Evidence map›Paper›PMID 34427821›Full record

ArticleAnnals of surgical oncology2022

Predicting Colon Cancer-Specific Survival for the Asian Population Using National Cancer Registry Data from Taiwan.

Han-Ching Chan, Chi-Cheng Huang, Ching-Chieh Huang, Amrita Chattopadhyay, Kuan-Hung Yeh, Wen-Chung Lee, Chun-Ju Chiang, Hsin-Ying Lee, Skye Hung-Chun Cheng, Tzu-Pin Lu

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Article in Annals of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Han-Ching ChanInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Chi-Cheng HuangDepartment of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Ching-Chieh HuangInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Amrita ChattopadhyayInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Kuan-Hung YehDepartment of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Wen-Chung LeeInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Chun-Ju ChiangInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Hsin-Ying LeeInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Skye Hung-Chun ChengDepartment of Radiation Oncology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan.
Tzu-Pin LuInstitute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. tplu@ntu.edu.tw.ORCID http://orcid.org/0000-0003-3697-0386

Funding

Ministry of Health and Welfare A1081115Ministry of Science and Technology, Taiwan MOST-104-2314-B-002-107-MY2Ministry of Science and Technology, Taiwan MOST-106-2314-B-002-134-MY2
6 · The paper itself

Abstract

purposeColon cancer is the third most incident and life-threatening cancer in Taiwan. A comprehensive survival prediction system would greatly benefit clinical practice in this area. This study was designed to develop an accurate prognostic model for colon cancer patients by using clinicopathological variables obtained from the Taiwan Cancer Registry database.

methodsWe analyzed 20,218 colon cancer patients from the Taiwan Cancer Registry database, who were diagnosed between 2007 and 2015, were followed up until December 31, 2017, and had undergone curative surgery. We proposed two prognostic models, with different combinations of predictors. The first model used only traditional clinical features. The second model included several colon cancer site-specific factors (circumferential resection margin, perineural invasion, obstruction, and perforation), in addition to the traditional features. Both prediction models were developed by using a Cox proportional hazards model. Furthermore, we investigated whether race is a significant predictor of survival in colon cancer patients by using Model 1 on the Surveillance, Epidemiology, and End Results (SEER) cancer registry dataset.

resultsThe proposed models displayed a robust prediction performance (all Harrell's c-index >0.8). For both the calibration and validation steps, the differences between the predicted and observed mortality were mostly less than 5%.

conclusionsThe prediction model (Model 1) is an effective predictor of survival regardless of the ethnic background of patients and can potentially help to provide better prediction of colon cancer-specific survival outcomes, thus allowing physicians to improve treatment plans.

Indexed as

Colonic NeoplasmsHumansNeoplasm StagingPrognosisProportional Hazards ModelsRegistriesSEER ProgramTaiwan

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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.