ArticleAbdominal radiology (New York)2024
CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Biomarkers of metastatic disease in pheochromocytoma and paraganglioma.Endocrine connections · 2026Article
- Multi-omics insights into metastatic pheochromocytomas and paragangliomas: Mechanisms, signatures, and prediction models.Hormones (Athens, Greece) · 2026Review
- Contrast-enhanced CT-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting preoperative diagnosis of pheochromocytoma and adrenal adenoma.BMC medical imaging · 2026Article
- An interpretable machine learning framework using multi-phase computed tomography for differentiation of adrenal lipid-poor adenomas and pheochromocytomas.Frontiers in artificial intelligence · 2026Article
- CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.Insights into imaging · 2025Article
- The value of CT-based radiomics for differentiation of pleural effusions in bacterial pneumonia andTranslational pediatrics · 2025Article
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14 authors.
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Abstract
objectivesThe purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma.
methodsA total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value.
results3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy.
conclusionThe combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.
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