Cancer development and management is influenced by a broad range of factors. Addressing these requires a holistic approach with patient-centered, evidence-based cancer care. The socioeconomic and psychologic burdens in both cancer survivors and caregivers have posed grievously far-reaching consequences to the community well-being. In the modern era of precision medicine, there is an imminent demand for advanced technologies for effective cancer risk stratification and personalized treatment regimen customization. Concerted efforts have been made to exploit potential roles of informatics and artificial intelligence (AI) in holistic integrative cancer care. Specifically, significant advances in powerful deep architectures, sophisticated module designs, and the open-sourcing of large labeled cancer datasets make AI increasingly promote cancer prevention, screening, early diagnosis, and precision treatment. However, many barriers impede clinical utilization of AI technologies, such as: (i) lack of accessible, fully-labeled, large-cohort multi-institutional quality data for technology development, (ii) inadequacy of assessment and validation of developed technologies in real world scenarios, (iii) insufficiency of technical strategies for handling complex and multi-dimensional data, and (iiii) inefficient integration strategies of multi-modality data. The special issue welcomes influential and state-of-art research and review articles about informatics and AI in cancer prevention, detection, and treatment, as well as palliative care, survivorship, and other topics of interest. Potential topics include, but are not limited to:
- AI-based techniques for cancer detection, diagnosis, treatment, and management
- AI-based framework and architecture for cancer treatment outcome prediction, patient stratification and clinical decision making
- Real-world applications of AI for clinical evaluation and validation of informatics, radiology, and histopathology
- Technical advances in analysis of informatics, data multi-source heterogeneity, unlabeled or partially labelled data, and multi-modality data integration strategy.
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Guest Editors:
Jing Cai
Professor, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Email: jing.cai@polyu.edu.hk
Interests: radiation therapy; medical physics; medical imaging and image processing; artificial intelligence; big data; cancer diagnosis and therapy; intelligent wearables; aging
Feng-ming Spring KONG
Professor, Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
Email: kong0001@hku.hk
Interests: image-guided and molecular marker-guided precision adaptive radiation therapy; gene therapy for tumor control; survival and radiation-induced normal tissue toxicity; predictive biomarkers for survival and treatment side effects of radiation therapy; modern stereotactic radiation technology to modulate immunofunction
Zhenyu Liu
Professor, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Email: zhenyu.liu@ia.ac.cn
Interests: medical image analysis; artificial intelligence; radiomics