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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.

Objective: To evaluate the efficacy of immunotherapy for GTN treatment after methotrexate-resistance or in cases of multiresistant disease, through a systematic review, as well as to present the first 4 Brazilian cases of immunotherapy for GTN treatment., Methods: Three independent researchers searched five electronic databases (EMBASE, LILACS, Medline, CENTRAL and Web of Science), for relevant articles up to February/2023 (PROSPERO CRD42023401453). The quality assessment was performed using the Newcastle Ottawa scale for case series and case reports. The primary outcome of this study was the occurrence of complete remission. The presentation of the case reports was approved by the Institutional Review Board., Results: Of the 4 cases presented, the first was a low-risk GTN with methotrexate resistance unsuccessfully treated with avelumab, which achieved remission with sequential multiagent chemotherapy. The remaining 3 cases were high-risk multiagent-resistant GTN that were successfully treated with pembrolizumab, among which there were two subsequent gestations, one of them with normal pregnancy and healthy conceptus. Regarding the systematic review, 12 studies were included, only one of them on avelumab, showing a 46.7% complete remission rate. The remaining 11 studies were on pembrolizumab, showing an 86.7% complete remission rate, regardless of tumor histology. Both immunotherapies showed good tolerability, with two healthy pregnancies being recorded: one after avelumb and another after pembrolizumab., Conclusion: Immunotherapy showed effectiveness for GTN treatment and may be especially useful in cases of high-risk disease, where pembrolizumab achieves a high therapeutic response, regardless of the histological type, and despite prior chemoresistance to multiple lines of treatment., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2023 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)