ArticleQuantitative imaging in medicine and surgery2021
Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Decoding the Rotation Effect: A Retrospective Analysis of Lesion Orientation and Its Impact on Wavelet-Based Radiomics Feature Extraction and Lung Cancer Classification.Journal of imaging informatics in medicine · 2026Article
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- The qualitative and quantitative characteristics of serous endometrial carcinoma on MRI: applying a novel nomogram for predicting an aggressive histological type.Frontiers in oncology · 2025Article
- A radiomics-based brain network in T1 images: construction, attributes, and applications.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?Frontiers in oncology · 2024Article
- Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.Quantitative imaging in medicine and surgery · 2023Article
- Multi-task learning-based histologic subtype classification of non-small cell lung cancer.La Radiologia medica · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color.Diagnostics (Basel, Switzerland) · 2022Article
- Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.Quantitative imaging in medicine and surgery · 2022Article
- Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study.Diagnostics (Basel, Switzerland) · 2021Article
- Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients.Frontiers in oncology · 2021Article
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Abstract
backgroundDiscriminating the subtypes of non-small cell lung cancer (NSCLC) based on computed tomography (CT) images is a challenging task for radiologists. Although several machine learning methods such as radiomics, and deep learning methods such as convolutional neural networks (CNNs) have been proposed to explore the problem, large sample sizes are required for effective training, and this may not be easily achieved in single-center datasets.
methodsIn this study, an automated subtype recognition model with capsule net (CapsNet) was developed for the subtype discrimination of NSCLC. CapsNet utilizes an activity vector to record the relative spatial relationship of image elements that can subsequently better delineate the global image characteristics. CT images of 72 adenocarcinoma (AC) and 54 squamous cell carcinoma (SCC) patients were retrospectively collected from a single clinical center. The cancer region on the CT image was manually segmented for every subject by an experienced radiologist, and CapsNet, CNN, and four radiomics models were then used to construct the recognition model.
resultsThe study demonstrated that CapsNet achieved the best discriminative performance (accuracy 81.3%, specificity 80.7%, sensitivity 82.2%) although its area under the curve was just marginally better than that of the optimal random forest (RF) based radiomics model. Not surprisingly, the performance of the CNN was only comparable to the other radiomics models.
conclusionsThis study demonstrated that CapsNet is a viable potential framework for discriminating the subtypes of NSCLC, and its use could be extended to the recognition of other diseases especially in limited single-center datasets.
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