ArticleFrontiers in immunology2024
Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 18F-FDG PET/CT Semiquantitative and Radiomic Features for Assessing Pathologic Axillary Lymph Node Status in Clinical Stage I-III Breast Cancer Patients: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Altered Neural Activity in Adolescent Major Depressive Disorder With Nonsuicidal Self-Injury: A Resting-State Functional Magnetic Resonance Imaging Meta-Analysis.Neural plasticity · 2025Pooled it
- Clinical fluorescent probes: mechanisms, advantages and inspirations.Chemical science · 2026Review
- Differential diagnosis of benign and malignant pulmonary nodules attached to the interlobar pleura.Quantitative imaging in medicine and surgery · 2026Article
- The value of an integrated multi-omics model in the diagnosis of benign and malignant pulmonary nodules.Translational cancer research · 2026Article
- Family history of cancer and risk of lung cancer: a retrospective cohort study in a Chinese population.Frontiers in medicine · 2026Article
- Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience.Journal of clinical medicine · 2025Article
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- IVIM-DWI-Based Radiomics for Lesion Phenotyping and Clinical Status Prediction in Relapsing-Remitting Multiple Sclerosis.Journal of clinical medicine · 2025Article
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- Visual and Predictive Assessment of Pneumothorax Recurrence in Adolescents Using Machine Learning on Chest CT.Journal of clinical medicine · 2025Article
- Quantitative Magnetic Resonance Imaging and Patient-Reported Outcomes in Patients Undergoing Hip Labral Repair or Reconstruction.Journal of imaging · 2025Article
- Added Value of SPECT/CT in Radio-Guided Occult Localization (ROLL) of Non-Palpable Pulmonary Nodules Treated with Uniportal Video-Assisted Thoracoscopy.Journal of clinical medicine · 2025Article
- CT-Based Radiomics Enhance Respiratory Function Analysis for Lung SBRT.Bioengineering (Basel, Switzerland) · 2025Article
- Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.Journal of cancer research and clinical oncology · 2025Article
- Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.Cancers · 2025Article
- Is Personalized Mechanical Thrombectomy Based on Clot Characteristics Feasible? A Radiomics Model Using NCECT to Predict FPE in AIS Patients Undergoing Thromboaspiration.Journal of clinical medicine · 2025Article
- Article
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6 authors.
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Abstract
Purpose: The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging. Materials and methods: A data set of Centers 1 (training set: 2633; internal testing set: 1129); Center 2 and Center 3 (external testing set: 218) of patients with pulmonary nodule cases was retrospectively collected. Handcrafted features were extracted from noncontrast chest CT scans by three senior radiologists. A total of 22 clinically handcrafted parameters (age, gender, L-RADS, and PNI-GARS et al.) were used to construct machine learning models (random forest, gradient boosting, and explainable boosting) for the classification of preoperative pulmonary nodules, and the parameters of the model were adjusted to achieve optimal performance. To evaluate the prediction capacity of each model. Both 5-fold cross-validation and 10-fold cross-validation were used to test the robustness of the models. Results: The explainable boosting model had the best performance on our constructed data. The model achieves an accuracy of 89.9%, a precision of 97.48%, a specificity of 89.5%, a sensitivity of 91.1%, and an AUC of 90.3%. In human-machine comparison, the AUC of machine learning models (90.4%, 95% CI: 85.5%-94.8%) was significantly improved compared to radiologists (60%, 95% CI: 50%-71.4%). Conclusions: The explainable boosting model exhibited superior performance on our dataset, achieving high accuracy and precision in the diagnosis of pulmonary nodules compared to experienced radiologists.
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