AI in precision oncology
- Submission status
- Open
- Submission deadline
The fields of cancer research and precision oncology are undergoing a massive transformation due to the application of artificial intelligence (AI). AI has enabled the detection of hidden patterns from multiple sources of information, including molecular profiling, pathology, and medical imaging, among others, as well as the integration of -omics data to provide a more comprehensive understanding of cancer. AI has also spurred the development of new assays for characterising cancer, prognostication, and predicting responses to specific treatments. These advances in tailoring treatment to the unique characteristics of a patient's cancer are a significant breakthrough. Despite the many opportunities that AI offers, challenges arise when translating these new tools from research settings to clinical practice.
The purpose of this Collection is to disseminate the most recent research and advancements in all facets of AI in cancer research, including basic, translational, and clinical studies. Additionally, the Collection seeks to provide a comprehensive review of the current applications of AI in precision oncology and offer expert insights on how to expedite AI tools from the laboratory to the clinic, with the ultimate goal of improving patient care. The Collection will prioritise articles which are using innovative methods, address a relevant real-world problem and at the same time provide high-quality evidence using multicentric datasets.
The topics will include, but are not limited to:
- Prognostic and predictive biomarkers in cancer
- Molecular profiling (genomics, transcriptomics, proteomics)
- Digital pathology
- Medical imaging
- Real-world data analysis
- Multimodal data integration
- Novel clinical trial designs
This is a joint collection between npj Precision Oncology and npj Breast Cancer. Topics of particular interest for npj Breast Cancer include:
- AI systems for breast cancer diagnosis
- AI systems for lymph node metastasis detection
- Automation of the assessment of immunohistochemical biomarkers
- HER2 and HER2-Low assessment
- Tumour infiltrating lymphocytes and tumour microenvironment
- Inference of breast cancer biomarkers from H&E-stained slides
- Multimodal biomarkers
This Collection supports and amplifies research related to SDG 3.
Editors
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Raquel Perez-Lopez
Team Leader, Radiomics Group, Vall d´Hebron Institute of Oncology (VHIO), Barcelona, Spain
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Jorge S. Reis-Filho
Director, Experimental Pathology and of the Experimental Pathology Fellowship Program Affiliate Member, Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
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Jakob Nikolas Kather
Professor, Technical University of Dresden, Dresden, Germany
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Fiona Kolbinger
Surgical clinician scientist, National Center for Tumor Diseases, University Hospital Else Kroener Fresenius Center for Digital Health in Dresden, Dresden, Germany
