Evidence map›Paper›PMID 26413811›Full record

ArticleOncotarget2015

Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients.

Lijun Shen, Johan van Soest, Jiazhou Wang, Jialu Yu, Weigang Hu, Yutao U T Gong, Vincenzo Valentini, Ying Xiao, Andre Dekker, Zhen Zhang

Abstract readValidation Study
In one paragraph

Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Machine Learning-Based Survival Prediction Tool for Adrenocortical Carcinoma.The Journal of clinical endocrinology and metabolism · 2025
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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

10 authors.

Lijun ShenDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Johan van SoestDepartment of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
Jiazhou WangDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Jialu YuDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
Weigang HuDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Yutao U T GongDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
Vincenzo ValentiniDepartment of Radiation Oncology, Università Cattolica S Cuore, Rome, Italy.
Ying XiaoDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
Andre DekkerDepartment of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
Zhen ZhangDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk of local recurrence (LR), distant metastases (DM) and overall survival (OS) of locally advanced rectal cancer after preoperative chemoradiation can be estimated by prediction models and visualized using nomograms, which have been trained and validated in European clinical trial populations. Data of 277 consecutive locally advanced rectal adenocarcinoma patients treated with preoperative chemoradiation and surgery from Shanghai Cancer Center, were retrospectively collected and used for external validation. Concordance index (C-index) and calibration curves were used to assess the performance of the previously developed prediction models in this routine clinical validation population. The C-index for the published prediction models was 0.72 ± 0.079, 0.75 ± 0.043 and 0.72 ± 0.089 in predicting 2-year LR, DM and OS in the Chinese population, respectively. Kaplan-Meier curves indicated good discriminating performance regarding LR, but could not convincingly discriminate a low-risk and medium-risk group for distant control and OS. Calibration curves showed a trend of underestimation of local and distant control, as well as OS in the observed data compared with the estimates predicted by the model. In conclusion, we externally validated three models for predicting 2-year LR, DM and OS of locally advanced rectal cancer patients who underwent preoperative chemoradiation and curative surgery with good discrimination in a single Chinese cohort. However, the model overestimated the local control rate compared to observations in the clinical cohort. Validation in other clinical cohorts and optimization of the prediction model, perhaps by including additional prognostic factors, may enhance model validity and its applicability for personalized treatment of locally advanced rectal cancer.

Indexed as

Decision Support TechniquesAdenocarcinomaAgedAsian PeopleChemoradiotherapy, AdjuvantChinaDiscriminant AnalysisDisease-Free SurvivalDisease ProgressionFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNeoadjuvant TherapyNeoplasm Recurrence, Localexternal validationnomogrampreoperative chemoradiationrectal cancer

Identifiers

PMID26413811
PMCPMC4742002

What OpenQuestion holds

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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.