Evidence map›Paper›PMID 39579008›Full record

ArticleCancer control : journal of the Moffitt Cancer Center

Evaluation of Risk Factors, and Development and Validation of Prognostic Prediction Models for Distant Metastasis in Patients With Rectal Cancer: A Study Based on the SEER Database and a Chinese Population.

Huiru Zhang, Haojun Wang, Yan Yao, Lijuan Liu, Fubin Feng, Huayao Li, Changgang Sun

Abstract readValidation Study
In one paragraph

Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Huiru ZhangCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.ORCID 0009-0007-3872-5049
Haojun WangCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.ORCID 0009-0009-4363-5050
Yan YaoOncology Laboratory, Weifang Traditional Chinese Hospital, Weifang, China.ORCID 0000-0003-3981-9112
Lijuan LiuDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang, China.ORCID 0000-0003-1138-5543
Fubin FengDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang, China.ORCID 0000-0002-1586-638X
Huayao LiCollege of Traditional Chinese Medicine, Shandong Second Medical University, Weifang, China.ORCID 0000-0003-2331-366X
Changgang SunCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.ORCID 0000-0003-0778-8788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to construct a prediction model regarding risk factors and prognostic factors for distant metastasis of T1-T3 stage rectal cancer. For this purpose, a population-based retrospective cohort study was conducted.

methodsData on 7872 patients diagnosed with rectal cancer between 2004 and 2020 were obtained from the Surveillance, Epidemiology, and End Results database, of whom 746 had distant metastases at diagnosis. Independent risk factors for distant metastasis of rectal cancer were determined using univariate and multivariate logistic regression analyses. Cox proportional hazards regression analyses clarified the independent prognostic factors for distant metastases of rectal cancer. A 7:3 randomization process was used to place all patients into the training and internal validation groups. Furthermore, we retrospectively collected clinical data from 226 patients who had both rectal cancer and distant metastases between 2012 and 2024 at the Weifang Hospital of Traditional Chinese Medicine. We used the calibration curve, DCA curve, C-index, and area under the curve (AUC) to assess the discriminatory and pre-precision qualities of the models.

resultsThe multivariate logistic regression analysis identified race, tumor grade, T stage, N stage, radiotherapy, chemotherapy, surgery, tumor size, and histological subtype as risk factors for distant metastases in rectal cancer, with AUC values for both training and validation sets exceeding 0.8. Using Cox regression analysis, we determined that the age, sex, tumor size, surgery, chemotherapy, and radiotherapy were independent predictors of distant metastasis of rectal cancer. In the prognostic model, the C-index of the training cohort was 0.687 (95% CI: 0.6615-0.7125), that of the internal validation cohort was 0.692 (95% CI: 0.6508-0.7332), and that of the external validation cohort was 0.704 (0.6785-0.7295).

conclusionOur nomogram can predict risk factors and analyze the 1-, 2-, and 3 year prognosis of distant metastases in patients with rectal cancer, providing valuable guidance for future clinical work.

Indexed as

Neoplasm MetastasisRectal NeoplasmsSEER ProgramAdultAgedChinaFemaleHumansMaleMiddle AgedNeoplasm StagingNomogramsPrognosisProportional Hazards ModelsRetrospective StudiesRisk Factorsdistant metastasisfactual databaselogistic modelsnomogramprognosisrectal cancerrisk factors

Identifiers

PMID39579008
PMCPMC11585045

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