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. 2018 Apr 19;118(10):1349–1358. doi: 10.1038/s41416-018-0048-3

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© The Author(s) 2018

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. 4 — Validation of the frozen tissue model using fresh tissue. a 2D PCA model of frozen ovarian cancer samples (>50% tumour content) and all normal samples combined as ‘Normal Tissue’. b 3D LDA component analysis. c Leave-one-patient-out cross-validation of the binary ‘Ovarian Cancer’ vs ‘Normal Tissue’ frozen model, showing 100% correct classification. d OMB Recognition software example showing reported classification for individual sampling points (burns) and the probability value associated with the reported class. e Bar chart showing the numbers of sampling points in fresh tissue classified with >75% probability using the OMB recognition software, plus the diagnostic accuracy at those sampling points [OC 100% (61/61), normal ovary 100% (64/64), fallopian tube 100% (58/58), peritoneum 94.1% (32/34)]