Risk factors for acute pain and its persistence following breast cancer surgery

Pain ◽  
2005 ◽  
Vol 119 (1-3) ◽  
pp. 16-25 ◽  
Author(s):  
Jennifer Katz ◽  
Ellen L. Poleshuck ◽  
Carl H. Andrus ◽  
Laura A. Hogan ◽  
Beth F. Jung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Acute Pain ◽  
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 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 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.

scholarly journals Can Acute Pain Treatment Reduce Postsurgical Comorbidity after Breast Cancer Surgery? A Literature Review

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fumimasa Amaya ◽  
Toyoshi Hosokawa ◽  
Akiko Okamoto ◽  
Megumi Matsuda ◽  
Yosuke Yamaguchi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Acute Pain ◽  
Pain Treatment ◽  
Cancer Recurrence ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Regional Analgesia ◽  
Postsurgical Pain ◽  
Analgesic Regimen ◽  
Definition Of

Regional analgesia, opioids, and several oral analgesics are commonly used for the treatment of acute pain after breast cancer surgery. While all of these treatments can suppress the acute postsurgical pain, there is growing evidence that suggests that the postsurgical comorbidity will differ in accordance with the type of analgesic used during the surgery. Our current study reviewed the effect of analgesics used for acute pain treatments on the major comorbidities that occur after breast cancer surgery. A considerable number of clinical studies have been performed to investigate the relationship between the acute analgesic regimen and common comorbidities, including inadequate quality of recovery after the surgery, persistent postsurgical pain, and cancer recurrence. Previous studies have shown that the choice of the analgesic modality does affect the postsurgical comorbidity. In general, the use of regional analgesics has a beneficial effect on the occurrence of comorbidity. In order to determine the best analgesic choice after breast cancer surgery, prospective studies that are based on a clear definition of the comorbidity state will need to be undertaken in the future.

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
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