ArticleFrontiers in genetics2020
DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis From Histopathology Images.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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
31 citing papers in PubMed.
- Opportunities and challenges of immunotherapy for patients with resectable and potentially resectable stage III non-small cell lung cancer.Cancer biology & medicine · 2026Review
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.NPJ precision oncology · 2025Article
- Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.BJC reports · 2025Article
- A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.Scientific reports · 2025Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.BMC bioinformatics · 2025Article
- Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.Frontiers in oncology · 2025Article
- Predicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning.Cancers · 2024Article
- Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.Cell reports. Medicine · 2024Article
- A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.Scientific reports · 2024Article
- Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records.Translational lung cancer research · 2023Article
- Review
- Article
- CT-derived body composition associated with lung cancer recurrence after surgery.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.Biomolecules · 2022Article
- Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.Nature communications · 2022Article
- Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.International journal of computer assisted radiology and surgery · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is critical for patients who cannot undergo eradicable surgery to predict the risk of lung cancer recurrence and metastasis; therefore, the physicians can design the appropriate adjuvant therapy plan. However, traditional circulating tumor cell (CTC) detection or next-generation sequencing (NGS)-based methods are usually expensive and time-inefficient, which urge the need for more efficient computational models. In this study, we have established a convolutional neural network (CNN) framework called DeepLRHE to predict the recurrence risk of lung cancer by analyzing histopathological images of patients. The steps for using DeepLRHE include automatic tumor region identification, image normalization, biomarker identification, and sample classification. In practice, we used 110 lung cancer samples downloaded from The Cancer Genome Atlas (TCGA) database to train and validate our CNN model and 101 samples as independent test dataset. The area under the receiver operating characteristic (ROC) curve (AUC) for test dataset was 0.79, suggesting a relatively good prediction performance. Our study demonstrates that the features extracted from histopathological images could be well used to predict lung cancer recurrence after surgical resection and help classify patients who should receive additional adjuvant therapy.
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.