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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine Learning in Pituitary Surgery.

Vittorio Stumpo¹, Victor E Staartjes², Luca Regli¹

¹Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Acta Neurochirurgica. Supplement

|December 4, 2021

Summary

This summary is machine-generated.

Machine learning (ML) shows promise in pituitary surgery for diagnosis and outcome prediction. Current research is preliminary, needing larger studies and validation for clinical use.

Keywords:

Artificial intelligence Endocrinology Machine learning Neurosurgery Outcome prediction Pituitary

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Area of Science:

Neurosurgery
Medical Artificial Intelligence
Pituitary Gland Disorders

Background:

Machine learning (ML) applications are growing in neurosurgery for diagnosis, characterization, and outcome prediction.
Literature on ML in pituitary surgery is less extensive but addresses clinically relevant questions.

Purpose of the Study:

To review reported ML applications in pituitary surgery.
To discuss barriers to clinical translation of ML research in this field.

Main Methods:

Review of published ML applications in pituitary surgery.
Analysis of ML use in differential diagnosis, lesion characterization, and outcome/complication prediction.
Discussion of practical barriers to clinical translation.

Main Results:

ML applications cover differential diagnosis, lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), and prediction of surgical outcomes (resection, recurrence, remission) and complications (CSF leak, hyponatremia).
Published reports are largely preliminary, requiring larger datasets and external validation.

Conclusions:

ML offers potential tools for pituitary surgery but requires further development.
Overcoming limitations necessitates larger populations, robust validation, clear outcome selection, and multicenter collaborations for informed patient management.