Unveiling Recurrence Patterns: Analyzing Predictive Risk Factors for Breast Cancer Recurrence after Surgery.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-11-08 eCollection Date: 2024-01-01 DOI:10.1177/11769351241297633

Monireh Shahmoradi, Ahmad Fazilat, Mostafa Ghaderi-Zefrehei, Arash Ardalan, Ali Bigdeli, Nahid Nafissi, Ebrahim Babaei, Mahsa Rahmani

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Abstract

Objectives: Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models.

Methods: After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets. Model fitting, was assessed through the Akaike Information Criterion (AIC), and indicated the logistic distribution as the most fit one among concurrent and independent variable models.

Results: The Cox proportional hazard regression model consistently demonstrated superior fitting, characterized by the lowest AIC values. The average age at diagnosis was 50.39 years, with a standard deviation of 11.13. Typical survival time was estimated 53.44 months, falling within a confidence interval of 51.41-55.48 months at a 95% confidence level. The 1-year survival rate was determined at 0.92 (95% CI: 0.89-0.94). Notably, patient age while cancer diagnosis, progesterone receptor (PR), tumor grade, and tumor stage were found to be statistically significant (P < .05) risk factors for prediction of BC recurrence after surgery in Iran by Cox model.

Conclusions: Our findings underscore the importance of further exploration and consideration of the identified risk factors in BC research and treatment strategies.

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揭示复发模式：分析乳腺癌术后复发的预测风险因素。

目的：乳腺癌（BC）是导致全球女性癌症死亡的第二大原因，因此有必要针对其关键问题进行全面研究。本研究旨在利用各种参数和半参数模型，探讨乳腺癌患者从手术到疾病复发的时间间隔及其生存率：在对伊朗德黑兰 BC 中心 2010 年至 2021 年收集的数据进行检查后，2246 个数据集中有 171 个病例符合分析标准。通过阿凯克信息准则（AIC）对模型拟合进行评估，结果表明，在并发和自变量模型中，逻辑分布是最拟合的模型：结果：Cox 比例危险回归模型一直表现出较好的拟合效果，其特点是 AIC 值最低。确诊时的平均年龄为 50.39 岁，标准差为 11.13 岁。典型生存时间估计为 53.44 个月，在 95% 的置信水平下，置信区间为 51.41-55.48 个月。1 年生存率为 0.92（95% 置信区间：0.89-0.94）。值得注意的是，癌症确诊时的患者年龄、孕酮受体（PR）、肿瘤分级和肿瘤分期均有统计学意义（P 结论：P<0.05）：我们的研究结果表明，在 BC 研究和治疗策略中进一步探索和考虑已确定的风险因素非常重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.