ArticlePloS one2026
Diagnostic performance of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo evaluate the diagnostic value of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules.
methodsThis retrospective study included 132 patients with pulmonary nodules, including 30 benign and 102 malignant lesions. All patients underwent dual-phase 99mTc-MIBI SPECT/CT at approximately 20 minutes and 2 hours after tracer injection. Semiquantitative parameters, including early and delayed tumor-to-normal ratios (T/N) and retention indices (RI), were calculated. Clinical variables and imaging parameters were analyzed using univariable and multivariable logistic regression, and selected variables were further used to develop machine learning models.
resultsMalignant nodules showed significantly higher early-phase uptake and lower retention index values than benign nodules. Multivariable analysis identified elevated CEA and RImax as independent predictors of malignancy. Machine learning models built on these simple semiquantitative parameters showed promising diagnostic performance, with an AUC of 0.944 (95% CI: 0.883-0.990) for SVM on the training set, 0.805 (95% CI: 0.678-0.912) for Logistic Regression (LR), 0.881 (95% CI: 0.800-0.949) for Artificial Neural Network (ANN), and 0.979 (95% CI: 0.951-0.995) for Random Forest (RF), demonstrating their effectiveness in classifying pulmonary nodules.
conclusionDual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters provide useful information for distinguishing benign from malignant pulmonary nodules. A machine learning strategy based on simple and interpretable parameters may offer a practical tool for pulmonary nodule assessment, especially in settings where complex imaging analysis is not feasible.
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