KSC 2017
Current Result Document : 2 / 2
| 한글제목(Korean Title) |
항암치료반응 예측을 위한 PET 영상 기반 기계학습 분류 알고리즘 비교 연구 |
| 영문제목(English Title) |
Comparison of machine learning classification algorithms for prediction of chemotherapy response in PET image |
| 저자(Author) |
김욱
박지수
박용성
이용진
변병현
공창배
김병일
임상무
우상근
Wook Kim
Jisu Park
Yong Sung Park
Yong Jin Lee
Byung Hyun Byun
Chang-Bae Kong
Byung Il Kim
Sang Moo Lim
Sang-Keun Woo
|
| 원문수록처(Citation) |
VOL 44 NO. 02 PP. 0998 ~ 1000 (2017. 12) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
18F-FDG PET image is widely used in nuclear medicine to diagnosis cancer patients. Especially, texture analysis of 18F-FDG PET image has a potential of predicting the survival rate or investigate the therapeutic response in patients. For this reason, the aim of this study was to predict the chemotherapy response using PET image and machine learning classification algorithms. Machine learning classification algorithms were used linear support vector machine (SVM), radial basis function (RBF) kernel SVM, Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The highest predicting score was 0.958. And highest predict score features were mean of standard uptake value (SUVmean) and SUV standard deviation (SUVsd) and machine learning classification algorithm was NB. Consequently, this result may help to predict the chemotherapy response in osteosarcoma patients.
|
| 키워드(Keyword) |
|
| 파일첨부 |
PDF 다운로드
|
