An article co-authored by multiple members of the PNA (Including Drs. Samson, Chaichana, and Quinones-Hinojosa) looks at the role of machine learning in forecasting outcomes of pituitary surgery. They conclude that it is not ready yet as no model achieved clinical applicability.
Link:
Brain Sci
. 2023 Mar 15;13(3):495.
doi: 10.3390/brainsci13030495.
Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence
• PMID: 36979305 DOI: 10.3390/brainsci13030495
Abstract
Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality.
Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”.
Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60-72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80-0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability
Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.
Keywords: Cushing disease; acromegaly; adenoma; artificial intelligence; machine learning; outcomes; pituitary adenoma; reporting quality assessment; systematic review
Dr. Susan Samson
Dr. Kaisorn Chaichana
Dr. Alfredo Quinones-Hinojosa
