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Based on findings from:
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MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells
Stefan Hinz, et al.. eBioMedicine, 2026.
DOI: doi:10.1016/j.ebiom.2026.106241
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scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution
Tao Ren, et al.. Cancer Discovery, 2026.
DOI: doi:10.1158/2159-8290.CD-25-0965
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Artificial intelligence-based pathological model for pan-cancer lymph node metastasis detection: a multicentre diagnostic study with retrospective and prospective validation
Shaoxu Wu, et al.. The Lancet Digital Health, 2026.
DOI: doi:10.1016/j.landig.2025.100961
Weekly News Brief: May 18–22, 2026
Catch up on news from this past week by listening to our news brief for the week of May 18–22, 2026.
This week’s brief covers a machine learning platform that predicts breast cancer risk from cellular mechanical properties, a new framework for linking cancer survival outcomes to individual tumor cells, and a pan-cancer AI model that detects lymph node metastases missed by pathologists.
To learn more about MechanoAge, read "Machine Learning Predicts Breast Cancer Risk Based on Cellular Mechanical Phenotypes," or see the source report in eBioMedicine.
For more information on scSurvival, read "Bringing Cancer Survival Modeling to Single-Cell Resolution," or see the source report in Cancer Discovery.
To learn more about PanCAM, read "PanCAM Pathological Model Improves Detection of Lymph Node Metastasis," or see the source report in The Lancet Digital Health.
Disclaimer: This newscast was generated with the assistance of AI tools and avatars. All content is reviewed and approved by the editorial staff of ASCO AI in Oncology. Contact us with any questions.
The ideas and opinions expressed in ASCO AI in Oncology do not necessarily reflect those of Conexiant or ASCO. The mention of any company, product, service, or therapy should not be construed as an endorsement of any kind. Conexiant and ASCO assume no responsibility for any injury or damage to persons or property arising out of or related to any use of material contained in this publication or to any errors or omissions.