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. 2020 Aug 7;10:13378. doi: 10.1038/s41598-020-70125-8

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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One-year survival projection space segmentation: initial 1-year survival distribution (a), projection space division using square cells and survival probability estimation per cell (b), resulting projection space division using cell survival probability distribution (c), novel patient data projection (d). Each point represents an individual patient. The projection space is divided in a square grid (b) with each cell having a specific survival rate computed based on patients belonging to that cell (which have either survived or deceased within the year). The overall space is divided in three zones (c); the survival rate for each zone is calculated using patients belonging to each zone. Novel patient data is projected into the reduced space and prognosis is estimated based on projection coordinates (d). Axes are dimensionless and come from UMAP dimension reduction.