breast cancer surgery
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Breast Cancer ◽  
2022 ◽  
Midori Morita ◽  
Akihiko Shimomura ◽  
Emi Tokuda ◽  
Yoshiya Horimoto ◽  
Yukino Kawamura ◽  

Abstract Background Due to the lack of clinical trials on the efficacy of chemotherapy in older patients, an optimal treatment strategy has not been developed. We investigated whether adjuvant chemotherapy could improve the survival of older patients with breast cancer in Japan. Methods We retrospectively analyzed data of patients with breast cancer aged ≥ 70 years who underwent breast cancer surgery in eight hospitals between 2008 and 2013. Clinical treatment and follow-up data were obtained from the patients’ medical electric records. Results A total of 1095 patients were enrolled, of which 905 were included in the initial non-matched analysis. The median age and follow-up period were 75 (range 70–93) and 6.3 years, respectively. Of these patients, 127 (14%) received adjuvant chemotherapy (Chemo group) while the remaining 778 (86%) did not (Control group). The Chemo group was younger (mean age in years 73 vs 76; P < 0.0001), had a larger pathological tumor size (mean mm 25.9 vs 19.9; P < 0.0001), and more metastatic axillary lymph nodes (mean numbers 2.7 vs 0.7; P < 0.0001) than the Control group. The disease-free survival (DFS) and overall survival (OS) did not differ significantly between the two groups (P = 0.783 and P = 0.558). After matched analyses, DFS was found to be significantly prolonged with adjuvant chemotherapy (P = 0.037); however, OS difference in the matched cohort was not statistically significant (P = 0.333). Conclusion The results showed that adjuvant chemotherapy was associated with a reduced risk of recurrence, but survival benefits were limited.

2022 ◽  
Vol 10 (1) ◽  
Paolo Taurisano ◽  
Chiara Abbatantuono ◽  
Veronica Verri ◽  
Ilaria Pepe ◽  
Luigia S. Stucci ◽  

Abstract Background Psycho-oncology literature pointed out that individual health outcomes may depend on patients’ propensity to adopt approach or, conversely, avoidant coping strategies. Nevertheless, coping factors associated with postoperative distress remain unclear, unfolding the lack of tailored procedures to help breast cancer patients manage the psychological burden of scheduled surgery. In view of this, the present study aimed at investigating: 1. pre-/post-surgery distress variations occurring among women diagnosed with breast cancer; 2. the predictivity of approach and avoidant coping strategies and factors in affecting post-surgery perceived distress. Methods N = 150 patients (mean age = 59.37; SD =  ± 13.23) scheduled for breast cancer surgery were administered a screening protocol consisting of the Distress Thermometer (DT) and the Brief-COPE. The DT was used to monitor patients’ distress levels before and after surgery (± 7 days), whereas the Brief-COPE was adopted only preoperatively to evaluate patients’ coping responses to the forthcoming surgical intervention. Non-parametric tests allowed for the detection of pre-/post-surgery variations in patients’ perceived distress. Factor analysis involved the extraction and rotation of principal components derived from the Brief-COPE strategies. The predictivity of such coping factors was investigated through multiple regression (Backward Elimination). Results The Wilcoxon Signed-Rank Test yielded a significant variation in DT mean scores (TW = -5,68 < -zα/2 = -1,96; p < .001) indicative of lower perceived distress following surgery. The four coping factors extracted and Varimax-rotated were, respectively: 1. cognitive processing (i.e., planning + acceptance + active coping + positive reframing); 2. support provision (i.e., instrumental + emotional support); 3. emotion-oriented detachment (i.e., self-blame + behavioral disengagement + humor + denial); 4. goal-oriented detachment (i.e., self-distraction). Among these factors, support provision (B = .458; β = − .174; t = − 2.03; p = .045), encompassing two approach coping strategies, and goal-oriented detachment (B = .446; β = − .176; t = − 2.06; p = .042), consisting of one avoidant strategy, were strongly related to post-surgery distress reduction. Conclusion The present investigation revealed that the pre-surgery adoption of supportive and goal-oriented strategies led to postoperative distress reduction among breast cancer patients. These findings highlight the importance of timely psychosocial screening and proactive interventions in order to improve patients’ recovery and prognosis.

Clinical Pain ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 99-104
Mi Kyung Cho ◽  
Dong Min Kim ◽  
Young Mo Kim ◽  
Tae-Woong Yang ◽  
Jin-A Yoon ◽  

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 47
Shi-Jer Lou ◽  
Ming-Feng Hou ◽  
Hong-Tai Chang ◽  
Hao-Hsien Lee ◽  
Chong-Chi Chiu ◽  

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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