ArticleMedical & biological engineering & computing2021
Lung cancer histology classification from CT images based on radiomics and deep learning models.
Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Association Between Incense Burning and the Risk of Lung Cancer in Asian Population: Meta-Analysis of Nine Case-Control Studies.Cancer reports (Hoboken, N.J.) · 2024Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- Diagnostic Accuracy of Artificial Intelligence Models for Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of Lung-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study.JMIR bioinformatics and biotechnology · 2025Article
- Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study.Scientific reports · 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
- Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility.Discover oncology · 2025Article
- Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Ferroptosis-related gene signature for predicting prognosis and identifying potential therapeutic drug in EGFR wild-type lung adenocarcinoma.Communications biology · 2024Article
- An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans.Bioengineering (Basel, Switzerland) · 2024Article
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- Research on the electromyography-based pattern recognition for inter-limb coordination in human crawling motion.Frontiers in neuroscience · 2024Article
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- A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.Medicina (Kaunas, Lithuania) · 2023Article
- A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region.PloS one · 2023Article
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Authors and funding
7 authors.
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Abstract
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor's CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc = 0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM + Inception outperformed experts by 7-25% (p < 0.05). The proposed methodology does not require detailed segmentation of the tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making. Lung cancer histology classification from CT images based on CNN + LSTM.
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