Artificial Intelligence and Machine Learning in Cancer Diagnosis, Treatment, and Prognosis
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 1012
Special Issue Editor
Interests: artificial intelligence; machine learning; hepatobiliary and transplantation surgery; optical coherence tomography
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) are redefining cancer research by enabling earlier detection, more accurate diagnosis, and increasingly personalized treatment strategies. As cancer rates continue to rise worldwide, the ability of AI systems to analyze complex imaging, molecular, and clinical datasets at scale offers unprecedented opportunities to accelerate discovery and improve outcomes across the cancer care continuum.
Recent advances in deep learning architectures, specialized computing hardware, and multimodal datasets have driven rapid progress. Convolutional neural networks have achieved high accuracy in tumor detection, segmentation, and grading across radiology and digital pathology. Transformers now facilitate sophisticated analyses of genomic sequences, clinical narratives, and integrated datasets, while generative adversarial networks support data augmentation and modeling of rare cancer presentations. Collectively, these methods are enhancing early cancer detection, reducing diagnostic variability, and improving efficiency in clinical workflows.
AI is also reshaping precision oncology. Multimodal AI models that integrate imaging, histopathology, genomics, and clinical data enable more refined prognostic assessment, biomarker discovery, and therapy response prediction. AI-driven tools are increasingly informing personalized treatment planning, supporting real-time disease monitoring, and accelerating drug discovery through automated target identification and candidate screening.
At the same time, emerging infrastructures such as federated learning and edge computing are addressing key challenges related to data privacy and scalability by enabling decentralized model training. Yet significant barriers remain, including data quality limitations, annotation burden, model interpretability, regulatory concerns, and inequitable access to AI technologies. Overcoming these challenges is essential for responsible clinical translation.
This Special Issue invites original research articles and comprehensive reviews that investigate, develop, or critically assess AI and ML applications in cancer research. Topics of interest include, but are not limited to, the following:
- Early cancer detection, including screening algorithms and radiomic or liquid biopsy-based approaches;
- Diagnostic support systems that leverage imaging, pathology, or multimodal datasets;
- Prognostic and predictive modeling for risk stratification, treatment response, and survival outcomes;
- AI-driven treatment planning, decision support tools, and personalized therapeutic strategies;
- Methodological innovations, such as multimodal learning, explainable AI, integration of large-scale omics data, federated learning, or real-world validation frameworks.
By showcasing diverse perspectives and cutting-edge methodologies, this Special Issue aims to stimulate meaningful dialogue within the oncology and data science communities. Ultimately, it seeks to highlight where AI and ML can most effectively contribute to advancing cancer research and improving patient outcomes in the years ahead.
Priv.-Doz. Iakovos Amygdalos MBBS BSc PhD
Guest Editor
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-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
- artificial intelligence in oncology and oncological surgery
- machine learning for cancer diagnosis
- multimodal data integration
- precision oncology and prognostic modeling
- federated and explainable AI
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