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Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy
Chun Ming Huang
, Ming Yii Huang
, Ching Wen Huang
, Hsiang Lin Tsai
, Wei Chih Su
,
Wei Chiao Chang
, Jaw Yuan Wang
, Hon Yi Shi
TMU Research Center of Drug Discovery
TMU Research Center of Cancer Translational Medicine
Ph.D. Program in Drug Discovery and Development Industry
Master Program in Clinical Genomics and Proteomics
Department of Clinical Pharmacy
Graduate Institute of Clinical Medicine
Master Program in Applied Epidemiology
Ph.D. Program in Medical Neuroscience
The Ph.D. Program for Translational Medicine
Ph. D. Program in the Clinical Drug Development of Herbal Medicine
Research output
:
Contribution to journal
›
Article
›
peer-review
35
Citations (Scopus)
Overview
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Nursing and Health Professions
Rectum Cancer
100%
Chemoradiotherapy
100%
Artificial Neural Network
80%
Multivariate Logistic Regression Analysis
40%
Support Vector Machine
40%
K Nearest Neighbor
40%
Cancer Staging
20%
Carcinoembryonic Antigen
20%
Medicine and Dentistry
Chemoradiotherapy
100%
Rectum Cancer
100%
Multivariate Logistic Regression Analysis
40%
Cancer Staging
20%
Chemotherapy Regimens
20%
Carcinoembryonic Antigen
20%
Pharmacology, Toxicology and Pharmaceutical Science
Chemoradiation Therapy
100%
Rectum Cancer
100%
Chemotherapy Regimens
20%
Cancer Staging
20%
Carcinoembryonic Antigen
20%