Artificial Intelligence in Cancer Screening
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 19124
Special Issue Editors
Interests: biomarkers; colorectal cancer; imaging; lung cancer
Interests: artificial intelligence in medicine; biomedical engineering; computational methods; machine and deep learning; medical imaging
Special Issue Information
Dear Colleagues,
Cancer is expected to rank as the leading cause of death worldwide in the 21st century. Screening is possible and recommended for several cancers, including those of the breast, colon and rectum, uterine cervix, and lungs. Screening by enabling the detection and radical cure of early-stage lesions offers the opportunity for a remarkable increase in participant life expectancy. Several screening tools are available, and each screening intervention has its own peculiarities. However, the fundamentals of cancer screening procedures are relatively homogeneous and well encoded, and some unresolved issues, albeit presenting some specificities, are shared.
Artificial intelligence (AI) is a powerful class of methods for decision support, having rapidly evolved over recent years. Several approaches have been developed to analyze the increasingly extensive and variably complex datasets often publicly available. With enthusiasm, AI is beginning to be applied to several facets of screening intervention, from the risk stratification of subjects to be invited, to the reading of radiological, optical, and histo-cytopathological images, and integration with stool, serum, and fluid biomarkers to ultimately promote and reinforce the implementation of screening in public health services, thanks to an improved cost/benefit ratio.
In this Special Issue of Cancers, we offer an overview of the current and potential applications of AI to the screening interventions considering, on the one hand, the AI methodological and technical developments and, on the other hand, the applications to specific screening contexts.
Prof. Dr. Mario Mascalchi
Prof. Dr. Stefano Diciotti
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- biomarkers
- breast cancer
- cancer screening
- cervical cancer
- colorectal cancer
- deep learning
- lung cancer
- machine learning
- risk stratification