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. 2021 Mar 15;11:5950. doi: 10.1038/s41598-021-85276-5

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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(a) Non-invasive and invasive cancer cells showed different, yet heterogeneous, morphologies on non-aligned fibrous templates, and this difference was highlighted by (b) different YAP/TAZ states. (c) A neural network was constructed to classify cells as invasive or non-invasive based on morphology and/or YAP/TAZ levels. (d) Using morphology to classify cells resulted in 80.4% accuracy, while YAP/TAZ predicted cell classification with 90.2% accuracy. Using both morphology and mechanosensing increased accuracy to 95.1%, highlighting the utility of exploiting both morphology and mechanosensing in invasive cancer cells (**** p < 0.001, n_MCF-7 = 177 and n_MDA-MB-231 = 115 cells).