Value of LASSO-Logistic Regression Model Incorporating CT Features and GLCM Parameters in Differentiating Benign and Malignant Solitary Pulmonary Nodules

Sheng GAO; Xingxing ZHAN

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Sheng GAO, Xingxing ZHAN. Value of LASSO-Logistic Regression Model Incorporating CT Features and GLCM Parameters in Differentiating Benign and Malignant Solitary Pulmonary Nodules[J]. Journal of Kunming Medical University.

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Value of LASSO-Logistic Regression Model Incorporating CT Features and GLCM Parameters in Differentiating Benign and Malignant Solitary Pulmonary Nodules

Sheng GAO¹,
Xingxing ZHAN^{2
,
,}

1.
CT Department
2.
Department of Ultrasound，Zongyang County People's Hospital，Tongling Anhui 246700，China

Received Date: 2025-12-04

Abstract

Abstract

Objective To investigate the value of a least absolute shrinkage and selection operator （LASSO）-Logistic regression model based on computed tomography （CT） imaging features and gray-level co-occurrence matrix （GLCM） parameters in differentiating benign and malignant solitary pulmonary nodules （SPNs）. Methods A retrospective study was conducted on 300 patients with SPNs admitted to Zongyang County People's Hospital, Tongling City, from December 1, 2021 to June 30, 2025. The patients were randomly divided into a modeling cohort （n = 210） and a validation cohort （n = 90） at a ratio of 7∶3. Based on pathological results, patients in the modeling cohort were further classified into a malignant group （n = 161） and a benign group （n = 49）. Clinical data and imaging features were compared between the two groups. LASSO regression analysis was used to evaluate the importance of influencing factors and address collinearity. Spearman rank correlation analysis was performed to examine the relationships among these factors. Logistic regression analysis was conducted to identify influencing factors for benignity and malignancy of isolated pulmonary nodules. A nomogram prediction model was established using R software, and receiver operating characteristic （ROC） curves were plotted for validation. Calibration curves and decision curves were used to assess predictive value. Results No significant differences in clinical data or imaging features were observed between the modeling and validation cohorts （P > 0.05）. Among the 210 patients in the modeling cohort, 161 （76.67%） were malignant lesions （malignant group） and 49 （23.33%） were benign lesions （benign group）. Compared with the benign group, the malignant group showed higher levels of heat shock protein 90 alpha （HSP90α）, higher proportions of lobulation sign, spiculation sign, ground-glass opacity （GGO） component, and non-solid nodules, as well as higher GLCM entropy. Conversely, the malignant group had a lower proportion of flat margin and lower GLCM angular second moment （ASM） levels. LASSO regression identified six influencing factors: HSP90α, flat margin, presence of GGO component, density, GLCM-ASM, and GLCM entropy. Correlation analysis revealed that HSP90α, presence of GGO component, density, and GLCM entropy were positively correlated with malignancy （r_s = 0.309, 0.305, 0.357, 0.312, respectively）, while flat margin and GLCM-ASM were negatively correlated with malignancy （r_s = −0.326, −0.358, respectively）. Logistic regression analysis confirmed that HSP90α, straight edge, GGO component, density, GLCM parameter-angular second moment, and GLCM parameter-entropy were independent predictors of benign versus malignant SPNs （all P < 0.05）. Based on these findings, a nomogram model was constructed and validated using the validation cohort. ROC curves demonstrated that the model achieved an area under the curve （AUC） of 0.945 in the modeling cohort and 0.935 in the validation cohort. Calibration curves showed close alignment between the predicted and reference curves. Additionally, the prediction model demonstrated high net benefit across the threshold range. Conclusion HSP90α, flat margin, GGO component, density, GLCM-ASM, and GLCM entropy can serve as reference indicators for differential diagnosis of benign and malignant isolated pulmonary nodules.
- Solitary pulmonary nodule,
- Tomography,
- Computed tomography,
- Prediction model

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References(27)

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