Clinical radiology · 2024
Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas.
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Liu W, Wang W, Guo M, Zhang H
Аннотация
AIM: To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas.
MATERIALS AND METHODS: We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models.
RESULTS: The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769.
CONCLUSION: Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.
MATERIALS AND METHODS: We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models.
RESULTS: The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769.
CONCLUSION: Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.