aceMultiple Machine Learning Algorithms for Predicting Postoperative Residual Tumor in Pituitary Neuroendocrine Tumors
Aims To develop and validate an interpretable machine learning (ML) model for predicting radiologically confirmed postoperative residual tumor (RT) in pituitary neuroendocrine tumors (PitNETs). Methods We retrospectively analyzed 584 surgically treated patients with PitNETs from a single center, using a random 7:3 training-test split and internal cross-validation. The primary endpoint was radiologically confirmed postoperative RT, assessed using MRI obtained within 48 h after surgery and longitudinal follow-up M…
In a single-center study of 584 pituitary tumor patients, a random forest model predicted residual tumor after surgery with an AUC of 0.945 in internal testing, versus 0.715 for logistic regression.
Evaluation used only a single-center random split and internal cross-validation, so generalizability to other centers and future patients is unproven and internal performance may be overstated.
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- Peer-reviewedCNS Neuroscience & Therapeutics2026-11-01
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Truvace Impact Record TRV-2026-2415, v1: “Multiple Machine Learning Algorithms for Predicting Postoperative Residual Tumor in Pituitary Neuroendocrine Tumors.” Truvace, 2026-11-07. /record/TRV-2026-2415 (accessed on the day you copy it). sha256 34845b6ef3d35c72…
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