ArticleCancers2021
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Evaluation of high-grade growth patterns in invasive adenocarcinomas manifesting as ground-glass lesions via clinical and qualitative and quantitative computed tomography features.Quantitative imaging in medicine and surgery · 2026Article
- Integrating radiomics and deep learning for enhanced prediction of high-grade patterns in stage IA lung adenocarcinoma.Translational lung cancer research · 2025Article
- Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.Cancer research and treatment · 2025Article
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- Prognostic effect of CD74 and development of a radiomic model for predicting CD74 expression in non-small cell lung cancer.Frontiers in medicine · 2025Article
- Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.La Radiologia medica · 2024Article
- Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.Journal of cancer research and clinical oncology · 2023Article
- Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence in lung cancer: Application and future thinking.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 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
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal-training and internal-validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16-2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.
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