Pharmacoeconomic aspects of adjuvant early breast cancer treatment in postmenopausal women with anastrozole or tamoxifen: a Slovenian perspective

2004 ◽  
Vol 2 (3) ◽  
pp. 75
Author(s):  
P Piskur ◽  
M Sonc ◽  
T Cufer ◽  
S Borstnar ◽  
A Mrhar
2020 ◽  
Vol 20 (17) ◽  
pp. 1994-2004 ◽  
Author(s):  
Pooja Ratre ◽  
Keerti Mishra ◽  
Amit Dubey ◽  
Amber Vyas ◽  
Akhlesh Jain ◽  
...  

Background: Estrogens are essential for the growth of breast cancer in the case of premenopausal as well as in postmenopausal women. However, most of the breast cancer incidences are reported in postmenopausal women and the concurrent risk surges with an increase in age. Since the enzyme aromatase catalyses essential steps in estrogen biosynthesis, Aromatase Inhibitors (AIs) are effective targeted therapy in patients with Estrogen Receptor positive (ER+) breast cancer. AIs are more effective than Selective Estrogen Receptor Modulators (SERMs) because they block both the genomic and nongenomic activities of ER. Till date, first, second and third-generation AIs have been approved by the FDA. The third-generation AIs, viz. Letrozole, Anastrozole, Exemestane, are currently used in the standard treatment for postmenopausal breast cancer. Methods: Data were collected from Medline, PubMed, Google Scholar, Science Direct through searching of keywords: ‘aromatase’, ‘aromatase inhibitors’, ‘breast cancer’, ‘steroidal aromatase inhibitors’, ‘non-steroidal inhibitors’ and ‘generations of aromatase inhibitors’. Results: In the current scenario of breast cancer chemotherapy, AIs are the most widely used agents which reveal optimum efficacy along with the least side effects. Keeping in view the prominence of AIs in breast cancer therapy, this review covered the detailed description of aromatase including its role in the biosynthesis of estrogen, biochemistry, gene expression, 3D-structure, and information of reported AIs along with their role in breast cancer treatment. Conclusion: AIs are the mainstream solution of the ER+ breast cancer treatment regimen with the continuous improvement of human understanding of the importance of a healthy life of women suffering from breast cancer.


2020 ◽  
Author(s):  
Qing Yang ◽  
Ting Luo ◽  
Wei Zhang ◽  
Xiaorong Zhong ◽  
Ping He ◽  
...  

Abstract Background: Due to the multidimensional, multilayered, and chronological order of the cancer data in this study, it was challenging for us to extract treatment paths. Therefore, it was necessary to design a new data mining scheme to effectively extract the treatment path of breast cancer. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. Methods: We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways.Results: We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, 3 postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of P <0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions.Conclusions: The cSPADE algorithm combined with system clustering can achieve hierarchical and vertical mining of breast cancer treatment models. By uncovering the treatment pathways of early breast cancer patients by this method, the real-world breast cancer treatment behavior model can be evaluated, and it can provide a reference for the redesign and optimization of the treatment pathways.


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