ArticleFrontiers in oncology2020
CT-Based Deep Learning Model for Invasiveness Classification and Micropapillary Pattern Prediction Within Lung Adenocarcinoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The presence of a cribriform pattern is related to poor prognosis in lung adenocarcinoma after surgical resection: A meta-analysis.General thoracic and cardiovascular surgery · 2024Pooled it
- GAN-generated target reconstruction CT radiomics for prediction of the novel IASLC grading of pulmonary adenocarcinoma.Journal of thoracic disease · 2026Article
- Retrieval‑augmented large language models for depression screening and suicide risk stratification.BMC psychiatry · 2026Article
- Pancreatic cancer burden in China from 1990 to 2023: an analysis based on the global burden of disease study 2023.Frontiers in endocrinology · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS-MIA discrimination on pulmonary CT.Frontiers in medicine · 2026Article
- Integrating deep learning and radiomics in the differentiation of major histological subtypes of invasive non-mucinous lung adenocarcinoma using positron emission tomography and computed tomography.Translational lung cancer research · 2025Article
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
- State-of-the-art artificial intelligence methods for pre-operative planning of cardiothoracic surgery and interventions: a narrative review.Journal of thoracic disease · 2025Review
- Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.Scientific reports · 2025Article
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizingBMC cancer · 2024Article
- Article
- Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less.Frontiers in oncology · 2024Article
- Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules.Translational lung cancer research · 2023Article
- Noninvasive diagnosis of pulmonary nodules using a circulating tsRNA-based nomogram.Cancer science · 2023Article
- [Advances in the Study of Invasive Non-mucinous Adenocarcinoma with Different Pathological Subtypes].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Review
- A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma.Annals of translational medicine · 2022Article
- Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.Frontiers in oncology · 2022Article
- Factors associated with lymph node metastasis upstage after resection for patients with micropapillary lung adenocarcinoma.Thoracic cancer · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
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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.