Diagnostic Performance of the Support Vector Machine Model for Breast Cancer on Ring-Shaped Dedicated Breast Positron Emission Tomography Images

Yoko Satoh, Daiki Tamada, Yoshie Omiya, Hiroshi Onishi, Utaroh Motosugi

研究成果: ジャーナルへの寄稿学術論文査読

7 被引用数 (Scopus)

抄録

Objective The aim of this study was to evaluate the diagnostic ability of support vector machine (SVM) for early breast cancer (BC) using dedicated breast positron emission tomography (dbPET). Methods We evaluated 116 abnormal fluorodeoxyglucose (FDG) uptakes less than 2 cm on dbPET images in 105 women. Fluorodeoxyglucose uptake patterns and quantitative PET parameters were compared between BC and noncancer groups. Diagnostic accuracy of the SVM model including quantitative parameters was compared with that of visual assessment based on FDG-uptake pattern. Results Age, maximum standardized uptake value, peak standardized uptake value, total lesion glycolysis, metabolic tumor volume, and lesion-to-contralateral background ratio were significantly different between BC and noncancer groups. Area under the curve, sensitivity, specificity, and accuracy for FDG-uptake pattern of visual assessment were 0.77, 0.57, 0.77, and 0.71, respectively; those of an SVM model including age, maximum standardized uptake value, total lesion glycolysis, and lesion-to-contralateral background ratio were 0.89, 0.94, 0.77, and 0.85, respectively. Conclusions Support vector machine showed high diagnostic performance for BC using dbPET.

本文言語英語
ページ(範囲)413-418
ページ数6
ジャーナルJournal of Computer Assisted Tomography
44
3
DOI
出版ステータス出版済み - 2020
外部発表はい

All Science Journal Classification (ASJC) codes

  • 放射線学、核医学およびイメージング

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