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. 2022 Jul 21;127(7):1270–1278. doi: 10.1038/s41416-022-01889-1

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© The Author(s) 2022

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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Fig. 1 — a Graphical abstract/overview of the workflow (graphics generated using www.BioRender.com). b Example of the histological measurement of the histological depth of invasion depicting a pT3a case (upper panel) and a pT3b case (lower panel). The measurement was performed from the edge of the muscular layer to the deepest identifiable cancer cell within the adipose tissue. c, d Determination of the Cutoffs used for the pT3a/b subclassification guided by the biostatistical tool Cutoff finder [11]. The upper panel shows the frequency of the depth of adipose tissue invasion, the optimal cut-off is shown by the red line. The lower panel shows the hazard ratio for DSS in dependence of the cut-off point.