Fiore, Giorgio;
Bertani, Giulio A;
Baldeweg, Stephanie E;
Borg, Anouk;
Conte, Giorgio;
Dorward, Neil;
Ferrante, Emanuele;
... Marcus, Hani J; + view all
(2025)
Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade.
Pituitary
, 28
(1)
, Article 26. 10.1007/s11102-024-01495-9.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11102-024-01495-9 |
| Publisher version: | https://doi.org/10.1007/s11102-024-01495-9 |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Adenomas, Gross-total resection, Machine learning Neuroendocrine tumor PitNET Pituitary |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204316 |
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