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 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 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.

394 Incidence and Risk Factors for Shoulder Impingement Syndrome After Breast Cancer Surgery

2012 ◽  
Vol 48 ◽  
pp. S157
Author(s):  
A. Bergmann ◽  
B.A. Silva ◽  
R.A. Dias ◽  
E.A.N. Fabro ◽  
M.A. Bello ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Surgery ◽  
Impingement Syndrome ◽  
Breast Cancer Surgery ◽  
Shoulder Impingement Syndrome ◽  
Shoulder Impingement

scholarly journals Identification of Patient Subgroups and Risk Factors for Persistent Breast Pain Following Breast Cancer Surgery

2012 ◽  
Vol 13 (12) ◽  
pp. 1172-1187 ◽  
Author(s):  
Christine Miaskowski ◽  
Bruce Cooper ◽  
Steven M. Paul ◽  
Claudia West ◽  
Dale Langford ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Breast Pain ◽  
Patient Subgroups

scholarly journals A Risk Factor Analysis of Axillary Web Syndrome in Patients After Breast Cancer Surgery: A Single Center Study in Korea

2021 ◽  
Vol 45 (5) ◽  
pp. 401-409
Author(s):  
Sangah Jeong ◽  
Byung Joo Song ◽  
Jiyoung Rhu ◽  
Cheolki Kim ◽  
Sun Im ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Underlying Disease ◽  
Disease Type ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Univariable Analysis ◽  
Axillary Web Syndrome ◽  
Single Center Study ◽  
Type Of Surgery

Objective To investigate the prevalence and risk factors of axillary web syndrome (AWS) in Korean patients.Methods This retrospective study included a total of 189 women who underwent breast cancer surgery and received physical therapy between September 2019 and August 2020. We analyzed AWS and the correlation between the patients’ demographics, underlying disease, type of surgery and chemotherapy or radiation therapy, and lymphedema.Results The prevalence of AWS was found to be 30.6%. In the univariable analysis, age, chemotherapy, and hypertension were related to AWS. Finally, the multivariable logistic regression revealed that chemotherapy (odds ratio [OR]=2.84; 95% confidence interval [CI], 1.46–5.53) and HTN (OR=2.72; 95% CI, 1.18–6.30) were the strongest risk factors of AWS.Conclusion To the best of our knowledge, this was the first study that explored the risk factors of AWS in a Korean population after breast cancer surgery. As almost one-third of patients suffer from AWS after breast cancer surgery, it is essential to closely monitor the development of AWS in patients with hypertension or undergoing chemotherapy.

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