Risk Factors Associated with Lymphoedema among Chinese Women after Breast Cancer Surgery

2013 ◽  
pp. 2180-2196
Author(s):  
Hua-Ping Huang ◽  
Qing Zeng ◽  
Jian-Rong Zhou
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Chinese Women ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Factors Associated

scholarly journals Risk Factors for Chronic Pain Following Breast Cancer Surgery: A Prospective Study

2006 ◽  
Vol 7 (9) ◽  
pp. 626-634 ◽  
Author(s):  
Ellen L. Poleshuck ◽  
Jennifer Katz ◽  
Carl H. Andrus ◽  
Laura A. Hogan ◽  
Beth F. Jung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Chronic Pain ◽  
Prospective Study ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
A Prospective Study

scholarly journals The incidence and risk factors of frozen shoulder in patients with breast cancer surgery

2019 ◽  
Vol 26 (4) ◽  
pp. 825-828 ◽  
Author(s):  
Chul‐Hyun Cho ◽  
Kyoung‐Lak Lee ◽  
Jihyoung Cho ◽  
Duhan Kim
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Frozen Shoulder ◽  
Cancer Surgery ◽  
Breast Cancer Surgery

scholarly journals Income level and regional policies, underlying factors associated with unwarranted variations in conservative breast cancer surgery in Spain

BMC Cancer ◽  
2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Manuel Ridao-López ◽  
Sandra García-Armesto ◽  
Begoña Abadía-Taira ◽  
Salvador Peiró-Moreno ◽  
Enrique Bernal-Delgado
Keyword(s):  
Breast Cancer ◽  
Income Level ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Factors Associated ◽  
Regional Policies

scholarly journals Development of a screening instrument for risk factors of persistent pain after breast cancer surgery

2012 ◽  
Vol 107 (9) ◽  
pp. 1459-1466 ◽  
Author(s):  
R Sipilä ◽  
A-M Estlander ◽  
T Tasmuth ◽  
M Kataja ◽  
E Kalso
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Screening Instrument ◽  
Persistent Pain ◽  
Cancer Surgery ◽  
Breast Cancer Surgery

scholarly journals Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 47
Author(s):  
Shi-Jer Lou ◽  
Ming-Feng Hou ◽  
Hong-Tai Chang ◽  
Hao-Hsien Lee ◽  
Chong-Chi Chiu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Summary Score ◽  
Cancer Surgery ◽  
Machine Learning Algorithms ◽  
Breast Cancer Surgery ◽  
Support Vector ◽  
Survival Prediction ◽  
Sf 36

Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.

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