Evidence map›Paper›PMID 31134367›Full record

ArticleEuropean radiology2019

Optoacoustic imaging of the breast: correlation with histopathology and histopathologic biomarkers.

Gisela L G Menezes, Ritse M Mann, Carla Meeuwis, Bob Bisschops, Jeroen Veltman, Philip T Lavin, Marc J van de Vijver, Ruud M Pijnappel

Abstract read
In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. 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

8 authors.

Gisela L G MenezesSeno Medical Instruments, 8023 Vantage Dr. #1000, San Antonio, TX, 78230, USA. giselalgm@gmail.com.
Ritse M MannDepartment of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
Carla MeeuwisDepartment of Radiology, Rijnstate Hospital, Wagnerlaan 55, 6815 AD, Arnhem, The Netherlands.
Bob BisschopsDepartment of Radiology, Albert Schweitzer Hospital, Albert Schweitzerplaats 25, 3318 AT, Dordrecht, The Netherlands.
Jeroen VeltmanDepartment of Radiology, ZGT, Zilvermeeuw 1, 7609 PP, Almelo, The Netherlands.
Philip T LavinBoston Biostatistics Research Foundation, 3 Cahill Park Drive, Framingham, MA, 01702, USA.
Marc J van de VijverDepartment of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
Ruud M PijnappelDepartment of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThis study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes. MATERIALS AND

methodsAll 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and histopathological findings. Kruskal-Wallis tests were used to analyze the relationship between US and OA/US features and molecular subtypes of breast cancer. If a significant relationship was found, additional Wilcoxon-Mann-Whitney tests were used to identify the differences between molecular subtype groups.

resultsUS sound transmission helped to differentiate between LUMA and LUMB, LUMB and TNBC, and LUMB and all other molecular subtypes combined (p values < 0.05). Regarding OA/US features, the sum of internal features helped to differentiate between TNBC and HER2-enriched subtypes (p = 0.049). Internal vessels (p = 0.025), sum of all internal features (p = 0.019), and sum of internal and external features (p = 0.028) helped to differentiate between LUMA and LUMB. All internal features, the sum of all internal features, the sum of all internal and external features, and the ratio of internal and external features helped to differentiate between LUMA and TNBC. The same features also helped to differentiate between LUMA and TNBC from other molecular subtypes (p values < 0.05).

conclusionsThe use of OA/US might help radiologists to better differentiate between breast cancer molecular subtypes. Further studies need to be carried out in order to validate these results. KEY POINTS: • The combination of functional and morphologic information provided by optoacoustic imaging (OA) combined with gray-scale US helped to differentiate between breast cancer molecular subtypes.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorBreastBreast NeoplasmsDiagnosis, DifferentialFemaleHumansMiddle AgedPhotoacoustic TechniquesRetrospective StudiesUltrasonography, MammaryBiomarkers, TumorBreast neoplasmsMolecular imagingOptoacoustic technologies

Identifiers

PMID31134367
PMCPMC6828639

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.