ArticleNPJ digital medicine2023
An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- [Safety and Clinical Benefit Analysis of No-chest-tube Strategy After Uniportal Video-assisted Thoracoscopic Pulmonary Wedge Resection Based on Propensity Score Matching].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Article
- AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.Journal of cancer research and clinical oncology · 2026Article
- Development and validation of a CT-based habitat radiomics model for predicting pathological grading in non-small cell lung cancer.Frontiers in medicine · 2026Article
- GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation.NPJ digital medicine · 2025Article
- Predicting Invasiveness of Lung Adenocarcinoma from Chest CT with Few-shot Vision-Language Ternary Classification Model.NPJ digital medicine · 2025Article
- Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module.Journal of imaging · 2025Article
- Lung adenocarcinoma subtype classification based on contrastive learning model with multimodal integration.Scientific reports · 2025Article
- Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules.Scientific reports · 2025Article
- Diagnostic accuracy of deep learning for the invasiveness assessment of ground-glass nodules with fine segmentation: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2025Article
- Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Development of an oral cancer detection system through deep learning.BMC oral health · 2024Article
- Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.Biomedical engineering online · 2024Article
- Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.Reproductive biology and endocrinology : RB&E · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- The efficacy of machine learning models in lung cancer risk prediction with explainability.PloS one · 2024Article
- Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.AI (Basel, Switzerland) · 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
10 authors at 6 institutions in 1 country.
Funding
Abstract
Lung cancer screening using computed tomography (CT) has increased the detection rate of small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically meaningful to accurate assessment of the nodule histology by CT scans with advanced deep learning algorithms. However, recent studies mainly focus on predicting benign and malignant nodules, lacking of model for the risk stratification of invasive adenocarcinoma. We propose an ensemble multi-view 3D convolutional neural network (EMV-3D-CNN) model to study the risk stratification of lung adenocarcinoma. We include 1075 lung nodules (≤30 mm and ≥4 mm) with preoperative thin-section CT scans and definite pathology confirmed by surgery. Our model achieves a state-of-art performance of 91.3% and 92.9% AUC for diagnosis of benign/malignant and pre-invasive/invasive nodules, respectively. Importantly, our model outperforms senior doctors in risk stratification of invasive adenocarcinoma with 77.6% accuracy [i.e., Grades 1, 2, 3]). It provides detailed predictive histological information for the surgical management of pulmonary nodules. Finally, for user-friendly access, the proposed model is implemented as a web-based system ( https://seeyourlung.com.cn ).
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.