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:: Volume 22, Issue 1 (1-2024) ::
Int J Radiat Res 2024, 22(1): 163-169 Back to browse issues page
Backpropagation neural network-based survival analysis for breast cancer patients
J.F. Pei , J. Zhang , D.C. Jin , B.L. Miao
Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, 312000, China , cdshuyi19931866093@163.com
Abstract:   (573 Views)
Background: A substantial number of women throughout the world are affected with breast cancer, a dangerous and sometimes deadly condition. The creation of precise prediction models to determine the chance of survival in breast cancer patients has drawn increasing attention in recent years. The use of backpropagation neural networks (BPNNs) to forecast breast cancer patient survival is investigated in this study. Materials and Methods: A total of 198 patients with early breast cancer who were treated in our hospital were selected The control group received traditional breast cancer radical mastectomy and radiotherapy, and the experimental group received mastoscopy Adjuvant nipple-areola complex (NAC) modified radical mastectomy combined with prosthesis implantation and radiotherapy was used to compare the surgical conditions, postoperative complications, patient satisfaction and living standards in two groups. Results: The range of change was small, and the difference was statistically significant (P<0.05) ;in the laboratory group, patient dissatisfaction was noticeably raised over that in the standard group, and the discrepancy was politically sensitive (P<0.05); over that in the control group, the scores of all dimensions of survival quality were appreciably raised over that in the laboratory group, and the correlation was striking (P<0.05). Survival quality was greatly expanded in the experimental and postoperative groups at 3 and 6 months postoperatively before surgery, and the difference was statistically significant (P<0.05); Conclusion: The study demonstrates that BPNN-based predictive models can be useful tools for improving the accuracy of breast cancer survival rate prediction, thus aiding in more effective treatment planning and decision-making for breast cancer patients.
Keywords: Breast lumpectomy, breast cancer, nipple-areola complex, prosthesis insertion.
Full-Text [PDF 769 kb]   (411 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Trayes KP and Cokenakes SHE (2021) Breast cancer treatment. Am Fam Physician, 104(2): 171-178.
2. Burstein HJ, Curigliano G, Thürlimann B, et al. (2021) Panelists of the St Gallen CONSENSUS CONFERENCE.Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. Ann Oncol, 32(10):1216-1235.
3. Wang H and Zhang H (2021) Visual mechanism characteristics of static painting based onPSO-BP neural network. ComputIntell Neurosci, 2021: 3835083. [DOI:10.1155/2021/3835083]
4. WEI Changsheng, LUO Chengyu, ZHANG Shuqi (2021) The application of circumareolar incision combined with breast lumpectomy for axillary lymph node dissection in breast-conserving surgery for early stage breast cancer[J]. Journal of Capital Medical University, 42(6): 1014-1020.
5. Jenkins S, Kachur ME, Rechache K, et al. (2021) Rare breast cancer subtypes. Curr Oncol Rep, 23(5): 54. [DOI:10.1007/s11912-021-01048-4]
6. Zeng X, Liu C, Yao J, et al. (2021) Breast cancer stem cells,heterogeneity, targeting therapies and therapeutic implications. Pharmacol Res, 163: 105320. [DOI:10.1016/j.phrs.2020.105320]
7. Houghton SC and Hankinson SE (2021) Cancer progress and priorities: Breast cancer. Cancer Epidemiol Biomarkers Prev, 30(5): 822-844. [DOI:10.1158/1055-9965.EPI-20-1193]
8. Vranic S, Cyprian FS, Gatalica Z, Palazzo J (2019) PD-L1 status in breast cancer: Current view and perspectives. Semin Cancer Biol, 72: 146-154. [DOI:10.1016/j.semcancer.2019.12.003]
9. Sun L, Zhang JJ, Huaihua Y, et al. (2021) Safety and quality of life improvement of modified radical breast cancer treatment with breast lumpectomy-assisted preservation of the nipple-areola complex[J]. Journal of Oncology, 27(4): 283-287.
