AI-Driven Oncology: Advancing Cancer Detection, Diagnosis, and Personalized Treatment
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 603
Special Issue Editors
Interests: medical image analysis; artificial intelligence in medicine; deep learning; computer-aided diagnostics; precsion medicine; diagnostics and prognostic markers; bigdata in medicine
Special Issues, Collections and Topics in MDPI journals
2. Faculty of Computer Science & Engineering, New Mansoura University, Gamasa 35712, Egypt
Interests: explainable artificial intelligence (XAI); deep learning; continuous learning systems; risk management; cardiovascular health prediction; computer-aided diagnostics; genetic algorithms; nanotechnology applications; visual impairment assistance systems; congestion prediction algorithms
Special Issue Information
Dear Colleagues,
Recent developments in machine learning (ML) and artificial intelligence (AI) have revolutionized oncology by providing creative ways to improve cancer diagnosis, detection, and individualized care. While early and precise diagnosis is still essential for bettering patient outcomes, conventional methods frequently depend on clinical criteria and a small number of biomarkers, which may not adequately account for the complexity of cancer progression. Large-scale datasets, such as genetic profiles, clinical records, and medical imaging, can be analyzed by AI-driven models to find trends and more accurately forecast treatment outcomes.
The application of AI and ML in oncology has the potential to enhance diagnostic precision, identify novel biomarkers, predict clinical outcomes, and recommend the most effective treatment regimens. This Special Issue will gather state-of-the-art research on the application of AI and ML in cancer detection, diagnosis, prognosis, and treatment planning. Additionally, the capacity of AI to handle multi-omics data, including transcriptomics, proteomics, and genomes, creates new opportunities for the identification of predictive and prognostic biomarkers. For these AI models to be adopted in clinical practice and to build patient and healthcare provider trust, it is vital that they are explainable and interpretable.
We invite submissions that address novel algorithms, models, and frameworks for the following topics:
- Cancer Detection and Diagnosis;
- Predicting Cancer Prognosis and Clinical Outcomes;
- Treatment Response Prediction;
- Integration of Multi-Omics Data;
- Explainability and Interpretability in AI Models.
We welcome original research articles and reviews that explore innovative AI/ML methodologies, real-world applications, and challenges in the field of oncology. Submissions should provide significant contributions to the understanding and development of AI techniques in cancer care. We encourage authors working in diverse disciplines, including but not limited to oncology, data science, bioinformatics, and computer science, to submit their work to this Special Issue. All submissions will be subject to rigorous peer review, and we particularly welcome studies that combine theoretical and practical perspectives, demonstrate clinical relevance, and present real-world applications of AI in cancer care.
Dr. Mohamed Shehata
Guest Editor
Dr. Fatma M. Talaat
Guest Editor Assistant
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 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 2900 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
- cancer detection and diagnosis
- AI in oncology
- explainable AI (XAI)
- precision oncology
- medical imaging
- prognostic biomarkers
- clinical decision support systems
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