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. 2023 Feb 9;13:2335. doi: 10.1038/s41598-023-28358-w

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© The Author(s) 2023

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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Summary clustering results depicting the ability of imaging indices (top) and shape modes (bottom) to discriminate PVR status. (A) K-means clustering using features with the most significant associations with PVR status with two clusters (blue squares: cluster 1; red triangles: cluster 2; filled: PVR; open: No-PVR). (B) Number of patients in the PVR (dark gray bars) and No-PVR (light gray bars) cohorts in each cluster. (C) Matching matrix and MCC performance metric for classifying patients by PVR status. EF Ejection fraction, ESVi End-systolic volume index, LV Left ventricular, MCC Matthews correlation coefficient, RV Right ventricular.