularization of low-dose computed tomography (LDCT) for lung cancer screening, the number of smoking- and non-smoking-related lung cancer patients worldwide manifesting with subsolid nodules has increased, especially in Asian populations. This increases the survival rate of lung cancer populations. However, there are still some dilemmas in the clinical application, screening, diagnosis, and management of subsolid nodules.

Screening

With the rising utilization of low-dose pulmonary computed tomography in Asian non-smoking populations, the diagnosis of early lung cancer is facing two problems: overdiagnosis and delayed diagnosis. Therefore, how to optimize the efficiency of the lung cancer screening process using clinical decision sharing, public health education, follow-up guidelines with adherence rates, and artificial intelligence assistance is an important issue in early lung cancer screening.

Diagnosis

In recent years, due to the rapid development of artificial intelligence technology, the clinical use of computer artificial intelligence software to assist radiologists in diagnosis has become an important clinical issue. Due to the rapid increase in the number of imaging examinations, radiologists are overworked and faced with burnout, from which the risk of clinical misdiagnosis is increased. Computer-assisted software may reduce the workload of radiologists. However, the impact of artificial intelligence software on the clinical lung cancer diagnosis process still needs clinical verification in the real world. In addition, the use of radiomics to assist the pre-operative diagnosis and growth trend assessment of early lung cancer has also become the focus of current international research.

Management

In recent years, due to the advancement in thoracoscopic surgery technology, there are more alternative surgical options for early lung cancer treatment/management. Therefore, applying radiologic imaging criteria to select appropriate lung cancer surgical methods such as wedge resection, lobectomy, or segmentectomy will reduce the harm caused by surgery in lung cancer patients without affecting the survival rate of lung cancer. In addition, how to apply robotic surgery in the treatment of early lung cancer is also a major focus of early lung cancer research.

Paper recruitment

Therefore, the improvement in the early diagnosis of lung cancer is an important clinical challenge. Papers submitted to this Special Issue should focus on lung cancer screening, diagnosis, clinical management, and the application of artificial intelligence in lung cancer screening with prognostic outcomes. In this Special Issue, research on clinical and translational aspects, as well as original articles, review articles, and case reports, should allow insights into current knowledge and gain further perspectives on the early diagnosis of lung cancer alongside its management.

Dr. Fu-Zong Wu
Guest Editor

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Keywords

  • lung cancer
  • LDCT
  • overdiagnosis
  • artificial intelligence
  • deep learning
  • prognosis
  • radiomics
  • texture analysis
  • thoracic surgery

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Published Papers (2 papers)

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Review

17 pages, 1304 KiB  
Review
Treatment Strategies for First-Line PD-L1-Unselected Advanced NSCLC: A Comparative Review of Immunotherapy-Based Regimens by PD-L1 Expression and Clinical Indication
by Blerina Resuli, Diego Kauffmann-Guerrero, Maria Nieves Arredondo Lasso, Jürgen Behr and Amanda Tufman
Diagnostics 2025, 15(15), 1937; https://doi.org/10.3390/diagnostics15151937 - 31 Jul 2025
Viewed by 358
Abstract
Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between [...] Read more.
Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between October 2016 to September 2024. Phase II and III randomized trials evaluating ICI monotherapy, ICI–chemotherapy combinations, and dual ICI regimens in patients with advanced NSCLC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Results: PD-1-targeted therapies demonstrated superior OS compared to PD-L1-based regimens, with cemiplimab monotherapyranking highest for OS benefit (posterior probability: 90%), followed by sintilimab plus platinum-based chemotherapy (PBC) and pemetrexed—PBC. PFS atezolizumab plus bevacizumab and PBC, and camrelizumab plus PBC were the most effective regimens. ICI–chemotherapy combinations achieved higher ORRs but were associated with greater toxicity. The most favorable safety profiles were observed with cemiplimab, nivolumab, and avelumab monotherapy, while atezolizumab plus PBC and sugemalimab plus PBC carried the highest toxicity burdens. Conclusions: In PD-L1-unselected advanced NSCLC, PD-1 blockade—particularly cemiplimab monotherapy—and rationally designed ICI–chemotherapy combinations represent the most efficacious treatment strategies. Balancing efficacy with safety remains critical, especially in the absence of predictive biomarkers. These findings support a patient-tailored approach to immunotherapy and highlight the need for further biomarker-driven and real-world investigations to optimize treatment selection. Full article
(This article belongs to the Special Issue Lung Cancer: Screening, Diagnosis and Management: 2nd Edition)
20 pages, 1610 KiB  
Review
Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection—Precision Screening for Lung Cancer
by Hsin-Hung Chen, Yun-Ju Wu and Fu-Zong Wu
Diagnostics 2025, 15(12), 1562; https://doi.org/10.3390/diagnostics15121562 - 19 Jun 2025
Viewed by 840
Abstract
Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and [...] Read more.
Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and smoking history. This can lead to limitations, such as overdiagnosis, false positives, and the underrepresentation of non-smokers, which are especially prevalent in Asian populations. Precision medicine offers a transformative solution by tailoring screening protocols to individual risk profiles through the integration of clinical, genetic, environmental, and radiological data. Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. Polygenic risk scores (PRSs) and molecular biomarkers further refine stratification, enabling more personalized and effective screening intervals. Incorporating these innovations into clinical workflows, alongside shared decision-making (SDM) and robust data infrastructure, represents a paradigm shift in lung cancer prevention. However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. This review explores the current landscape and future directions of precision medicine in lung cancer screening, emphasizing the need for interdisciplinary collaboration and population-specific strategies to realize its full potential in reducing the global burden of lung cancer. Full article
(This article belongs to the Special Issue Lung Cancer: Screening, Diagnosis and Management: 2nd Edition)
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