Evidence map›Paper›PMID 39525831›Full record

ArticleCancer informatics2024

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

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

Abstract read
In one paragraph

Article in Cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Monireh ShahmoradiDepartment of Mathematical Statistics, Yasouj University, Yasouj, Iran.
Ahmad FazilatDepartment of Genetics, Motamed Cancer Institute, Breast Cancer Research Center, ACECR, Tehran, Iran.ORCID https://orcid.org/0000-0002-0698-5686
Mostafa Ghaderi-ZefreheiDepartment of Genetics, Animal Science, Yasouj University, Yasouj, Iran.
Arash ArdalanDepartment of Statistics and Computer Science, ISC Royal Holloway University of London, UK.
Ali BigdeliDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
Nahid NafissiDepartment of Breast Surgery, Rasoul Akram Hospital, Clinical Research Development Center (RCRDC) Iran University of Medical Sciences, Tehran, Iran.
Ebrahim BabaeiPreventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Department of Community and Family Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Mahsa RahmaniDepartment of Mathematical Statistics, Yasouj University, Yasouj, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

breast cancercox modelrecurrenceSemi-parametric modelssurgery

Identifiers

PMID39525831
PMCPMC11549699

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.