ArticleCancers2024
Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Translational deep learning models for risk stratification to predict prognosis and immunotherapy response in gastric cancer using digital pathology.Journal of translational medicine · 2025Article
- Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review.Cancers · 2025Review
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93,
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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 Socratic graph.