Computational Oncology: Machine Learning, Digital Biomarkers, and Clinical Decision Support
A Special Issue of Cancers (ISSN 2072-6694) belonging to the section "Cancer Informatics and Big Data".
Deadline for manuscript submissions: 20 April 2027 | Viewed by 223
Editors
Interests: computational oncology; artificial intelligence in healthcare; healthcare data analytics; digital biomarkers; clinical decision support systems
Special Issues, Collections and Topics in MDPI journals
Interests: wearable health monitoring; internet of medical things (IoMT); biomedical signal processing; digital health; ambient assisted living
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical analytics; communication in healthcare; explainable artificial intelligence; precision medicine; computer-aided diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence (AI), machine learning, and computational methodologies is transforming oncology and offering unprecedented opportunities to enhance cancer prevention, diagnosis, prognosis, treatment planning, and patient management. The increasing availability of multimodal clinical data, including medical imaging, histopathology, genomics, transcriptomics, electronic health records, wearable sensor data, and real-world evidence, has accelerated the development of intelligent computational models that can extract clinically meaningful insights and support precision oncology.
Recent advances in machine learning and deep learning have enabled the identification of digital biomarkers that support early cancer detection, risk stratification, treatment response prediction, and survival analysis. At the same time, clinical decision support systems are becoming increasingly sophisticated, integrating heterogeneous data sources to help clinicians make personalized, evidence-based treatment decisions. However, significant challenges remain in model interpretability, data heterogeneity, clinical validation, privacy preservation, fairness, and the effective translation of AI-driven solutions into routine clinical practice.
This Special Issue aims to bring together researchers, clinicians, biomedical engineers, computer scientists, and healthcare professionals working at the intersection of computational oncology and intelligent healthcare technologies. We invite original research articles, reviews, and methodological studies that address innovative machine learning algorithms, explainable artificial intelligence, digital biomarkers, radiomics, multi-omics integration, foundation models, federated learning, and clinical decision support systems to advance precision cancer care. By fostering interdisciplinary collaboration, this Special Issue seeks to highlight emerging computational approaches that could accelerate translational cancer research and ultimately improve patient outcomes.
Dr. Akash Kumar Bhoi
Dr. Paolo Barsocchi
Prof. Dr. Victor Hugo C. De Albuquerque
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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- computational oncology
- machine learning
- deep learning
- digital biomarkers
- clinical decision support
- precision oncology
- explainable artificial intelligence
- radiomics
- multi-omics integration
- cancer diagnosis and prognosis
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