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Special Issue "Unlocking the Potential of AI and Big Data in Cancer Research: Advances and Applications"
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".
Deadline for manuscript submissions: 20 December 2023 | Viewed by 1235
Special Issue Editors
2. Department of Translational Medicine, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy
Interests: radiotherapy; artificial intelligence; machine learning; process mining; radiomics
Special Issues, Collections and Topics in MDPI journals
Interests: urological malignancies; radiation oncology; new fractionation protocols; treatment accuracy; patient’s quality of life; prognostic and predictive factors; SBRT hypofractionation; oligometastatic disease
Special Issues, Collections and Topics in MDPI journals
Special Issue in Cancers: The Role of Stereotactic Ablative Radiotherapy in the Management of Localized and Metastatic Genitourinary Tumours
Special Issue in BioMedInformatics: Advances in Quantitative Imaging Analysis: From Theory to Practice
Special Issue Information
AI and big data have the potential to revolutionize cancer research by providing new insights and enabling more accurate diagnoses and treatments supported by an increasing number of data. The sheer volume of data generated by modern medical technology, such as that in the field of omics sciences, presents a unique opportunity to apply advanced machine learning techniques to uncover previously hidden patterns and relationships.
By analyzing large amounts of data from imaging scans and other diagnostic tests, machine learning algorithms can be trained to identify subtle signs of disease that may be missed by human radiologists and pathologists. This can lead to earlier diagnoses and better outcomes for patients.
Another area where AI and big data can play a role is in the development of personalized medicine. Furthermore, by analyzing large amounts of data from patient records and genomic sequencing, AI algorithms can identify patterns and markers that are unique to an individual patient. This information can be used to tailor treatment plans to the specific needs of each patient, leading to more effective and less toxic therapies.
Big data can also be used to improve the understanding of the underlying mechanisms of cancer, which can help in the discovery of new targets for drug development. By analyzing large amounts of data from patient records, genomic sequencing, and preclinical studies, researchers can identify new potential therapeutic targets and biomarkers.
This Special Issue will focus on the latest developments and applications of AI and big data in cancer research, highlighting the potential of these technologies to improve patient outcomes and accelerate the discovery of new treatments.
Potential topics include, but are not limited to:
- The use of AI and machine learning algorithms for the early detection of cancer from imaging scans and diagnostic tests;
- Personalized medicine and precision oncology using AI-driven analysis of patient records and genomic data;
- Identifying new therapeutic targets and biomarkers for cancer using big data analysis;
- AI-assisted drug discovery and development using large-scale data analysis of preclinical studies;
- Predictive modeling and risk assessment of cancer using AI and big data;
- The ethical and societal implications of using AI and big data in cancer research and treatment;
- The use of AI and big data in clinical trials, including patient selection, drug dosing, and trial design;
- Integrating AI and big data with electronic health records (EHRs) to improve patient outcomes and care coordination;
- The use of AI and big data for real-time monitoring and surveillance of cancer patients to detect early signs of recurrence or resistance to treatment;
- Developing and evaluating AI-driven decision support systems for cancer diagnosis and treatment planning.
Dr. Federico Mastroleo
Dr. Giulia Marvaso
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. Cancers is an international peer-reviewed open access semimonthly 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 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.
- artificial intelligence
- machine learning
- data analysis
- data science
- deep learning