10. Katsura C, Ogunmwonyi I, Kankam HK, Saha S (2022) Breast cancer: presentation,investigation and management. Br J Hosp Med (Lond), 83(2): 1-7. [DOI:10.12968/hmed.2021.0459]
11. Shen Q and Reedijk M (2021) Notch Signaling and the Breast Cancer Microenvironment. AdvExp Med Biol, 1287: 183-200. [DOI:10.1007/978-3-030-55031-8_12]
12. Thomas M (2021) Breast cancer-related upper limb lymphoedema: an overview. Br JCommunityNurs, 26(4):S30-S35. [DOI:10.12968/bjcn.2021.26.Sup4.S30]
13. Zhong L, Fan LJ, Du JZ, et al. (2020) Effect of neoadjuvant chemotherapy on the efficacy and postoperative complications of stage I breast reconstruction with mastectomy prosthesis implantation[J]. Chinese Journal of Practical Diagnosis and Therapy, 34(8): 782-785.
14. Zheng Q (2021) Information System Security Evaluation Algorithm Based on PSO-BPNeural Network. ComputIntell Neurosci, 2021: 6046757. [DOI:10.1155/2021/6046757]
15. Wang S, Xiong Y, Zhang Q, et al. (2021) Clinical significance and immunogenomic landscape analyses of the immune cellsignature based prognostic model for patients with breast cancer. Brief Bioinform, 22(4): bbaa311. [DOI:10.1093/bib/bbaa311]
16. Zhang YN, Xia KR, Li CY, et al. (2021) Review of breast cancer pathologigcal image processing. Biomed Res Int, 2021:1994764. [DOI:10.1155/2021/1994764]
17. Duarte C, Salazar A, Strasser-Weippl K, et al. (2021) Breast cancer in Colombia: a growing challengefor the healthcare system. Breast Cancer Res Treat, 186(1): 15-24. [DOI:10.1007/s10549-020-06091-6]
18. Xie L and Xie J (2021) Recent and long-term efficacy of NAC-preserving modified radical mastectomy combined with one-stage prosthesis implantation for early-stage breast cancer[J]. Chinese Journal of General Surgery, 30(7): 871-876.
19. Cali Daylan AE and Leone JP (2021) Targeted therapies for breast cancer brain metastases. Clin Breast Cancer, 21(4): 263-270. [DOI:10.1016/j.clbc.2020.11.014]
20. Wang M (2021) Manufacturing capacity evaluation of smart job-shop based on neural network. Int Journal of Simulation Modelling, 20(4): 778-789. [DOI:10.2507/IJSIMM20-4-CO19]
21. Wu D and Shen Y (2021) English feature recognition based on GA-BP neural network algorithm and data mining. ComputIntell Neurosci, 2021: 1890120. [DOI:10.1155/2021/1890120]
22. Davey MG, Lowery AJ, Miller N, Kerin MJ (2021) MicroRNA expression profiles and breast cancer chemotherapy. Int J Mol Sci, 22(19): 10812. [DOI:10.3390/ijms221910812]
23. Duan C, Gao J, Jia H (2020) Long-term comparison of clinical efficacy between modified radical surgery and breast-conserving surgery for early stage breast cancer patients[J]. Guizhou Medicine, 44(2): 267-268.
24. Teichgraeber DC, Guirguis MS, Whitman GJ (2021) Breast cancer staging: Updates intheAJCC Cancer Staging Manual, 8th Edition, and Current Challenges forRadiologists, From the AJR Special Series on Cancer Staging. AJR Am J Roentgenol, 217(2): 278-290. [DOI:10.2214/AJR.20.25223]
25. Gutnik L and Fayanju OM (2021) Controversies in breast cancer surgery. Surg Clin North Am, 101(6): 1033-1044. [DOI:10.1016/j.suc.2021.06.002]
26. Wu PJ and Yang D (2021) E-commerce workshop scheduling based on deep learning and genetic algorithm. Int Journal of Simulation Modelling, 20(1): 192-200. [DOI:10.2507/IJSIMM20-1-CO4]
27. Chen Y (2021) BP neural network based on simulated annealing algorithm optimizationfor financial crisis dynamic early warning model. ComputIntell Neurosci, 2021: 4034903. [DOI:10.1155/2021/4034903]
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Pei J, Zhang J, Jin D, Miao B. Backpropagation neural network-based survival analysis for breast cancer patients. Int J Radiat Res 2024; 22 (1) :163-169
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Volume 22, Issue 1 (1-2024) Back to browse issues page
International Journal of Radiation Research
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