Evidence map›Paper›PMID 37563337›Full record

ArticleAnnals of surgical oncology2023

Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status.

Kevin A Chen, Kathryn E Kirchoff, Logan R Butler, Alexa D Holloway, Muneera R Kapadia, Cherie M Kuzmiak, Stephanie M Downs-Canner, Phillip M Spanheimer, Kristalyn K Gallagher, Shawn M Gomez

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In one paragraph

Article in Annals of surgical oncology, 2023. 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. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Kevin A ChenDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Kathryn E KirchoffJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Logan R ButlerDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Alexa D HollowayDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Muneera R KapadiaDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Cherie M KuzmiakDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Stephanie M Downs-CannerDepartment of Surgery, Breast Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Phillip M SpanheimerDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Kristalyn K GallagherDivision of Surgical Oncology, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. kristalyn_gallagher@med.unc.edu.
Shawn M GomezJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. smgomez@unc.edu.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
UNC Integrated Translational Oncology Program (UNC-iTOP)T32CA244125 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WILLIAM Y. KIM, Jen Jen Yeh · 2019 to 2026
$3.9M
NCI NIH HHS P30 CA008748NCI NIH HHS T32 CA244125NIH HHS T32-CA244125
6 · The paper itself

Abstract

backgroundIntraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop an artificial intelligence model to predict the pathologic margin status of resected breast tumors using specimen mammography.

methodsA dataset of specimen mammography images matched with pathologic margin status was collected from our institution from 2017 to 2020. The dataset was randomly split into training, validation, and test sets. Specimen mammography models pretrained on radiologic images were developed and compared with models pretrained on nonmedical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).

resultsThe dataset included 821 images, and 53% had positive margins. For three out of four model architectures tested, models pretrained on radiologic images outperformed nonmedical models. The highest performing model, InceptionV3, showed sensitivity of 84%, specificity of 42%, and AUROC of 0.71. Model performance was better among patients with invasive cancers, less dense breasts, and non-white race.

conclusionsThis study developed and internally validated artificial intelligence models that predict pathologic margins status for partial mastectomy from specimen mammograms. The models' accuracy compares favorably with published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could more precisely guide the extent of resection, potentially improving cosmesis and reducing reoperations.

Indexed as

Breast NeoplasmsArtificial IntelligenceBreastFemaleHumansMammographyMastectomyMastectomy, SegmentalRetrospective Studies

Identifiers

PMID37563337
PMCPMC10592216

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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.