ArticleMolecular imaging and biology2021
Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer.
Article in Molecular imaging and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells.Journal of cancer research and clinical oncology · 2023Trial
- Post-Tuberculosis Sequelae and Active Tuberculosis in Lung Cancer: Imaging Patterns and Clinical Associations-A Retrospective Single-Center Cohort Study.Journal of clinical medicine · 2026Article
- Development of anQuantitative imaging in medicine and surgery · 2025Article
- Multimodal radiomics fusion for predicting postoperative recurrence in NSCLC patients.Journal of cancer research and clinical oncology · 2025Article
- Development and validation of a diagnostic radiomics model for the differential diagnosis of coexistent tuberculosis and lung cancer versus isolated lung cancer.Journal of thoracic disease · 2025Article
- Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.EJNMMI physics · 2025Article
- Diagnosis and management of splenic tuberculosis: a case report and literature review.Frontiers in medicine · 2025Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
- Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities.La Radiologia medica · 2023Article
- Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study.European radiology · 2023Article
- Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.Cancers · 2023Article
- Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.European radiology · 2023Article
- PET morphology helps distinguish solitary and solid pulmonary tuberculosis from non-small cell lung cancer.Japanese journal of radiology · 2023Article
- A Comprehensive Study on the Correlation of Treatment, Diagnosis and Epidemiology of Tuberculosis and Lung Cancer.Tanaffos · 2023Review
- Distinction of Lymphoma from Sarcoidosis onJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Diagnostic Performance of Machine Learning Models Based onMolecular imaging and radionuclide therapy · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aim to accurately differentiate between active pulmonary tuberculosis (TB) and lung cancer (LC) based on radiomics and semantic features as extracted from pre-treatment positron emission tomography/X-ray computed tomography (PET/CT) images. PROCEDURES: A total of 174 patients (77/97 pulmonary TB/LC as confirmed by pathology) were retrospectively selected, with 122 in the training cohort and 52 in the validation cohort. Four hundred eighty-seven radiomics features were initially extracted to quantify phenotypic characteristics of the lesion region in both PET and CT images. Eleven semantic features were additionally defined by two experienced nuclear medicine physicians. Feature selection was performed in 5 steps to enable derivation of robust and effective signatures. Multivariable logistic regression analysis was subsequently used to develop a radiomics nomogram. The calibration, discrimination, and clinical usefulness of the nomogram were evaluated in both the training and independent validation cohorts.
resultsThe individualized radiomics nomogram, which combined PET/CT radiomics signature with semantic features, demonstrated good calibration and significantly improved the diagnostic performance with respect to the semantic model alone or PET/CT signature alone in training cohort (AUC 0.97 vs. 0.94 or 0.91, p = 0.0392 or 0.0056), whereas did not significantly improve the performance in validation cohort (AUC 0.93 vs. 0.89 or 0.91, p = 0.3098 or 0.3323).
conclusionThe radiomics nomogram showed potential for individualized differential diagnosis between solid active pulmonary TB and solid LC, although the improvement of performance was not significant relative to semantic model.
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