Evidence map›Paper›PMID 38244167›Full record

ArticleBreast cancer research and treatment2024

Performance of a novel spectroscopy-based tool for adjuvant therapy decision-making in hormone receptor-positive breast cancer: a validation study.

R Charles Coombes, Christina Angelou, Zamzam Al-Khalili, William Hart, Darius Francescatti, Nicholas Wright, Ian Ellis, Andrew Green, Emad Rakha, Sami Shousha and 3 more

Erratum issuedOpen access · hybridAbstract readValidation Study
PubMed Full text at PMC Publisher
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. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 2 countries.

R Charles CoombesImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Christina AngelouImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Zamzam Al-KhaliliImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
William HartImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Darius FrancescattiRush Medical College, Chicago, USA.
Nicholas WrightBarts Cancer Institute, London, UK.
Ian EllisNottingham University Hospital, Nottingham, UK.
Andrew GreenNottingham University Hospital, Nottingham, UK.
Emad RakhaNottingham University Hospital, Nottingham, UK.
Sami ShoushaImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Hemmel AmraniaImperial College London, South Kensington Campus, London, SW7 2AZ, UK. Hemmel.amrania@imperial.ac.uk.ORCID http://orcid.org/0009-0000-6872-8119
Chris C PhillipsImperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Carlo PalmieriUniversity of Liverpool, Liverpool, UK.
Imperial College London · GBBarts Health NHS Trust · GBRush University Medical Center · USUniversity of Liverpool · GB

Funding

Cancer Research UK 28503National Institute for Health and Care Research RDB01
6 · The paper itself

Abstract

purposeDigistain Index (DI), measured using an inexpensive mid-infrared spectrometer, reflects the level of aneuploidy in unstained tissue sections and correlates with tumor grade. We investigated whether incorporating DI with other clinicopathological variables could predict outcomes in patients with early breast cancer.

methodsDI was calculated in 801 patients with hormone receptor-positive, HER2-negative primary breast cancer and ≤ 3 positive lymph nodes. All patients were treated with systemic endocrine therapy and no chemotherapy. Multivariable proportional hazards modeling was used to incorporate DI with clinicopathological variables to generate the Digistain Prognostic Score (DPS). DPS was assessed for prediction of 5- and 10-year outcomes (recurrence, recurrence-free survival [RFS] and overall survival [OS]) using receiver operating characteristics and Cox proportional hazards regression models. Kaplan-Meier analysis evaluated the ability of DPS to stratify risk.

resultsDPS was consistently highly accurate and had negative predictive values for all three outcomes, ranging from 0.96 to 0.99 at 5 years and 0.84 to 0.95 at 10 years. DPS demonstrated statistically significant prognostic ability with significant hazard ratios (95% CI) for low- versus high-risk classification for RFS, recurrence and OS (1.80 [CI 1.31-2.48], 1.83 [1.32-2.52] and 1.77 [1.28-2.43], respectively; all P < 0.001).

conclusionDPS showed high accuracy and predictive performance, was able to stratify patients into low or high-risk, and considering its cost and rapidity, has the potential to offer clinical utility.

Indexed as

Breast NeoplasmsReceptors, EstrogenReceptors, ProgesteroneAdultAgedAged, 80 and overChemotherapy, AdjuvantClinical Decision-MakingErb-b2 Receptor Tyrosine KinasesFemaleHumansKaplan-Meier EstimateMiddle AgedNeoplasm Recurrence, LocalPrognosisProportional Hazards ModelsERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesReceptors, EstrogenReceptors, ProgesteroneBreast cancerDistant recurrenceMortalityPrognosis

Identifiers

PMID38244167
PMCPMC11101376
OpenAlexW4391053574

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.