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. 2020 Oct 13;8(2):e001343. doi: 10.1136/jitc-2020-001343

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© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

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Unsupervised clustering analysis: (A) heatmaps for (i) radiomics intratumoral and peritumoral features where 78.9% HPs clustered together in cluster 1, (ii) quantitative vessel tortuosity features. (B) K-mean clustering analysis: (i) elbow curve representing an optimum number of clusters formed using the top three principal components after performing principal component analysis on the entire feature cohort. The optimum number of clusters were observed to be four. (ii) clusters after performing clustering using k=4. These clusters had 78.1% compactness within the clusters. Almost all the HP (80%) were clustered in clusters 3 and 4. Responders and non-responders formed clusters 2 and 1. HP, hyperprogressor.