Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence
A special issue of Current Oncology (ISSN 1718-7729). This special issue belongs to the section "Medical Oncology".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 429
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
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. Current Oncology is an international peer-reviewed open access monthly 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 2200 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
- screening
- prevention
- detection
- staging
- body composition
- computer-aided diagnosis
- PET
- CT
- MRI
- mammography
- artificial intelligence
- machine learning
- deep learning
- diagnostic radiology
- cancers
