Title:External Validation of Ultrasound Radiomics for Small (≤ 4 cm) Renal Mass
Differentiation: A Comparison with Radiologists
Volume: 20
Author(s): Ming Liang, Licong Dong, Bing Ou, Xinbao Zhao, Jiayi Wu, Haolin Qiu, Mengting Ye and Baoming Luo*
Affiliation:
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No.107 Yanjiang Road West, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial
Hospital, Sun Yat-sen University, Guangzhou, China
Keywords:
Small renal masses, Ultrasound, Machine learning, Radiomics, Decision curve analysis, Renal cell carcinoma.
Abstract:
Background:
Renal cell carcinoma, especially in small renal masses (≤ 4 cm) (SRM), has increased. Pathological analysis revealed a high proportion of benign
masses, highlighting the urgent need for precise SRM differentiation.
Objectives:
This research aimed to independently validate the performance of machine learning-based ultrasound (US) radiomics analysis in differentiating
benign from malignant SRM, and to compare its performance with that of radiologists.
Methods:
A total of 499 patients from two hospitals were retrospectively included in this study and divided into two cohorts. US images were used to extract
radiomics features. To obtain the most robust features, inter-observer correlation coefficient, Spearman correlation coefficient, and least absolute
shrinkage and selection operator methods were applied for feature selection. Three models were developed in the training data using the stochastic
gradient boosting algorithm, including a clinical model, a radiomics model, and a combined model that integrated clinical factors and radiomics
features. The performance of these models was evaluated in the independent external validation data, including discrimination, calibration, and
clinical usefulness, and compared with pooled radiologists' assessments.
Results:
The AUCs of the clinical, radiomics, and combined models were 0.844, 0.942, and 0.954, respectively. The radiomics and combined models
significantly outperformed the clinical model (all p < 0.05), while no significant difference was observed between them (p = 0.32). The radiomics
and combined models showed good discrimination and calibration. Decision curve analysis exhibited that the combined model had clinical
usefulness. Compared with the pooled radiologists’ assessment (AUC, 0.799), the combined model showed superior classification results (p <
0.01) and higher specificity (p < 0.01) with similar sensitivity (p = 0.62).
Conclusion:
The combined model incorporating clinical factors and radiomics features accurately distinguished benign from malignant SRM.