ArticleEuropean radiology2018
Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 3 of them syntheses that pooled it.
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Who cites it
105 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- PET radiomics for histologic subtype classification of non-small cell lung cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Icotinib, an EGFR tyrosine kinase inhibitor, as adjuvant therapy for patients with stage IIA-IIIA EGFR-mutant non-small-cell lung adenocarcinoma: a multicenter, open-label, single-arm, phase II study (ICAPE).Investigational new drugs · 2023Trial
- Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.Journal of healthcare engineering · 2021Trial
- 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
- Article
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- 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
- Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.Oncology letters · 2025Article
- Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility.Discover oncology · 2025Article
- Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.Frontiers in oncology · 2025Article
- Article
- Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization.Physics and imaging in radiation oncology · 2024Article
- Article
- An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules.Quantitative imaging in medicine and surgery · 2023Article
45 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
objectivesTo distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature
methodsThis study involved 129 patients with non-small cell lung cancer (NSCLC) (81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts.
resultsFive features were selected to construct the radiomic signature for histologic subtype classification. The performance of the radiomic signature to distinguish between lung ADC and SCC in both training and validation cohorts was good, with an AUC of 0.905 (95% confidence interval [CI]: 0.838 to 0.971), sensitivity of 0.830, and specificity of 0.929. In the validation cohort, the radiomic signature showed an AUC of 0.893 (95% CI: 0.789 to 0.996), sensitivity of 0.828, and specificity of 0.900.
conclusionsA unique radiomic signature was constructed for use as a diagnostic factor for discriminating lung ADC from SCC. Patients with NSCLC will benefit from the proposed radiomic signature. KEY POINTS: • Machine learning can be used for auxiliary distinguish in lung cancer. • Radiomic signature can discriminate lung ADC from SCC. • Radiomics can help to achieve precision medical treatment.
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