Article Data

  • Views 344
  • Dowloads 152

Original Research

Open Access

Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost

  • Aimin Yang1,2,3,4,5
  • Zezhong Ma2,3,4,5,*,
  • Jian Wang2,5
  • Shujuan Yuan1,3,4,5
  • Zunqian Zhang1,3,4,6
  • Dianbo Hua7

1Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, 063210 Tangshan, Hebei, China

2Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, 063210 Tangshan, Hebei, China

3The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, 063210 Tangshan, Hebei, China

4Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, 063210 Tangshan, Hebei, China

5College of Science, North China University of Science and Technology, 063210 Tangshan, Hebei, China

6School of Metallurgy and Energy, North China University of Science and Technology, 063210 Tangshan, Hebei, China

7Beijing Sitairui Cancer Data Analysis Joint Laboratory, 101100 Beijing, China

DOI: 10.22514/ejgo.2022.031 Vol.43,Issue 4,August 2022 pp.40-48

Submitted: 03 June 2022 Accepted: 12 July 2022

Published: 15 August 2022

*Corresponding Author(s): Zezhong Ma E-mail: Mazz@stu.ncst.edu.cn

Abstract

Objective: Breast cancer is a malignant disease with a high mortality rate. Using postoperative rehabilitation data of breast cancer patients, this study explored the effects of immune, tumor, microenvironmental, psychological, nutritional, aerobic exercise and advanced work indexes on the rehabilitation of breast cancer patients. To determine the weight of the impact of different indications on cancer recovery. By combining the adaptive grid optimization algorithm with the XGBoost (Extreme Gradient Boosting) algorithm, an intelligent prediction model for breast cancer rehabilitation was constructed using patients indexes as input and the recurrence location as the output. Our results showed that the model constructed in this study could effectively predict cancer cell metastasis during breast cancer recurrence in recovered patients. Compared with artificial intelligence algorithm models such as neural network algorithm, support vector machine algorithm, gradient boosting tree algorithm and Adaboost, the model demonstrated a forecast accuracy rate of >93%. The model established in this study could effectively predict the recurrence position of breast cancer and provide an auxiliary reference for doctors to treat breast cancer patients more effectively.


Keywords

Breast cancer; Adaptive mesh optimization; XGBoost; Location of recurrence


Cite and Share

Aimin Yang,Zezhong Ma,Jian Wang,Shujuan Yuan,Zunqian Zhang,Dianbo Hua. Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost. European Journal of Gynaecological Oncology. 2022. 43(4);40-48.

References

[1] Kumar A, Ambade B, Sankar TK, Sethi SS, Kurwadkar S. Source identification and health risk assessment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in Jamshedpur, India. Sustainable Cities and Society. 2020; 52: 101801.

[2] Ma P, Zhang Z, Wang J, Zhang W, Liu J, Lu Q, et al. Review on the application of metalearning in artificial intelligence. Computational Intelligence and Neuroscience. 2021; 2021: 1–12.

[3] Yang A, Zhang W, Wang J, Yang K, Han Y, Zhang L. Review on the application of machine learning algorithms in the sequence data mining of DNA. Frontiers in Bioengineering and Biotechnology, 2020, 8: 1032.

[4] Nabizadeh R, Sorooshian A, Delikhoon M, Baghani AN, Golbaz S, Aghaei M, et al. Characteristics and health effects of volatile organic compound emissions during paper and cardboard recycling. Sustainable Cities and Society. 2020; 56: 102005.

[5] Qian Y, Kent EE. Gender differences in the association between unmet support service needs and mental health among American cancer caregivers. Supportive Care in Cancer. 2022; 30: 5469–5480.

[6] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians. 2018; 68: 394–424.

[7] Zhang Y, Zhou T, Han S, Chang J, Jiang W, Wang Z, et al. Development and external validation of a nomogram for predicting the effect of tumor size on cancer-specific survival of resected gallbladder cancer: a population-based study. International Journal of Clinical Oncology. 2021; 26: 1120–1129.

[8] Fu JB, Molinares DM, Morishita S, Silver JK, Dibaj SS, Guo Y, et al. Retrospective analysis of acute rehabilitation outcomes of cancer inpatients with leptomeningeal disease. PM & R. 2020; 12: 263–270.

[9] Cenik F, Mähr B, Palma S, Keilani M, Nowotny T, Crevenna R. Role of physical medicine for cancer rehabilitation and return to work under the premise of the “Wiedereingliederungsteilzeitgesetz”. Wiener Klinische Wochenschrift. 2019; 131: 455–461.

[10] Yu G, Chen Z, Wu J, Tan Y. A diagnostic prediction framework on auxiliary medical system for breast cancer in developing countries. Knowledge-Based Systems. 2021; 232: 107459.

[11] Montazeri M, Montazeri M, Montazeri M, Beigzadeh A. Machine learning models in breast cancer survival prediction. Technology and Health Care. 2016; 24: 31–42.

[12] Asri H, Mousannif H, Moatassime HA, Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science. 2016; 83: 1064–1069.

[13] Agarap AFM. ‘On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset’. ICMLSC ’18: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing. Association for Computing Machinery: New York, USA. 2018.

[14] Rosario-Concepción RA, Calderín YB, Aponte CL, López-Acevedo CE, Sepúlveda-Irrizarry FL. Oncologists’ attitude and knowledge about cancer rehabilitation. PM & R. 2021; 13: 1357–1361.

[15] Yang A, Han Y, Liu C, Wu J, Hua D. D-TSVR recurrence prediction driven by medical big data in cancer. IEEE Transactions on Industrial Informatics. 2021; 17: 3508–3517.

[16] Paul K, Buschbacher R. Cancer rehabilitation. American Journal of Physical Medicine & Rehabilitation. 2011; 90: S1–S4.

[17] Yancik R. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. JAMA. 2001; 285: 885.

[18] Sheng X, Huo W, Zhang C, Zhang X, Han Y. A paper quality and comment consistency detection model based on feature dimensionality reduction. Alexandria Engineering Journal. 2022; 61: 10395–10405.

[19] Chen T, Guestrin C. XGBoost. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM: New York. 2016.

[20] Bennett JA, Botkin ME. Structural shape optimization with geometric description and adaptive mesh refinement. AIAA Journal. 1985; 23: 458–464.

[21] Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. ‘Mesh optimization’. SIGGRAPH ’93: Proceedings of the 20Th Annual Con-ference on Computer Graphics and Interactive Techniques. Association for Computing Machinery: New York, USA. 1993.

[22] Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Seminars in Nuclear Medicine. 2011; 41: 449–462.

[23] Shao D, Dai Y, Li N, Cao X, Zhao W, Cheng L, et al. Artificial intelligence in clinical research of cancers. Briefings in Bioinformatics. 2022; 23: bbab523.


Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.

Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.

JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.

Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.

BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Submission Turnaround Time

Conferences

Top