ReviewJournal of Cancer2020
Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.
Review in Journal of Cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Research progress in computer-aided diagnosis systems for lung cancer.NPJ digital medicine · 2025Review
- Deep Clustering-Based Immunotherapy Prediction for Gastric Cancer mRNA Vaccine Development.International journal of molecular sciences · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification.Heliyon · 2025Article
- Feasibility study of single-image super-resolution scanning system based on deep learning for pathological diagnosis of oral epithelial dysplasia.Frontiers in medicine · 2025Article
- [Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.Nature communications · 2024Article
- Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.Cancer medicine · 2023Article
- The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity.Diagnostics (Basel, Switzerland) · 2022Review
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms.Journal of healthcare engineering · 2022Article
- Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring.Cancers · 2021Review
- Antitumor activity via apoptotic cell death pathway of water soluble copper(II) complexes: effect of the diamino unit on selectivity against lung cancer NCI-H460 cell line.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2021Article
- Artificial intelligence and computational pathology.Laboratory investigation; a journal of technical methods and pathology · 2021Review
- AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features.Sensors (Basel, Switzerland) · 2020Article
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the main causes of cancer-related death in the world. The identification and characteristics of malignant cells are essential for the diagnosis and treatment of primary or metastatic cancers. Deep learning is a new field of artificial intelligence, which can be used for computer aided diagnosis and scientific research of lung cancer pathology by analyzing and learning through establishment and simulation of human brain. In this review, we will introduce the application, progress and problems of deep learning in pathology of lung cancer and make prospects for its future development.
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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.