ArticleEuropean radiology2021
Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.European radiology · 2024Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Aristolochic acid I accelerates lung adenocarcinoma progression coupled with the upregulation of core oncogenic networks: an integrated network toxicology and experimental study.Frontiers in pharmacology · 2026Article
- Effect of spatial resolution on the diagnostic performance of machine-learning radiomics model in lung adenocarcinoma: comparisons between normal- and high-spatial-resolution imaging for predicting invasiveness.Japanese journal of radiology · 2025Article
- A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.Discover oncology · 2025Review
- GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions.Scientific reports · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence.Frontiers in radiology · 2024Article
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Predicting cervical intraepithelial neoplasia and determining the follow-up period in high-risk human papillomavirus patients.Frontiers in oncology · 2023Article
- Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography.Translational oncology · 2021Article
- Exploration and Strategy Analysis of Mental Health Education for Students in Sports Majors in the Era of Artificial Intelligence.Frontiers in psychology · 2021Article
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN).
methodsEnrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared.
resultsWithout the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01).
conclusionsThe 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.
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