Peritumoral Radiomic Features on CT for Differential Diagnosis in Small-Cell Lung Cancer: Potential for Surgical Decision-Making
Introduction: Small-cell lung cancer (SCLC) is a leading cause of cancer-related mortality worldwide, with limited therapeutic outcomes and poor prognosis. Accurate diagnosis and optimal surgical decision-making remain critical challenges. This study aimed to develop and validate a clinical-radiomic...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-06-01
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| Series: | Cancer Control |
| Online Access: | https://doi.org/10.1177/10732748251351754 |
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| Summary: | Introduction: Small-cell lung cancer (SCLC) is a leading cause of cancer-related mortality worldwide, with limited therapeutic outcomes and poor prognosis. Accurate diagnosis and optimal surgical decision-making remain critical challenges. This study aimed to develop and validate a clinical-radiomics nomogram integrating computed tomography (CT) radiomic features of the peritumoral region and clinical factors to improve SCLC diagnosis and guide surgical planning. Methods: A retrospective cohort of 113 patients (54 SCLC, 59 non-small cell lung cancer) was analyzed. CT images were processed to extract 1050 radiomic features from both intratumoral and peritumoral (2-mm expanded) ROIs. Feature selection was performed using t-tests, LASSO regression, and mRMR analysis. Logistic regression models were constructed for original and expanded ROIs, and a clinical-radiomics nomogram was developed by combining significant radiomic features with independent clinical predictors (gender, smoking history, tumor diameter, glitch, and neuron-specific enolase levels). Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, and CIC curves. Results: The expanded ROI radiomics model outperformed the original ROI and clinical models, achieving higher accuracy (0.83 vs 0.76/0.70), sensitivity (0.80 vs 0.74/0.77), specificity (0.85 vs 0.75/0.65), and AUC (0.85 vs 0.76/0.71). The clinical-radiomics nomogram demonstrated superior diagnostic performance, with an AUC of 0.96 (95% CI: 0.88-1.00), accuracy of 0.91, sensitivity of 0.92, and specificity of 0.90. CIC analysis confirmed its clinical utility for surgical decision-making at intermediate-risk thresholds. Conclusion: The integration of peritumoral radiomic features and clinical factors into a nomogram provides a non-invasive tool for SCLC diagnosis and surgical planning. The superiority of the expanded model substantiates the potential presence of SCLC in peri-tumoral tissues that may be imperceptible through conventional imaging, thereby offering guidance for surgical decision-making. This approach has potential for improving treatment outcomes and warrants further validation in multicenter studies. |
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| ISSN: | 1526-2359 |