Evidence map›Paper›PMID 37035165›Full record

ArticleFrontiers in oncology2023

Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy.

Feng Xu, Chuang Zhu, Zhihao Wang, Lei Zhang, Haifeng Gao, Zhenhai Ma, Yue Gao, Yang Guo, Xuewen Li, Yunzhao Luo and 9 more

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

Article in Frontiers in oncology, 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
0.3field-weighted citation impact, top 37% 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, 2 citations in OpenAlex.

  1. 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

19 authors at 4 institutions in 1 country.

Feng XuDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Chuang ZhuSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Zhihao WangSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Lei ZhangDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Haifeng GaoBreast Disease Prevention and Treatment Center, Haidian Maternal and Child Health Hospital, Beijing, China.
Zhenhai MaDepartment of General Surgery , Beijing Huairou Hospital, Beijing, China.
Yue GaoDepartment of General Surgery , Beijing Huairou Hospital, Beijing, China.
Yang GuoDepartment of Breast Surgery, Beijing Yanqing District Maternal and Child Health Care Hospital, Beijing, China.
Xuewen LiDepartment of General Surgery, Beijing Pinggu Hospital, Beijing, China.
Yunzhao LuoDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Mengxin LiDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Guangqian ShenDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
He LiuDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Yanshuang LiDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Chao ZhangDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Jianxiu CuiDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Jie LiDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Hongchuan JiangDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Jun LiuDepartment of Breast Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Capital Medical University · CNBeijing Chuiyangliu Hospital · CNBeijing University of Posts and Telecommunications · CNHainan Maternal and Child Health Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy. Materials and methods: The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged ≥18 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from Beijing Chao-Yang Hospital were randomly assigned (8:2) to the training (IDBCS development) and internal validation (performance evaluation of the IDBCS) datasets. Diagnostic performance was further assessed with internal and prospective validation datasets from Beijing Chao-Yang Hospital; further external validation was carried out with datasets from 5 primary care hospitals. Diagnostic accuracies, sensitivities, specificities, and positive and negative predictive values for IDBCS and endoscopists (expert, competent, or trainee) in the detection of malignant lesions were obtained by the Clopper-Pearson method. Results: Totally 11305 ductoscopy images in 1072 patients were utilized for developing and testing the IDBCS. Area under the curves (AUCs) in breast cancer detection were 0·975 (95%CI 0·899-0·998) and 0·954 (95%CI 0·925-0·975) in the internal validation and prospective datasets, respectively, and ranged between 0·922 (95%CI 0·866-0·960) and 0·965 (95%CI 0·892-0·994) in the 5 external validation datasets. The IDBCS had superior diagnostic accuracy compared with expert (0.912 [95%CI 0.839-0.959] vs 0.726 [0.672-0.775]; p<0.001), competent (0.699 [95%CI 0.645-0.750], p<0.001), and trainee (0.703 [95%CI 0.648-0.753], p<0.001) endoscopists. Conclusions: IDBCS outperforms clinical oncologists, achieving high accuracy in diagnosing breast cancer with PND. The novel system could help endoscopists improve their diagnostic efficacy in breast cancer diagnosis.

Indexed as

breast cancerdeep learningdiagnosisductoscopypathological nipple discharge

Identifiers

PMID37035165
PMCPMC10073663
OpenAlexW4353086453

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.