Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model

Chen Rui; Cai Nian; Luo Zhi-hao; Liu Xuan; Li Jian

doi:10.12052/gdutxb.230007

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Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model

更新时间：2026-05-27

- Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model
- Vol. 41, Issue 1, Pages: 34-40(2024)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 中山大学肿瘤防治中心华南肿瘤学国家重点实验室,广东,广州,510060
  3. 中山大学肿瘤防治中心广东省恶性肿瘤临床医学研究中心,广东,广州,510060
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230007
  CLC： TP391
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
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陈睿, 蔡念, 罗智浩, 刘璇, 黎剑. 基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析[J]. 广东工业大学学报, 2024, 41(1): 34-40.

Chen Rui, Cai Nian, Luo Zhi-hao, et al. Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model[J]. 2024, 41(1): 34-40.
陈睿, 蔡念, 罗智浩, 刘璇, 黎剑. 基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析[J]. 广东工业大学学报, 2024, 41(1): 34-40. DOI： 10.12052/gdutxb.230007.

Chen Rui, Cai Nian, Luo Zhi-hao, et al. Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model[J]. 2024, 41(1): 34-40. DOI： 10.12052/gdutxb.230007.

摘要

针对乳腺癌病程长、疾病发展较缓和的特点，提出了一种多任务循环神经网络带状回归模型进行乳腺癌个体生存分析。首先，提出一种基于循环神经网络的多任务带状回归模型，通过识别各病理特征对不同患者之间影响的区别，优化患者个体生存分析。其次，对带状校验矩阵的形式进行拓展并研究其对患者风险分布的影响。最后，在乳腺癌真实数据集上进行生存分析，不同患者之间产生明显的差异性，验证了模型的有效性。在2个乳腺癌真实数据集上进行的生存分析结果显示，基于循环神经网络的多任务带状回归模型的一致性指数(Concordance Index

C-index)较医学上常用的Cox回归模型有较大提升，并有着更小的95%置信区间。

Abstract

In view of the characteristics of long course and mild disease development of breast cancer

a multi-task recurrent neural network banded regression model is proposed to analyze individual survival of breast cancer. First

a multi-task banded regression model based on recurrent neural network is proposed to optimize the individual survival analysis of patients by identifying the difference in the effects of various pathological features on different patients. Then

the form of the banded check matrix is expanded and its effect on the hazard distribution of patient is investigated. Finally

the survival analysis on the real datasets of breast cancer shows obvious differences among different patients

which verifies the validity of the model. The survival analysis on two real datasets of breast cancer shows that the C-index of the multi-task banded regression model based on recurrent neural network is greatly improved compared with the Cox regression model commonly used in medicine

and has a smaller 95% confidence interval.

关键词

Keywords

references

ZHOU X, LI C, RAHAMAN M M, et al . A comprehensive review for breast histopathology image analysis using classical and deep neural networks [J ] . IEEE Access, 2020, 8: 90931-90956.

ARNOLD M, MORGAN E, RUMGAY H, et al . Current and future burden of breast cancer: global statistics for 2020 and 2040 [J ] . The Breast, 2022, 66: 15-23.

LOH S Y, YIP C H. Breast cancer as a chronic illness: implications for rehabilitation and medical education [J]. Journal of Health and Translational Medicine, 2006, 9(2): 3-11.

GEORGE B, SEALS S, ABAN I. Survival analysis and regression models [J]. Journal of Nuclear Cardiology, 2014, 21(4): 686-694.

GANGGAYAH M D, TAIB N A, HAR Y C, et al . Predicting factors for survival of breast cancer patients using machine learning techniques [J ] . BMC Medical Informatics and Decision Making, 2019, 19(1): 1-17.

SCHOBER P, VETTER T R. Survival analysis and interpretation of time-to-event data: the tortoise and the hare [J]. Anesthesia and Analgesia, 2018, 127(3): 792.

KATZMAN J L, SHAHAM U, CLONINGER A, et al . DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network [J ] . BMC Medical Research Methodology, 2018, 18(1): 1-12.

LEE C, YOON J, VAN DER SCHAAR M. Dynamic-deephit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data [J]. IEEE Transactions on Biomedical Engineering, 2019, 67(1): 122-133.

TANG W, MA J, MEI Q, et al. SODEN: a scalable continuous-time survival model through ordinary differential equation networks[J]. J Mach Learn Res, 2022, 23: 34: 1-34: 29.

YU C N, GREINER R, LIN H C, et al . Learning patient-specific cancer survival distributions as a sequence of dependent regressors [J ] . Advances in Neural Information Processing Systems, 2011, 24: 1845-1853.

HU S, FRIDGEIRSSON E, VAN WINGEN G, et al. Transformer-based deep survival analysis[C]//Survival Prediction-Algorithms, Challenges and Applications. New York: PMLR, 2021: 132-148.

WANG H H, CAI N, CHEN R, et al . Multi-task banded regression model for individual cancer survival analysis [J ] . Computer Engineering and Applications, 2023, 59(10): 299-305.

MEDSKER L R, JAIN L C. Recurrent neural networks [J]. Design and Applications, 2001, 5: 64-67.

WU Y, HALABI S. Interval censoring[M]. Boca Raton: Chapman and Hall/CRC, 2019: 493-508.

BUHLMANN H, GISLER A. Credibility in the regression case revisited (A late tribute to Charles A. Hachemeister) [J]. ASTIN Bulletin: The Journal of the IAA, 1997, 27(1): 83-98.

YE K, LIM L H. Every matrix is a product of toeplitz matrices [J]. Foundations of Compu-tational Mathematics, 2016, 16(3): 577-598.

CURTIS C, SHAH S P, CHIN S F, et al . The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups [J ] . Nature, 2012, 486(7403): 346-352.

SCHUMACHER M, BASTERT G, BOJAR H, et al . Randomized 2 × 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients [J ] . Journal of Clinical Oncology, 1994, 12(10): 2086-2093.

ANTOLINI L, BORACCHI P, BIGANZOLI E. A time-dependent discrimination index for survival data [J]. Statistics in Medicine, 2005, 24(24): 3927-3944.

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