Evidence map›Paper›PMID 27936129›Full record

ArticlePloS one2016

Characteristics of Differently Located Colorectal Cancers Support Proximal and Distal Classification: A Population-Based Study of 57,847 Patients.

Jiao Yang, Xiang Lin Du, Shu Ting Li, Bi Yuan Wang, Yin Ying Wu, Zhe Ling Chen, Meng Lv, Yan Wei Shen, Xin Wang, Dan Feng Dong and 5 more

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 3 pooled it
–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

40 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

15 authors.

Jiao YangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Xiang Lin DuDepartment of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX, United States of America.
Shu Ting LiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Bi Yuan WangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Yin Ying WuDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Zhe Ling ChenDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Meng LvDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Yan Wei ShenDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Xin WangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Dan Feng DongDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Dan LiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Fan WangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
En Xiao LiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Min YiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.
Jin YangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an city, Shaanxi Province, China.ORCID http://orcid.org/0000-0001-7754-7723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt has been suggested that colorectal cancer be regarded as several subgroups defined according to tumor location rather than as a single entity. The current study aimed to identify the most useful method for grouping colorectal cancer by tumor location according to both baseline and survival characteristics.

methodsCases of pathologically confirmed colorectal adenocarcinoma diagnosed from 2000 to 2012 were identified from the Surveillance, Epidemiology, and End Results database and categorized into three groups: right colon cancer (RCC), left colon cancer (LCC), and rectal cancer (ReC). Adjusted hazard ratios for known predictors of disease-specific survival (DSS) in colorectal cancer were obtained using a Cox proportional hazards regression model.

resultsThe study included 57847 patients: 43.5% with RCC, 37.7% with LCC, and 18.8% with ReC. Compared with LCC and ReC, RCC was more likely to affect old patients and women, and to be at advanced stage, poorly differentiated or un-differentiated, and mucinous. Patients with LCC or ReC had better DSS than those with RCC in subgroups including stage III or IV disease, age ≤70 years and non-mucinous adenocarcinoma. Conversely, patients with LCC or ReC had worse DSS than those with RCC in subgroups including age ˃70 years and mucinous adenocarcinoma.

conclusionsRCC differed from both LCC and ReC in several clinicopathologic characteristics and in DSS. It seems reasonable to group colorectal cancer into right-sided (i.e., proximal) and left-sided (i.e., distal) ones.

Indexed as

AdenocarcinomaAdolescentAdultAgedAged, 80 and overColonic NeoplasmsColorectal NeoplasmsDisease-Free SurvivalFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisProportional Hazards ModelsRectal Neoplasms

Identifiers

PMID27936129
PMCPMC5147913

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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.