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Machine Learning Analysis of Post-Operative Tumour Progression in Non-Functioning Pituitary Neuroendocrine Tumours: A Pilot Study.
Hussein, Ziad; Slack, Robert W; Baldeweg, Stephanie E; Mazomenos, Evangelos B; Marcus, Hani J.
Afiliación
  • Hussein Z; Department of Diabetes & Endocrinology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK.
  • Slack RW; Department of Diabetes & Endocrinology, University College London Hospital NHS Foundation Trust, London NW1 2BU, UK.
  • Baldeweg SE; Centre for Obesity & Metabolism, Department of Experimental & Translational Medicine, Division of Medicine, University College London, London WC1N 3BG, UK.
  • Mazomenos EB; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London WC1E 6BT, UK.
  • Marcus HJ; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Cancers (Basel) ; 16(6)2024 Mar 19.
Article en En | MEDLINE | ID: mdl-38539533
ABSTRACT
Post-operative tumour progression in patients with non-functioning pituitary neuroendocrine tumours is variable. The aim of this study was to use machine learning (ML) models to improve the prediction of post-operative outcomes in patients with NF PitNET. We studied data from 383 patients who underwent surgery with or without radiotherapy, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree, showed superior performance in predicting tumour progression when compared with parametric statistical modelling using logistic regression, with SVM achieving the highest performance. The strongest predictor of tumour progression was the extent of surgical resection, with patient age, tumour volume, and the use of radiotherapy also showing influence. No features showed an association with tumour recurrence following a complete resection. In conclusion, this study demonstrates the potential of ML models in predicting post-operative outcomes for patients with NF PitNET. Future work should look to include additional, more granular, multicentre data, including incorporating imaging and operative video data.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article