机构：[1]Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China四川大学华西医院[2]Key Laboratory of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, China四川大学华西医院[3]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China四川大学华西医院[4]Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China[5]Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang, China医技科室病理科河北医科大学第四医院[6]National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China[7]National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Shenzhen 518116, China北京大学深圳医院深圳市南山区人民医院深圳医学信息中心中国医学科学院肿瘤医院深圳医院
Background and Objective:: Chemotherapy is useful to many breast cancer patients, however, it is not
therapeutic for some patients. Pathologic complete response (pCR) is an indicator to good response in
Neoadjuvant chemotherapy (NAC). In this study, we aimed to develop a way to predict pCR before NAC.
Methods:: We retrospectively collected 287 stage II-III breast cancer cases either to a training set
(N = 197) or to a test set (N = 90). Fourteen candidate genes were selected from four public microarray data sets. A prediction model was built, by using these fourteen candidate genes and three reference
genes expression which were tested by TaqMan probe-based quantitative polymerase chain reaction, after
selecting a better algorithm.
Results:: The Naive Bayes algorithm had a relatively higher predictive value, compared with random
forest, support vector machine (SVM), and k-nearest neighbor (knn) algorithms (P < 0.05). This 17-gene
prediction model showed a high positive correlation with pCR (odds ratio, 8.914, 95% confidence interval,
4.430–17.934, P < 0.001). By using this model, the enrolled patients were classified into sensitive (SE)
and insensitive (INS) groups. The pCR rates between the SE and INS groups were highly different (42.3%
vs.7.6%, P < 0.001). The sensitivity and specificity of this prediction model were 84.5% and 62.0%.
Conclusions:: Instead of whole transcriptome-based technologies, panel gene expression with tens of essential genes implemented in a machine learning model has predictive potential for chemosensitivity in
breast cancers.
基金：
Science and Technology Department of Sichuan Province [2017SZ00 05, 2019YFS0324]; 1.3.5 project for disciplines of excellence [ZYGD18012]
语种：
外文
被引次数：
WOS：
PubmedID：
中科院分区：
出版当年[2020]版：
大类|3 区医学
小类|2 区计算机：理论方法2 区医学：信息3 区计算机：跨学科应用3 区工程：生物医学
最新[2025]版：
大类|2 区医学
小类|2 区计算机：跨学科应用2 区计算机：理论方法2 区工程：生物医学3 区医学：信息
JCR分区：
出版当年[2020]版：
Q1COMPUTER SCIENCE, THEORY & METHODSQ1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ1ENGINEERING, BIOMEDICALQ1MEDICAL INFORMATICS
最新[2023]版：
Q1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ1COMPUTER SCIENCE, THEORY & METHODSQ1ENGINEERING, BIOMEDICALQ1MEDICAL INFORMATICS
第一作者机构：[1]Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China[2]Key Laboratory of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, China[3]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
共同第一作者：
通讯作者：
通讯机构：[1]Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China[2]Key Laboratory of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, China[*1]Laboratory of Pathology, West China Hospital, Sichuan University, Guo Xue Xiang 37 Hao, Chengdu 610041, Sichuan, China.
推荐引用方式(GB/T 7714)：
Yang Libo,Fu Bo,Li Yan,et al.Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm[J].COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE.2020,192:doi:10.1016/j.cmpb.2020.105458.
APA：
Yang, Libo,Fu, Bo,Li, Yan,Liu, Yueping,Huang, Wenting...&Bu, Hong.(2020).Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,192,
MLA：
Yang, Libo,et al."Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 192.(2020)
