ArticleWorld journal of clinical cases2020
Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans.
Article in World journal of clinical cases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses.Journal of cancer research and clinical oncology · 2023Trial
- A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess.Parasitology research · 2022Trial
- A radiomics nomogram utilizing T2-weighted MRI for accurate diagnosis of rectocele.Abdominal radiology (New York) · 2026Article
- Whole-lung computed tomography radiomics combined with clinical features for differentiating multidrug-resistant tuberculosis from drug-sensitive tuberculosis: a retrospective multi-center study.Journal of thoracic disease · 2025Article
- Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients.iScience · 2025Article
- Early treatment monitoring of multidrug-resistant tuberculosis based on CT radiomics of cavity and cavity periphery.European radiology experimental · 2025Article
- Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities.La Radiologia medica · 2023Article
- Construction and Validation of a Diagnostic Scoring System for Predicting Active Pulmonary Tuberculosis in Patients with Positive T-SPOT Based on Indicators Associated with Coagulation and Inflammation: A Retrospective Cross-Sectional Study.Infection and drug resistance · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- The Role of Chest CT Radiomics in Diagnosis of Lung Cancer or Tuberculosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- The Novel Predictive Biomarkers for Type 2 Diabetes Mellitus in Active Pulmonary Tuberculosis Patients.Infection and drug resistance · 2022Article
- A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis.Frontiers in cellular and infection microbiology · 2022Article
- Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis.Frontiers in molecular biosciences · 2022Article
- Clinical characteristics and chest computed tomography findings related to the infectivity of pulmonary tuberculosis.BMC infectious diseases · 2021Article
- Differentiating mass-like tuberculosis from lung cancer based on radiomics and CT features.Translational cancer research · 2021Article
- Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma.Translational cancer research · 2021Article
- Cancer or Tuberculosis: A Comprehensive Review of the Clinical and Imaging Features in Diagnosis of the Confusing Mass.Frontiers in oncology · 2021Review
- Comparison of the Detection Rates of Different Diagnostic Methods for Primary Peripheral Lung Cancer.Frontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPulmonary tuberculosis (TB) and lung cancer (LC) are common diseases with a high incidence and similar symptoms, which may be misdiagnosed by radiologists, thus delaying the best treatment opportunity for patients.
aimTo develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography (CT) images.
methodsWe enrolled 478 patients (January 2012 to October 2018), who underwent preoperative CT screening. Radiomics features were extracted and selected from the CT data to establish a logistic regression model. A radiomics nomogram model was constructed, with the receiver operating characteristic, decision and calibration curves plotted to evaluate the discriminative performance.
resultsRadiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance. The radiomics nomogram model exhibited good discrimination, with an area under the curve of 0.914 (sensitivity = 0.890, specificity = 0.796) in the training cohort, and 0.900 (sensitivity = 0.788, specificity = 0.907) in the validation cohort. The decision curve analysis revealed that the constructed nomogram had clinical usefulness.
conclusionThese proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.