Multicenter evaluation of CT deep radiomics model in predicting Leibovich score risk groups for non-metastatic clear cell renal cell carcinoma

Background

Non-metastatic clear cell renal cell carcinoma (nccRCC) poses a significant risk of postoperative recurrence and metastasis, underscoring the importance of accurate preoperative risk assessment. While the Leibovich score is effective, it relies on postoperative histopathological data. This study aims to evaluate the efficacy of CT radiomics and deep learning models in predicting Leibovich score risk groups in nccRCC, and to explore the interrelationship between CT and pathological features.

Patients and Methods

This research analyzed 600 nccRCC patients from four datasets, dividing them into low (Leibovich scores of 0–2) and intermediate to high risk (Leibovich scores exceeding 3) groups. Radiological model was developed from CT subjective features, and radiomics and deep learning models were constructed from CT images. Additionally, a deep radiomics model using radiomics and deep learning features was developed, alongside a fusion model incorporating all feature types. Model performance was assessed by AUC values, while survival differences across predicted groups were analyzed using survival curves and the log-rank test. Moreover, the research investigated the interrelationship between CT and pathological features derived from whole-slide pathological images.

Results

Within the training dataset, four radiological, three radiomics, and thirteen deep learning features were selected to develop models predicting nccRCC Leibovich score risk groups. The deep radiomics model demonstrated superior predictive accuracy, evidenced by AUC values of 0.881, 0.829, and 0.819 in external validation datasets. Notably, significant differences in overall survival were observed among patients classified by this model (log-rank test p < 0.05 across all datasets). Furthermore, a correlation and complementarity were observed between CT deep radiomics features and pathological deep learning features.

Conclusions

The CT deep radiomics model precisely predicts nccRCC Leibovich score risk groups preoperatively and highlights the synergistic effect between CT and pathological data.