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Purpose <p>Prognostication of surgical complexity is crucial for optimizing decision-making and patient counseling in pituitary surgery. This study aimed to develop a clinical score to predict gross-total resection (GTR) in non-functioning pituitary adenomas (NFPAs) using externally validated machine-learning (ML) models.</p> Methods <p>Clinical and radiological data were collected from two tertiary medical centers. Patients had pre- and postoperative structural T1-weighted MRI with gadolinium and T2-weighted preoperative scans. Three ML classifiers were trained on the National Hospital for Neurology and Neurosurgery dataset and tested on the Foundation IRCCS Ca’ Granda Polyclinic of Milan dataset. Feature importance analyses and hierarchical-tree inspection identified predictors of surgical complexity, which were used to create the grading score. The prognostic performance of the proposed score was compared to that of the state-of-the art TRANSSPHER grade in the external dataset. Surgical morbidity was also analyzed.</p> Results <p>All ML models accurately predicted GTR, with the random forest classifier achieving the best performance (weighted-F1 score of 0.87; CIs: 0.71, 0.97). Key predictors—Knosp grade, tumor maximum diameter, consistency, and supra-sellar nodular extension—were included in the modified (m)-TRANSSPHER grade. The ROC analysis showed superior performance of the m-TRANSSPHER grade over the TRANSSPHER grade for predicting GTR in NFPAs (AUC 0.85 vs. 0.79).</p> Conclusions <p>This international multi-center study used validated ML algorithms to refine predictors of surgical complexity in NFPAs, yielding the m-TRANSSPHER grade, which demonstrated enhanced prognostic accuracy for surgical complexity prediction compared to existing scales.</p>

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Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月3日

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  • Giorgio Fiore,
  • Giulio A. Bertani,
  • Stephanie E. Baldeweg,
  • Anouk Borg,
  • Giorgio Conte,
  • Neil Dorward,
  • Emanuele Ferrante,
  • Ziad Hussein,
  • Anna Miserocchi,
  • Katherine Miszkiel,
  • Giovanna Mantovani,
  • Marco Locatelli,
  • Hani J. Marcus

摘要

Purpose

Prognostication of surgical complexity is crucial for optimizing decision-making and patient counseling in pituitary surgery. This study aimed to develop a clinical score to predict gross-total resection (GTR) in non-functioning pituitary adenomas (NFPAs) using externally validated machine-learning (ML) models.

Methods

Clinical and radiological data were collected from two tertiary medical centers. Patients had pre- and postoperative structural T1-weighted MRI with gadolinium and T2-weighted preoperative scans. Three ML classifiers were trained on the National Hospital for Neurology and Neurosurgery dataset and tested on the Foundation IRCCS Ca’ Granda Polyclinic of Milan dataset. Feature importance analyses and hierarchical-tree inspection identified predictors of surgical complexity, which were used to create the grading score. The prognostic performance of the proposed score was compared to that of the state-of-the art TRANSSPHER grade in the external dataset. Surgical morbidity was also analyzed.

Results

All ML models accurately predicted GTR, with the random forest classifier achieving the best performance (weighted-F1 score of 0.87; CIs: 0.71, 0.97). Key predictors—Knosp grade, tumor maximum diameter, consistency, and supra-sellar nodular extension—were included in the modified (m)-TRANSSPHER grade. The ROC analysis showed superior performance of the m-TRANSSPHER grade over the TRANSSPHER grade for predicting GTR in NFPAs (AUC 0.85 vs. 0.79).

Conclusions

This international multi-center study used validated ML algorithms to refine predictors of surgical complexity in NFPAs, yielding the m-TRANSSPHER grade, which demonstrated enhanced prognostic accuracy for surgical complexity prediction compared to existing scales.