Development of a nomogram integrating contrast-enhanced magnetic resonance imaging radiomics and clinical parameters for differentiating brain metastases of bronchial carcinoma from cerebral tuberculomas.
4 affiliations
- 1Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China.
- 2Department of Radiology, Hunan Institute for Tuberculosis Control, Hunan Chest Hospital, Changsha, China.
- 3Department of Internal Medicine, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China.
- 4Institute of Pulmonary Diseases, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China.
Abstract
Cerebral tuberculomas and brain metastases of bronchial carcinoma are often indistinguishable on magnetic resonance imaging (MRI), leading to misdiagnosis. This study develops a contrast-enhanced MRI radiomics-based nomogram to accurately differentiate them. A total of 482 patients (290 males, 192 females; mean age 56.08±12.96 years), including 254 with cerebral tuberculomas and 228 with brain metastases of bronchial carcinoma were enrolled from two centers. Radiomic features were extracted, standardized, and reduced via significance testing, Pearson correlation, and least absolute shrinkage and selection operator (LASSO) regression. A radiomic model was built, then integrated with clinical predictors into a nomogram. Performance was assessed across training, internal validation, and external validation cohorts using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). A total of 13 radiomic features were selected for model development. The radiomic model demonstrated areas under the curve (AUCs) of 0.907, 0.891 and 0.886 in training, internal validation, and independent external validation cohorts, respectively, significantly outperforming models based solely on clinical factors, which achieved AUCs of 0.781, 0.712, and 0.828 (P<0.05). The nomogram, which integrated the radiomic scores (Rad-scores), age, ring enhancement, meningeal thickening, and carcinoembryonic antigen (CEA) levels, exhibited superior performance with AUCs of 0.951, 0.937, and 0.940 across the three cohorts. DCA demonstrated that the nomogram provided greater clinical net benefits across a range of threshold probabilities, and calibration curves indicated strong consistency between predicted probabilities and actual outcomes. A nomogram combining clinical factors and contrast-enhanced MRI radiomics accurately differentiates cerebral tuberculomas from brain metastases of bronchial carcinoma, offering robust decision support for individualized diagnosis and treatment.
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BibTeX
@article{liang2026development,
title = {{Development of a nomogram integrating contrast-enhanced magnetic resonance imaging radiomics and clinical parameters for differentiating brain metastases of bronchial carcinoma from cerebral tuberculomas.}},
author = {Ruiyun Liang and Quhua Yin and Chengcheng Li and Huili Ren and Huiru Li and Min Song and Hui Zhang and Jingqiang Wu and Weijun Fang and Qi Wang},
journal = {Quantitative imaging in medicine and surgery},
year = {2026},
month = oct,
doi = {10.21037/qims-2026-1518},
url = {https://europepmc.org/article/MED/42997944},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development of a nomogram integrating contrast-enhanced magnetic resonance imaging radiomics and clinical parameters for differentiating brain metastases of bronchial carcinoma from cerebral tuberculomas.
AU - Ruiyun Liang
AU - Quhua Yin
AU - Chengcheng Li
AU - Huili Ren
AU - Huiru Li
AU - Min Song
AU - Hui Zhang
AU - Jingqiang Wu
AU - Weijun Fang
AU - Qi Wang
PY - 2026
DA - 2026/10/14
JO - Quantitative imaging in medicine and surgery
DO - 10.21037/qims-2026-1518
UR - https://europepmc.org/article/MED/42997944
AB - Cerebral tuberculomas and brain metastases of bronchial carcinoma are often indistinguishable on magnetic resonance imaging (MRI), leading to misdiagnosis. This study develops a contrast-enhanced MRI radiomics-based nomogram to accurately differentiate them. A total of 482 patients (290 males, 192 females; mean age 56.08±12.96 years), including 254 with cerebral tuberculomas and 228 with brain metastases of bronchial carcinoma were enrolled from two centers. Radiomic features were extracted, standardized, and reduced via significance testing, Pearson correlation, and least absolute shrinkage and selection operator (LASSO) regression. A radiomic model was built, then integrated with clinical predictors into a nomogram. Performance was assessed across training, internal validation, and external validation cohorts using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA). A total of 13 radiomic features were selected for model development. The radiomic model demonstrated areas under the curve (AUCs) of 0.907, 0.891 and 0.886 in training, internal validation, and independent external validation cohorts, respectively, significantly outperforming models based solely on clinical factors, which achieved AUCs of 0.781, 0.712, and 0.828 (P<0.05). The nomogram, which integrated the radiomic scores (Rad-scores), age, ring enhancement, meningeal thickening, and carcinoembryonic antigen (CEA) levels, exhibited superior performance with AUCs of 0.951, 0.937, and 0.940 across the three cohorts. DCA demonstrated that the nomogram provided greater clinical net benefits across a range of threshold probabilities, and calibration curves indicated strong consistency between predicted probabilities and actual outcomes. A nomogram combining clinical factors and contrast-enhanced MRI radiomics accurately differentiates cerebral tuberculomas from brain metastases of bronchial carcinoma, offering robust decision support for individualized diagnosis and treatment.
SN - 2223-4292
SN - 2223-4306
ER -