ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2021
Deep learning model improves radiologists' performance in detection and classification of breast lesions.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024Pooled it
- Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study.European radiology · 2026Article
- A Deep Learning Model Integrating Ultrasound Images and Multidimensional Clinical Information for Differentiating Benign and Malignant Non-Mass Lesions.Diagnostics (Basel, Switzerland) · 2026Article
- The future of intraoperative neuromonitoring (IONM) in spinal surgery.North American Spine Society journal · 2025Article
- Artificial intelligence applications in endometriosis imaging.Abdominal radiology (New York) · 2025Review
- Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Article
- Utility of Volatile Organic Compounds and Electronic Nose Technology for Breast Cancer Detection: A Systematic Review.Breast cancer (Dove Medical Press) · 2025Review
- CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.Tomography (Ann Arbor, Mich.) · 2024Article
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- Article
- The impact of artificial intelligence on the reading times of radiologists for chest radiographs.NPJ digital medicine · 2023Article
- A CT-Based Radiomics Nomogram Model for Differentiating Primary Malignant Melanoma of the Esophagus from Esophageal Squamous Cell Carcinoma.BioMed research international · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveComputer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.
methodsThis study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.
resultsThe sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852
conclusionsThe artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.