Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate ute
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Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate uterine endometrioid carcinoma grade Sakiko Kageyama 1 & Naoko Mori 1
&
Shunji Mugikura 1,2 & Hideki Tokunaga 3 & Kei Takase 1
Received: 27 February 2020 / Revised: 1 May 2020 / Accepted: 26 June 2020 # European Society of Radiology 2020
Abstract Objectives The purpose of our study was to perform Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma, and to evaluate the relationship between histological grade and the ratios of the different clusters in each patient. Methods This retrospective study enrolled 122 patients (training: n = 63; and validation: n = 59) imaged between May 2015 and February 2020. In the training cohort, manual segmentation was performed on the ADC maps to obtain the ADC data of each patient, and these ADC data were summated to obtain the “All-patient” ADC data. Cluster analysis (three clusters) was performed on this All-patient ADC data, and the ADC ranges of each cluster were defined as follows: cluster 1, 490–699 × 10−6 mm2/s; cluster 2, 700–932 × 10−6 mm2/s; and cluster 3, over 933 × 10−6 mm2/s. In the training and validation cohorts, the ADC data of each patient was classified into three clusters according to these ADC ranges. The cluster ratios of each patient were calculated and compared with histological grade. Results In the training cohort, a significant positive correlation was found between the cluster 1 ratio and histological grade (ρ = 0.34, p = 0.0059). The cluster 1 ratios of high-grade lesions (grade 3) were significantly higher than those of low-grade lesions (grades 1 and 2) (p = 0.0084). A similar significant positive correlation was found between the cluster 1 ratio and histological grade in the validation cohort (ρ = 0.44, p = 0.0006). Conclusions The cluster 1 ratio containing voxels with low ADC was significantly correlated with the histological grade of endometrioid carcinoma. Key Points • We performed Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma. • The cluster 1 ratio, which included low ADC values, was significantly positive correlated with histological grade in the training and validation cohorts. • The GMM-based cluster analysis of voxel-based ADC data was effective for grading endometrioid carcinoma. Keywords Uterus . Endometrioid carcinoma . Magnetic resonance imaging . Cluster analysis
* Naoko Mori [email protected]
Abbreviations FIGO The International Federation of Gynecology and Obstetrics GMM Gaussian mixture model ICC Interclass correlation coefficient
1
Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai 980-8574, Japan
2
Department of Image Statistics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo 2-1, Sendai 980-8574, Japan
Introduction
Department of Obst
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