Evidence map›Paper›PMID 33805407›Full record

ArticleCancers2021

Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry.

Donsuk Pongnikorn, Phichayut Phinyo, Jayanton Patumanond, Karnchana Daoprasert, Pachaya Phothong, Boonying Siribumrungwong

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Prediction model for aortic dissection, aortic aneurysm, and peripheral artery disease.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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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

6 authors.

Donsuk PongnikornDepartment of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand.ORCID 0000-0002-7993-9912
Phichayut PhinyoDepartment of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.ORCID 0000-0002-8543-6254
Jayanton PatumanondCenter for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.
Karnchana DaoprasertCancer Registry Unit, Lampang Cancer Hospital, Lampang 52000, Thailand.
Pachaya PhothongPolicy and Strategy Unit, Lampang Cancer Hospital, Lampang 52000, Thailand.
Boonying SiribumrungwongDivision of Vascular and Endovascular Surgery, Department of Surgery, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prognostic models for breast cancer developed from Western countries performed less accurately in the Asian population. We aimed to develop a survival prediction model for overall survival (OS) and disease-free survival (DFS) for Thai patients with breast cancer. We conducted a prognostic model research using a multicenter hospital-based cancer clinical registry from the Network of National Cancer Institutes of Thailand. All women diagnosed with breast cancer who underwent surgery between 1 January 2010 and 31 December 2011 were included in the analysis. A flexible parametric survival model was used for developing the prognostic model for OS and DFS prediction. During the study period, 2021 patients were included. Of these, 1386 patients with 590 events were available for a complete-case analysis. The newly derived individualized prediction of breast cancer survival or the IPBS model consists of twelve routinely available predictors. The C-statistics from the OS and the DFS model were 0.72 and 0.70, respectively. The model showed good calibration for the prediction of five-year OS and DFS. The IPBS model provides good performance for the prediction of OS and PFS for breast cancer patients. A further external validation study is required before clinical implementation.

Indexed as

adultbreast neoplasmsfemaleprognosisstatistical models

Identifiers

PMID33805407
PMCPMC8037061

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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.