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. 2020 Apr 24;10:6996. doi: 10.1038/s41598-020-63810-1

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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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(a) Statistics-based feature selection process based on repeatability and sensitivity to treatment. (b) Heatmap showing all features (y-axis) on days 1, 3, 7 and 10 (D01-D10) – features are measured as percent change from baseline. Left is responder (LS174T) group and right is non-responder (CT26) group, with both treated (T) and control (C). (c) Same as D), but after feature selection. Note that within the responder group, there is an oscillation between treated (T) and control (C) animals from day 1 onwards, not observed in non-responder group. (d) ROC for conventional parameters alone (PE, AUC, TP, MTT – thin lines), combined in an LDA model (ROI-LDA; blue) and top 2 components from PCA in an LDA model (PCA-LDA2; red), in the training and test data set. (e) Correlations to histology of conventional parameters (top), LDA scores from conventional parameters (ROI-LDA; top), the first component from the PCA (bottom), and the LDA scores from top 16 PCA components (PCA-LDA1) and top 2 PCA components (PCA-LDA2) (bottom). These are shown as absolute measurements, and not percent change from baseline.