Evidence map›Paper›PMID 27769212›Full record

ArticleBMC cancer2016

Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia.

Hui Miao, Mikael Hartman, Helena M Verkooijen, Nur Aishah Taib, Hoong-Seam Wong, Shridevi Subramaniam, Cheng-Har Yip, Ern-Yu Tan, Patrick Chan, Soo-Chin Lee and 1 more

Abstract read
In one paragraph

Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia.International journal of environmental research and public health · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018
    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

11 authors.

Hui MiaoSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore. ephmh@nus.edu.sg.
Mikael HartmanSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore.
Helena M VerkooijenImaging Division, University Medical Center Utrecht, PO Box 85500, 3508, GA, Utrecht, The Netherlands.
Nur Aishah TaibDepartment of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Hoong-Seam WongClinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia.
Shridevi SubramaniamClinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia.
Cheng-Har YipDepartment of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Ern-Yu TanDepartment of Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore.
Patrick ChanDepartment of Surgery, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore.
Soo-Chin LeeDepartment of Hematology Oncology, National University Cancer Institute, National University Health System, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Nirmala Bhoo-PathyClinical Epidemiology Unit, National Clinical Research Centre, Jalan Pahang, 50586, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancerMath is a set of web-based prognostic tools which predict nodal status and survival up to 15 years after diagnosis of breast cancer. This study validated its performance in a Southeast Asian setting.

methodsUsing Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).

resultsThe median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).

conclusionsThe discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.

Indexed as

Area Under CurveAsia, SoutheasternBreast NeoplasmsFemaleHumansKaplan-Meier EstimateNeoplasm MetastasisNeoplasm StagingPatient Outcome AssessmentPopulation SurveillancePrognosisRegistriesAsiaBreast cancerCancerMathPrognostic model

Identifiers

PMID27769212
PMCPMC5073834

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.