Trial reportJournal of digital imaging2023
Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images.
Trial report in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Real-world evolution of first-line ALK-positive NSCLC management in Spain.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Prediction of infiltration degree of ground-glass nodules using a fusion of CT radiomics and deep learning.Scientific reports · 2026Article
- Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- 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
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- Virtual biopsy through CT imaging: can radiomics differentiate between subtypes of non-small cell lung cancer?La Radiologia medica · 2025Article
- Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.Oncology letters · 2025Article
- Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- Construction and analysis of the invasive prediction model for pulmonary nodules: based on clinical, CT image and DNA methylation characteristics.Journal of thoracic disease · 2025Article
- Radiomics-based optimization of target selection in CT-guided percutaneous lung cancer biopsy: a retrospective study.Frontiers in oncology · 2025Article
- Article
- Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.Frontiers in medicine · 2024Article
- Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung.Cancer management and research · 2024Article
- Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis.Annals of medicine · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-invasive diagnostic method based on radiomic features in patients with non-small cell lung cancer (NSCLC) has attracted attention. This study aimed to develop a CT image-based model for both histological typing and clinical staging of patients with NSCLC. A total of 309 NSCLC patients with 537 CT series from The Cancer Imaging Archive (TCIA) database were included in this study. All patients were randomly divided into the training set (247 patients, 425 CT series) and testing set (62 patients, 112 CT series). A total of 107 radiomic features were extracted. Four classifiers including random forest, XGBoost, support vector machine, and logistic regression were used to construct the classification model. The classification model had two output layers: histological type (adenocarcinoma, squamous cell carcinoma, and large cell) and clinical stage (I, II, and III) of NSCLC patients. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence interval (CI) were utilized to evaluate the performance of the model. Seven features were selected for inclusion in the classification model. The random forest model had the best classification ability compared with other classifiers. The AUC of the RF model for histological typing and clinical staging of NSCLC patients in the testing set was 0.700 (95% CI, 0.641-0.759) and 0.881 (95% CI, 0.842-0.920), respectively. The CT image-based radiomic feature model had good classification ability for both histological typing and clinical staging of patients with NSCLC.
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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.