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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 models predict delayed hyponatremia post-transsphenoidal surgery using clinically available

Yutaro Fuse¹, Kazuhito Takeuchi², Hiroshi Nishiwaki³

¹Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.

|March 30, 2023

Summary

This summary is machine-generated.

Machine learning models can predict delayed hyponatremia (DHN) after pituitary neuroendocrine tumor (PitNET) surgery. These tools utilize pre- and post-operative data to identify patients at risk for this common complication.

Keywords:

Delayed hyponatremia Machine learning Outcome prediction PitNET Pituitary adenoma Transsphenoidal surgery

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Area of Science:

Neurosurgery
Endocrinology
Artificial Intelligence in Medicine

Background:

Delayed hyponatremia (DHN) is a frequent complication following pituitary surgery, often leading to unexpected hospital readmissions.
Endoscopic transsphenoidal surgery (eTSS) is a common approach for pituitary neuroendocrine tumors (PitNETs).

Purpose of the Study:

To develop predictive tools for DHN in patients undergoing eTSS for PitNETs.
To identify key clinical variables for predicting postoperative DHN.

Main Methods:

A retrospective study of 193 patients with PitNETs who underwent eTSS.
Four machine learning models (Random Forest, Support Vector Machine, Light Gradient Boosting Machine, Logistic Regression) were trained.
Models used preoperative and early postoperative clinical data, including patient characteristics, hormone levels, blood tests, and radiological findings.

Main Results:

The Random Forest model achieved the highest area under the receiver operating characteristic curve (ROC-AUC) of 0.759.
The Light Gradient Boosting Machine model showed the highest accuracy at 0.746.
The best Random Forest model incorporated 24 features, with nine available preoperatively.

Conclusions:

Machine learning models effectively predict DHN after PitNET resection.
The models leverage both pre- and post-resection features for accurate prediction.