Evidence map›Paper›PMID 37926760›Full record

ArticleBreast cancer research and treatment2024

A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles.

Zeina Al Masry, Romain Pic, Clément Dombry, Chrisine Devalland

Open access · greenAbstract read
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In one paragraph

Article in Breast cancer research and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Review
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

4 authors at 2 institutions in 1 country.

Zeina Al MasrySUPMICROTECH, CNRS, institut FEMTO-ST, 24 rue Alain Savary, 25000, Besançon, France. zeina.almasry@femto-st.fr.ORCID http://orcid.org/0000-0002-6673-0140
Romain PicUniversité de Franche-Comté, CNRS, LmB, 25000, Besançon, France.
Clément DombryUniversité de Franche-Comté, CNRS, LmB, 25000, Besançon, France.
Chrisine DevallandService d'anatomie et cytologie pathologiques, Hôpital Nord Franche-Comté, 100 Route de Moval, 90400, Trévenans, France.
Centre National de la Recherche Scientifique · FRL'Hôpital Nord Franche-Comté · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.

methodsA retrospective study between 2012 and 2020 with 333 cases that underwent an ODX assay from three hospitals in the Bourgogne Franche-Comté region (France) was conducted. Clinical and pathological reports were used to collect the data. A methodology based on distributional random forest was developed to predict the ODX score classes (ODX [Formula: see text] and ODX [Formula: see text]) using 9 clinico-pathological characteristics. This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.

resultsThe mean age of participants is 56.9 years old. We have correctly classified [Formula: see text] of patients in low risk and [Formula: see text] of patients in high risk. The overall accuracy is [Formula: see text]. The proportion of low risk correct predicted value (PPV) is [Formula: see text]. The percentage of high risk correct predicted value (NPV) is approximately [Formula: see text]. The F1-score and the Area Under Curve (AUC) are of 0.87 and 0.759, respectively.

conclusionThe proposed methodology makes it possible to predict the distribution of the ODX score for a patient. This prediction is reinforced by the determination of a family of known patients with follow-up of identical scores. The use of this methodology with the pathologist's expertise on the different histological and immunohistochemical characteristics has a clinical impact to help oncologist in decision-making regarding breast cancer therapy.

Indexed as

Breast NeoplasmsBreastFemaleGene Expression ProfilingHumansMiddle AgedNeoplasm Recurrence, LocalPrognosisRetrospective StudiesBreast CancerClinico-pathological dataDistributional Random ForestMachine LearningOncotype DX

Identifiers

PMID37926760
OpenAlexW4324107072

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.