Journal of magnetic resonance imaging : JMRI · 2025
Preoperative Assessment of Ki-67 Labeling Index in Pituitary Adenomas Using Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI.
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Zhao Kaiyang, Chen Chaoyue, Zhang Yang, Huang Zhouyang, Zhao Yanjie, Yue Qiang, Xu Jianguo
Аннотация
BACKGROUND: Ki-67 labeling index (Ki-67 LI) is a proliferation marker that is correlated with aggressive behavior and prognosis of pituitary adenomas (PAs). Dynamic contrast-enhanced MRI (DCE-MRI) may potentially contribute to the preoperative assessment of Ki-67 LI.
PURPOSE: To investigate the feasibility of assessing Ki-67 LI of PAs preoperatively using delta-radiomics based on DCE-MRI.
STUDY TYPE: Retrospective.
POPULATION: 605 PA patients (female = 47.1%, average age = 52.2) from two centers (high Ki-67 LI (≥ 3%) = 229; low Ki-67 LI (< 3%) = 376), divided into a training set (n = 313), an internal validation set (n = 196), and an external validation set (n = 96).
FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, DCE-MRI.
ASSESSMENT: This study developed a non-delta-radiomics model based on the non-delta-radiomic features directly extracted from four phases, a delta-radiomics model based on the delta-radiomic features, and a combined model integrating clinical parameters (Knosp grade and tumor diameter) with delta-radiomic features. U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression were utilized to select important radiomic features. Support vector machine (SVM), XGBoost (XGB), logistic regression (LR), and Gaussian naive Bayes (GNB) were utilized to develop the models.
STATISTICAL TESTS: Receiver operating characteristic (ROC) curve. Calibration curve. Decision curve analysis (DCA). Intraclass correlation coefficients (ICC). DeLong test for ROC curves. U test or t test for numerical variables. Fisher's test or Chi-squared test for categorical variables. A p-value < 0.05 was considered statistically significant.
RESULTS: The combined model demonstrated the best performance in preoperatively assessing the Ki-67 LI of PAs, achieving AUCs of 0.937 and 0.897 in the internal and external validation sets, respectively. The models based on delta-radiomic features outperformed the non-delta-radiomic model.
DATA CONCLUSION: A delta-radiomics-based model using DCE-MRI may show high diagnostic performance for preoperatively assessing the Ki-67 LI status of PAs.
EVIDENCE LEVEL: 3 Technical Efficacy: Stage 2.
PURPOSE: To investigate the feasibility of assessing Ki-67 LI of PAs preoperatively using delta-radiomics based on DCE-MRI.
STUDY TYPE: Retrospective.
POPULATION: 605 PA patients (female = 47.1%, average age = 52.2) from two centers (high Ki-67 LI (≥ 3%) = 229; low Ki-67 LI (< 3%) = 376), divided into a training set (n = 313), an internal validation set (n = 196), and an external validation set (n = 96).
FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, DCE-MRI.
ASSESSMENT: This study developed a non-delta-radiomics model based on the non-delta-radiomic features directly extracted from four phases, a delta-radiomics model based on the delta-radiomic features, and a combined model integrating clinical parameters (Knosp grade and tumor diameter) with delta-radiomic features. U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression were utilized to select important radiomic features. Support vector machine (SVM), XGBoost (XGB), logistic regression (LR), and Gaussian naive Bayes (GNB) were utilized to develop the models.
STATISTICAL TESTS: Receiver operating characteristic (ROC) curve. Calibration curve. Decision curve analysis (DCA). Intraclass correlation coefficients (ICC). DeLong test for ROC curves. U test or t test for numerical variables. Fisher's test or Chi-squared test for categorical variables. A p-value < 0.05 was considered statistically significant.
RESULTS: The combined model demonstrated the best performance in preoperatively assessing the Ki-67 LI of PAs, achieving AUCs of 0.937 and 0.897 in the internal and external validation sets, respectively. The models based on delta-radiomic features outperformed the non-delta-radiomic model.
DATA CONCLUSION: A delta-radiomics-based model using DCE-MRI may show high diagnostic performance for preoperatively assessing the Ki-67 LI status of PAs.
EVIDENCE LEVEL: 3 Technical Efficacy: Stage 2.