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Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis

Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data appr...

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Autores principales: Wang, Jiping, Zhang, Shunqin, Yi, Huangdi, Ma, Shuangge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: YJBM 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524818/
https://www.ncbi.nlm.nih.gov/pubmed/37781001
http://dx.doi.org/10.59249/IAJU7580
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author Wang, Jiping
Zhang, Shunqin
Yi, Huangdi
Ma, Shuangge
author_facet Wang, Jiping
Zhang, Shunqin
Yi, Huangdi
Ma, Shuangge
author_sort Wang, Jiping
collection PubMed
description Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of “mining large EMR data + deep learning-based analysis,” and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy.
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spelling pubmed-105248182023-09-29 Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis Wang, Jiping Zhang, Shunqin Yi, Huangdi Ma, Shuangge Yale J Biol Med Original Contribution Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of “mining large EMR data + deep learning-based analysis,” and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy. YJBM 2023-09-29 /pmc/articles/PMC10524818/ /pubmed/37781001 http://dx.doi.org/10.59249/IAJU7580 Text en Copyright ©2023, Yale Journal of Biology and Medicine https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons CC BY-NC license, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited. You may not use the material for commercial purposes.
spellingShingle Original Contribution
Wang, Jiping
Zhang, Shunqin
Yi, Huangdi
Ma, Shuangge
Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title_full Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title_fullStr Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title_full_unstemmed Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title_short Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis
title_sort comparative effectiveness analysis of lumpectomy and mastectomy for elderly female breast cancer patients: a deep learning-based big data analysis
topic Original Contribution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524818/
https://www.ncbi.nlm.nih.gov/pubmed/37781001
http://dx.doi.org/10.59249/IAJU7580
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