Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Pulmonology".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 139
Special Issue Editor
2. Experimental Oncology Deprtment, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia
Interests: solid tumors; genomics; bioinformatics; cancer biomarkers; liquid biopsy
Special Issue Information
Dear Colleagues,
Lung cancer, of which non-small-cell lung cancer is the commonest form, is the leading cause of cancer deaths worldwide. Over the past decade, we have witnessed the fast pace at which technological breakthroughs, especially regarding the development and application of artificial intelligence (AI) and omics methods, combined with non-invasive or minimally invasive medical procedures, have transformed the clinical management of lung cancer to improve patient screening, diagnosis, and therapeutic strategies.
In this Special issue, we aim to highlight the use of AI, machine learning, and omics at various clinical pathway stages, including early detection, diagnosis and subtype classification, the estimation of prognosis, the prediction of treatment response, and clinical trial optimization. The topics of interest for publication include, but are not limited to, the following: pre-clinical research and the clinical implementation of molecular tumor profiling, data mining, natural language processing, the machine-learning-based modeling of both routine clinical data and blood work, as well as radiomics, digital pathology, and omics data from liquid and solid biopsies, addressing current issues and discussing further directions for the implementation of AI and omics technologies for non-small-cell lung cancer clinical workup in order to deliver improved patient care.
Dr. Miljana Tanić
Guest Editor
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Keywords
- lung cancer
- NSCLC
- radiomics
- digital pathology
- electronic health records
- tumor profiling
- AI
- machine learning
- deep learning
- liquid biopsy
- early detection, diagnosis
- prognosis
- therapy
- biomarkers
- management
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