ArticleScientific reports2021
Deep learning classification of lung cancer histology using CT images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 3 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 3 syntheses or guidelines pooled it, 246 citations in OpenAlex.
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.European radiology · 2023Trial
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.Scientific reports · 2026Article
- Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.Abdominal radiology (New York) · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Trends and challenges in imaging research of Parkinson's disease: a 10-year bibliometric analysis.Psychoradiology · 2026Article
- Foundation model based prediction of lung cancer survival using temporal changes in dual time point CT scans.Scientific reports · 2025Article
- Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images.Journal of imaging informatics in medicine · 2025Article
- Explainable convolutional neural network architectures for high-performance taxonomic classification of gasteroid macrofungi.Scientific reports · 2025Article
- Deep learning-based classification of pleural malignancy using medical thoracoscopic images.Translational lung cancer research · 2025Article
- Enriched lung cancer classification approach using an optimized hybrid deep learning approach.Scientific reports · 2025Article
- Improving lung cancer detection with enhanced convolutional sequential networks.Scientific reports · 2025Article
- AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration.Cell reports. Medicine · 2025Article
- Medical digital twins: enabling precision medicine and medical artificial intelligence.The Lancet. Digital health · 2025Review
- MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images.BMC medical imaging · 2025Article
- Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.
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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.