Neuroradiology · 2026
Predicting recurrence in silent corticotroph adenomas: a habitat analysis and comprehensive nomogram approach.
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Zhao Xuening, Wang Xiaochen, Wang Sihui, Yan Ying, Chen Lingxu, Yuan Mengyuan, Sun Shengjun
Аннотация
BACKGROUND: Silent corticotroph adenoma (SCA) exhibits high invasiveness and recurrence; thus, accurate prediction of postoperative recurrence is crucial.
OBJECTIVE: To predict recurrence-free survival (RFS) in SCA using habitat analysis (subregion radiomics) and to develop a comprehensive nomogram integrating habitat scores, clinical, radiological, and pathological features.
METHODS: This retrospective study included 325 SCA patients, randomly assigned to training and testing cohorts in a 7:3 ratio. Radiomics features were extracted from tumor subregions clustered via K-means based on CE-T1WI and T2WI. Feature selection involved t-tests, Pearson / Spearman correlation, and the least absolute shrinkage and selection operator (LASSO) regression, and the habitat score was calculated based on the selected subregional radiomics features and their corresponding coefficients. Kaplan-Meier and Cox regression analyses were used to identify prognostic factors. A nomogram incorporating independent predictors was constructed and validated for RFS prediction. Predictive performance was evaluated through the Harrell C-index, calibration curves, and decision curve analysis (DCA).
RESULTS: Habitat scores significantly stratified patients by RFS (p < 0.001). Multivariate Cox analysis identified habitat score, Ki-67 index, surgical method, and postoperative gamma knife radiotherapy as independent predictors. Calibration and DCA curves confirmed good agreement and clinical utility.
CONCLUSION: Habitat scores derived from subregional radiomics provide good prognostic value for RFS prediction in SCA. The proposed nomogram enables individualized recurrence risk assessment, supporting postoperative decision-making.
OBJECTIVE: To predict recurrence-free survival (RFS) in SCA using habitat analysis (subregion radiomics) and to develop a comprehensive nomogram integrating habitat scores, clinical, radiological, and pathological features.
METHODS: This retrospective study included 325 SCA patients, randomly assigned to training and testing cohorts in a 7:3 ratio. Radiomics features were extracted from tumor subregions clustered via K-means based on CE-T1WI and T2WI. Feature selection involved t-tests, Pearson / Spearman correlation, and the least absolute shrinkage and selection operator (LASSO) regression, and the habitat score was calculated based on the selected subregional radiomics features and their corresponding coefficients. Kaplan-Meier and Cox regression analyses were used to identify prognostic factors. A nomogram incorporating independent predictors was constructed and validated for RFS prediction. Predictive performance was evaluated through the Harrell C-index, calibration curves, and decision curve analysis (DCA).
RESULTS: Habitat scores significantly stratified patients by RFS (p < 0.001). Multivariate Cox analysis identified habitat score, Ki-67 index, surgical method, and postoperative gamma knife radiotherapy as independent predictors. Calibration and DCA curves confirmed good agreement and clinical utility.
CONCLUSION: Habitat scores derived from subregional radiomics provide good prognostic value for RFS prediction in SCA. The proposed nomogram enables individualized recurrence risk assessment, supporting postoperative decision-making.