Evidence map›Paper›PMID 19828405›Full record

ReviewClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2009

Prognostic and predictive factors and genetic analysis of early breast cancer.

Miguel Martín, Fernando González Palacios, Javier Cortés, Juan de la Haba, José Schneider

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

5 authors.

Miguel MartínDepartment of Medical Oncology, Hospital General Universitario Gregorio Marañón, Madrid, Spain. mmartin@geicam.org
Fernando González Palacios
Javier Cortés
Juan de la Haba
José Schneider

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The great heterogeneity of breast cancer makes it impossible to firmly predict which patients with early-stage tumours will or will not need systemic treatments according to the conventional prognostic factors currently employed. In fact, a substantial percentage of patients receive medical treatment for a disease that will not relapse, while another proportion of patients regarded as having good prognostic factors according to the classic criteria do not receive treatment and suffer disease relapse. Considering that most oncological treatments have short- and long-term toxic effects, new methods capable of offering a more precise prognosis need to be developed. The individualisation of the diagnosis of patients with breast cancer based on molecular and gene expression studies is bringing about a veritable revolution in our understanding of the biology of the disease. The new molecular classification of breast cancer, based on these profiles, allows us to establish a prognosis according to the genetic characteristics of each tumour. Such individualisation of the diagnosis of patients with breast cancer will lead to the application of more specific treatments, thereby improving patient survival with lesser toxicity and increased economic savings. Of the different genetic analytical tests available, MammaPrint has been shown to be the option offering the most information on the behaviour of early breast cancer; as a result, it is the most useful technique in deciding the need for oncological treatment as a complement to surgery.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleHumansNeoplasm Recurrence, LocalNeoplasm StagingPrognosisBiomarkers, Tumor

Identifiers

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.