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 1556
Special Issue Editor
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
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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
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