Collection

Applications of Artificial Intelligence in Cancer

Submission status: Open
Submission deadline: 30 December 2024

Artificial intelligence (AI) is revolutionising science and healthcare, and cancer research is no exception. Multiple AI approaches have been used for cancer diagnosis and prognosis, drug target prediction, and the analysis of tumour composition from multimodal data, amongst many other tasks.

With this cross-journal Collection, the editors at Nature Communications, npj Digital Medicine, npj Precision Oncology, Communications Biology, Communications Medicine, and Scientific Reports invite submissions with a focus on AI in cancer. We welcome papers covering recent advances in the development and application of AI techniques – including machine learning and deep learning methods – with the purpose of deciphering cancer biology, improving diagnosis, prognosis and treatment, and leveraging the vast amounts of available datasets for the benefit of cancer patients.

This Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

To submit, see the participating journals

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Supervised deep learning models hold promise for the interpretation of histology images, but are limited by cost and quality of training datasets. Here, the authors develop a self-supervised deep learning method that can automatically discover features in cancer histology images that are associated with diagnosis, survival, and molecular phenotypes.
- Adalberto Claudio Quiros
- Nicolas Coudray
- Ke Yuan
ArticleOpen Access11 Jun 2024 Nature Communications
Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer
- Anuraj Nayarisseri
- Mohnad Abdalla
- Sanjeev Kumar Singh
ArticleOpen Access10 Jun 2024 Scientific Reports
Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma
- Chi T. Viet
- Kesava R. Asam
- Bradley E. Aouizerat
ArticleOpen Access8 Jun 2024 npj Precision Oncology
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning

Sahlsten et al. systematically evaluate two Bayesian deep learning methods and eight uncertainty measures for the segmentation of oropharyngeal cancer primary gross tumor volume with a multi-institute PET/CT dataset. The uncertainty-aware approach can accurately predict the segmentation quality that enables automatic segmentation quality control.
- Jaakko Sahlsten
- Joel Jaskari
- Kimmo Kaski
ArticleOpen Access8 Jun 2024 Communications Medicine
Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer
- Ching-Wei Wang
- Hikam Muzakky
- Tai-Kuang Chao
ArticleOpen Access29 May 2024 npj Digital Medicine
Melvin is a conversational voice interface for cancer genomics data

Melvin is a multi-modal Amazon Alexa skill that allows users to quickly explore cancer genomics data from TCGA through simple conversations.
- Akila R. Perera
- Vinay Warrier
- Jason J. Pitt
CommentOpen Access5 Jan 2024 Communications Biology

Applications of Artificial Intelligence in Cancer

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning

Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer

Melvin is a conversational voice interface for cancer genomics data

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