Evidence map›Paper›PMID 36097806›Full record

ArticleCancer research and treatment2023

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

Young-Gon Kim, In Hye Song, Seung Yeon Cho, Sungchul Kim, Milim Kim, Soomin Ahn, Hyunna Lee, Dong Hyun Yang, Namkug Kim, Sungwan Kim and 8 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer research and treatment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. 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

18 authors at 10 institutions in 1 country.

Young-Gon KimTransdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Korea.
In Hye SongDepartment of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Seung Yeon ChoInterdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, Korea.
Sungchul KimDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Milim KimDepartment of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Soomin AhnDepartment of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Hyunna LeeHealth Innovation Big Data Center, Asan Institute of Life Science, Asan Medical Center, Seoul, Korea.
Dong Hyun YangDepartment of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Namkug KimDepartment of Convergence Medicine, Asan Institute of Life Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sungwan KimTransdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Korea.
Taewoo KimGraduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Daeyoung KimGraduate School of AI, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Jonghyeon ChoiKnowledge of AI Lab, NCSOFT, Seongnam, Korea.
Ki-Sun LeeMedical Science Research Center, Ansan Hospital, Korea University College of Medicine, Ansan, Korea.
Minuk MaSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Minki JoSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
So Yeon ParkDepartment of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Gyungyub GongDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Korea Advanced Institute of Science and Technology · KRAsan Medical Center · KRSeoul National University Bundang Hospital · KRSeoul National University · KRAnsan University · KRCatholic University of Korea · KRNCSOFT (South Korea) · KRSeoul National University Hospital · KRUlsan College · KRUniversity of Ulsan · KR

Funding

Korea Health Industry Development InstituteMinistry of Health and Welfare HI18C0022
6 · The paper itself

Abstract

purposeAssessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. Materials and Methods: A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study.

resultsThe top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis.

conclusionIn this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.

Indexed as

Breast NeoplasmsDeep LearningAlgorithmsFemaleHumansLymphatic MetastasisLymph NodesSentinel Lymph Node BiopsyBreast neoplasmsClassificationDeep learningFrozen sectionsMetastasisNeoplasm metastasisSentinel lymph node

Identifiers

PMID36097806
PMCPMC10101783
OpenAlexW4294792546

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.