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 830

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
Special Issues, Collections and Topics in MDPI journals

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

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. Diagnostics 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

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

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 604
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)
Show Figures

Figure 1

Back to TopTop