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Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy by Yuta Akamine; Yu Ueda; Yoshiko Ueno; Keitaro Sofue; Takamichi Murakami; Masami Yoneyama; Makoto Obara; Marc Van Cauteren is a Medicine article available to read on EtoBox.

What is Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy about?

Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multiparametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques. Methods: HC was constructed using mp-MR including intravoxel incoherent motion, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI from 40 tumor and normal tissues in peripheral zone (PZ) and 23 tumor and normal tissues in transition zone (TZ). HC model was optimized by assessing the combinations of several dissimilarity and linkage methods. Goodness of HC model was validated by internal methods. Results: Accuracy for differentiating tumor and normal tissue by optimal HC model was 96.3% in PZ and 97.8% in TZ, comparable to current clinical standards. Relationship between input (DWI and permeability parameters) and output (tumor and normal tissue cluster) was shown by heat maps, consistent with literature. Conclusion: HC can accurately differentiate PCa and normal tissue, comparable to state-of-the-art diffusion based parameters. Contrary to DL techniques, HC is an operator-independe

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Author

Yuta Akamine; Yu Ueda; Yoshiko Ueno; Keitaro Sofue; Takamichi Murakami; Masami Yoneyama; Makoto Obara; Marc Van Cauteren

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0730-725X)

Published

2020

Language

EN

Field

Medicine (Health Sciences)