Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence
A special issue of Current Oncology (ISSN 1718-7729).
Deadline for manuscript submissions: 30 June 2025 | Viewed by 5994
Special Issue Editors
Interests: radiology; CT; MRI; PET; nuclear medicine; neuroendocrine tumors; liver; pancreas; gastrointestinal; tumor ablation
Special Issues, Collections and Topics in MDPI journals
Interests: abdominal imaging; CT technology; liver imaging; pancreatic cancer: early detection and screening; non-vascular intervention; body composition; CT radiostereometric analysis (CT-RSA)
Special Issue Information
Dear Colleagues,
Early diagnosis plays a crucial role in the effective treatment of cancer, as it significantly improves patient outcomes. Artificial intelligence (AI) methods have emerged as promising tools in the field of medicine, particularly in the early detection of cancer. By leveraging advanced algorithms and machine learning techniques, AI can analyze vast amounts of medical data to identify patterns and markers indicative of cancer. AI-based diagnostic systems can analyze various types of medical imaging, such as mammograms, CT scans, and MRIs, with exceptional accuracy. These systems can detect subtle abnormalities and have the potential to assist radiologists in making more confident and timely diagnoses. Moreover, AI can integrate multiple data sources, including genetic profiles and patient histories, to provide a comprehensive assessment of cancer risk. The use of AI methods in early cancer diagnosis offers several advantages. It can facilitate the identification of cancer at its earliest stages when treatment options are more effective and less invasive. Additionally, AI systems promise to help to reduce diagnostic errors and improve the efficiency of healthcare processes, leading to better patient outcomes and reduced healthcare costs. However, there are challenges to overcome in implementing AI-based cancer diagnosis. Ensuring the privacy and security of patient data, addressing ethical concerns, and integrating AI seamlessly into existing healthcare systems are important considerations.
This Special Issue invites authors to present their findings, comments, and challenging experiences with AI regarding imaging-based early diagnosis as well as imaging biomarkers serving as risk factors and prognostic co-factors in different types of human cancers.
Prof. Dr. Timm Denecke
Dr. Anselm Schulz
Guest Editors
Manuscript Submission Information
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Keywords
- screening
- prevention
- detection
- staging
- body composition
- computer-aided diagnosis
- PET
- CT
- MRI
- mammography
- artificial intelligence
- machine learning
- deep learning
- diagnostic radiology
- cancers
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