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

IF 2.5 3区 工程技术 Q2 BIOLOGY Yale Journal of Biology and Medicine Pub Date : 2023-09-29 eCollection Date: 2023-09-01 DOI:10.59249/IAJU7580
Jiping Wang, Shunqin Zhang, Huangdi Yi, Shuangge Ma
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Abstract

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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老年女性癌症患者行乳房切除术与乳房切除术的疗效比较分析:基于深度学习的大数据分析。
目的:为了评估治疗的相对有效性,随机临床试验仍然是金标准,但可能会受到成本高、样本量有限、无法完全反映现实世界以及可行性问题的挑战。目的是展示一种利用大型电子病历(EMR)数据模拟临床试验的大数据方法。为了克服回归分析的局限性,开发了一个基于深度学习的分析管道。研究设计和设置:对于早期女性癌症患者,乳房切除术(乳房切除术)和乳房切除术是两种最常用的手术方法。使用监测、流行病学和最终结果(SEER)-医疗保险数据设计了一项模拟试验,以评估其在总生存率方面的相对有效性。分析管道由倾向得分步骤、加权生存分析步骤和引导推理步骤组成。结果:共有65997名受试者参加了模拟试验,其中50704人和15293人分别参加了肿块切除术和乳房切除术。就老年SEER-Medicare早期女性癌症患者的总体生存率而言,这两种手术具有可比的效果(生存年变化=0.08,95%置信区间(CI):-0.08,0.25)。结论:本研究展示了“挖掘大型电子病历数据+基于深度学习的分析”的力量,所提出的分析策略和技术具有潜在的广泛应用前景。它为肿块切除术和乳房切除术的相对有效性提供了令人信服的证据。
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来源期刊
Yale Journal of Biology and Medicine
Yale Journal of Biology and Medicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.00
自引率
0.00%
发文量
41
期刊介绍: The Yale Journal of Biology and Medicine (YJBM) is a graduate and medical student-run, peer-reviewed, open-access journal dedicated to the publication of original research articles, scientific reviews, articles on medical history, personal perspectives on medicine, policy analyses, case reports, and symposia related to biomedical matters. YJBM is published quarterly and aims to publish articles of interest to both physicians and scientists. YJBM is and has been an internationally distributed journal with a long history of landmark articles. Our contributors feature a notable list of philosophers, statesmen, scientists, and physicians, including Ernst Cassirer, Harvey Cushing, Rene Dubos, Edward Kennedy, Donald Seldin, and Jack Strominger. Our Editorial Board consists of students and faculty members from Yale School of Medicine and Yale University Graduate School of Arts & Sciences. All manuscripts submitted to YJBM are first evaluated on the basis of scientific quality, originality, appropriateness, contribution to the field, and style. Suitable manuscripts are then subject to rigorous, fair, and rapid peer review.
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