ArticleCancers2020
Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Development and validation of an interpretable medical deep foundation model for predicting occult lymph node metastasis in early-stage small cell lung cancer: a multicenter retrospective diagnostic study.Translational lung cancer research · 2026Article
- Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer usingClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Separability of histopathology foundation model embeddings of stimulated Raman histology images for lung carcinoma subtyping.Journal of Cancer · 2026Article
- Fucoidan for Lung Cancer Therapy: A Review of Classification, Mechanisms, and Preclinical Studies.ACS omega · 2025Review
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Machine Learning Models Using General and Tissue-Specific Feature Extractors for Accurate Subtyping of Biopsy Samples: Advancing Lung Cancer Diagnosis in Latin America.JTO clinical and research reports · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- EMI-LTI: An enhanced integrated model for lung tumor identification using Gabor filter and ROI.MethodsX · 2025Article
- Classification of lung cancer severity using gene expression data based on deep learning.BMC medical informatics and decision making · 2025Article
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Unravelling the role of tumor microenvironment responsive nanobiomaterials in spatiotemporal controlled drug delivery for lung cancer therapy.Drug delivery and translational research · 2025Review
- Review
- Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.Cell reports. Medicine · 2024Article
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry.Journal of personalized medicine · 2024Review
- Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.BMC oral health · 2024Article
- Demographic bias in misdiagnosis by computational pathology models.Nature medicine · 2024Article
- Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase.ACS omega · 2024Review
- Development of an algorithm to identify small cell lung cancer patients in claims databases.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.
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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.