Evidence map›Paper›PMID 39256234›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024

A large population-based and validated study on the follow-up management and supportive strategy of locally advanced rectal cancer patients.

Yilin Yu, Haixia Wu, Liang Hong, Jianjian Qiu, Shiji Wu, Lingdong Shao, Cheng Lin, Zhiping Wang, Junxin Wu

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 authors.

Yilin YuDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Haixia WuShengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Liang HongDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Jianjian QiuDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Shiji WuDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Lingdong ShaoDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Cheng LinDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China. chenglin1988@yeah.net.
Zhiping WangDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China. 707770685@qq.com.
Junxin WuDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China. junxinwufj@aliyun.com.

Funding

Fujian Clinical Research Center for Radiation and Therapy of Digestive, Respiratory and Genitourinary Malignancies 2021Y2014the Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy 2020Y2012the National Clinical Key Specialty Construction Program 2021
6 · The paper itself

Abstract

objectiveOur objective was to evaluate the predictive factors and metastatic time for liver and lung metastasis in locally advanced rectal cancer (RC) patients.

methodsUnivariate and multivariate analysis were performed to identify risk factors and prognostic factors for liver metastasis and lung metastasis in RC. Survival probabilities were calculated using the Kaplan-Meier model and compared using the log-rank test between groups. The probability of time-to-event occurrence was calculated using the random survival forest model. Finally, the SEER database was used to verify our findings.

resultsOur results indicated that pathological T stage and pathological N stage were independent predictive factors for liver metastasis. Furthermore, CEA level, pathological T stage, and tumor deposit were independent predictive factors for lung metastasis. Based on the results of a multivariate Cox analysis, we categorized patients with liver and lung metastasis into three groups based on their scores. The results revealed that patients with higher scores had a higher probability of experiencing metastasis. For liver metastasis, Groups 1, 2, and 3 all exhibited higher occurrence rates within the first 24 months. However, for lung metastasis, Group 4 showed the highest occurrence rate at the 12th month, while Groups 5 and 6 exhibited the highest occurrence rates at the 15th month.

conclusionsIn summary, we developed predictive models to determine the likelihood of liver and lung metastasis in RC patients. It is crucial to implement a more intensive surveillance program for patients with unfavorable risk profiles in order to facilitate early detection of metastasis.

Indexed as

Liver NeoplasmsLung NeoplasmsNeoplasm StagingRectal NeoplasmsSEER ProgramAdultAgedFemaleFollow-Up StudiesHumansKaplan-Meier EstimateMaleMiddle AgedMultivariate AnalysisPrognosisProportional Hazards ModelsFollow-up strategyLiver metastasisLocally advanced rectal cancerLung metastasisPredictive model

Identifiers

PMID39256234

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Registered trials

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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.