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Background <p>Silent corticotroph adenoma (SCA) exhibits high invasiveness and recurrence; thus, accurate prediction of postoperative recurrence is crucial.</p> Objective <p>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.</p> Methods <p>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).</p> Results <p>Habitat scores significantly stratified patients by RFS (<i>p</i> &lt; 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.</p> Conclusion <p>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.</p>

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Predicting recurrence in silent corticotroph adenomas: a habitat analysis and comprehensive nomogram approach

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Xuening Zhao,
  • Xiaochen Wang,
  • Sihui Wang,
  • Ying Yan,
  • lingxu Chen,
  • mengyuan Yuan,
  • Shengjun Sun

摘要

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.