Evidence map›Paper›PMID 37274760›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2023

Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.

Jules Scholler, Diana Mandache, Marie Christine Mathieu, Aïcha Ben Lakhdar, Marie Darche, Tual Monfort, Claude Boccara, Jean-Christophe Olivo-Marin, Kate Grieve, Vannary Meas-Yedid and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Virtual histology imaging of lymph nodesCancer biology & medicine · 2026
    Observational
  5. Article
  6. Article
  7. Transforming Intraoperative Breast Cancer Diagnosis through D-FFOCT and AI Integration.Asian Pacific journal of cancer prevention : APJCP · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
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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

12 authors at 4 institutions in 1 country.

Jules SchollerPSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France.ORCID https://orcid.org/0000-0002-1890-5938
Diana MandacheAQUYRE Bioscences-LLTech SAS, Paris, France.ORCID https://orcid.org/0000-0002-7160-4093
Marie Christine MathieuGustave Roussy Cancer Campus, Department of Medical Biology and Pathology, Villejuif, France.
Aïcha Ben LakhdarSCM Bichat, Paris, France.
Marie DarcheSorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France.
Tual MonfortPSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France.
Claude BoccaraPSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France.ORCID https://orcid.org/0000-0002-9603-0700
Jean-Christophe Olivo-MarinInstitut Pasteur, Bioimage Analysis Unit, Paris, France.
Kate GrieveSorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France.
Vannary Meas-YedidInstitut Pasteur, Bioimage Analysis Unit, Paris, France.
Emilie Benoit A la GuillaumeAQUYRE Bioscences-LLTech SAS, Paris, France.
Olivier ThouveninPSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France.ORCID https://orcid.org/0000-0003-4853-7555
Centre National de la Recherche Scientifique · FRInstitut Pasteur · FRInstitut Gustave Roussy · FRLight for Life Technologies (France) · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration. Approach: We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual Results: Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level ( Conclusions: Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.

Indexed as

automated diagnosisdynamic optical coherence tomographylabel-free histopathologymachine learningmetabolic imaging

Identifiers

PMID37274760
PMCPMC10234284
OpenAlexW4379016377

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.