Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence
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 ou...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/3/495 |
| _version_ | 1797613055712952320 |
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| author | Matheus M. Rech Leonardo de Macedo Filho Alexandra J. White Carlos Perez-Vega Susan L. Samson Kaisorn L. Chaichana Osarenoma U. Olomu Alfredo Quinones-Hinojosa Joao Paulo Almeida |
| author_facet | Matheus M. Rech Leonardo de Macedo Filho Alexandra J. White Carlos Perez-Vega Susan L. Samson Kaisorn L. Chaichana Osarenoma U. Olomu Alfredo Quinones-Hinojosa Joao Paulo Almeida |
| author_sort | Matheus M. Rech |
| collection | DOAJ |
| description | 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 (<i>n</i> = 10), tumor management (<i>n</i> = 3), and intra- and postoperative complications (<i>n</i> = 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 (<i>n</i> = 5) and random forest (<i>n</i> = 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. |
| first_indexed | 2024-03-11T06:50:32Z |
| format | Article |
| id | doaj.art-552a4d33d4004687be8734e278ff144e |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-11T06:50:32Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-552a4d33d4004687be8734e278ff144e2023-11-17T10:00:38ZengMDPI AGBrain Sciences2076-34252023-03-0113349510.3390/brainsci13030495Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current EvidenceMatheus M. Rech0Leonardo de Macedo Filho1Alexandra J. White2Carlos Perez-Vega3Susan L. Samson4Kaisorn L. Chaichana5Osarenoma U. Olomu6Alfredo Quinones-Hinojosa7Joao Paulo Almeida8Department of Neurosurgery, University of Caxias do Sul, Caxias do Sul 95070-560, RS, BrazilDepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USADepartment of Neurosurgery, Mayo Clinic Florida, Jacksonville, FL 32224, USABackground: 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 (<i>n</i> = 10), tumor management (<i>n</i> = 3), and intra- and postoperative complications (<i>n</i> = 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 (<i>n</i> = 5) and random forest (<i>n</i> = 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.https://www.mdpi.com/2076-3425/13/3/495artificial intelligencemachine learningoutcomespituitary adenomaadenomaacromegaly |
| spellingShingle | Matheus M. Rech Leonardo de Macedo Filho Alexandra J. White Carlos Perez-Vega Susan L. Samson Kaisorn L. Chaichana Osarenoma U. Olomu Alfredo Quinones-Hinojosa Joao Paulo Almeida Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence Brain Sciences artificial intelligence machine learning outcomes pituitary adenoma adenoma acromegaly |
| title | Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence |
| title_full | Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence |
| title_fullStr | Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence |
| title_full_unstemmed | Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence |
| title_short | Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence |
| title_sort | machine learning models to forecast outcomes of pituitary surgery a systematic review in quality of reporting and current evidence |
| topic | artificial intelligence machine learning outcomes pituitary adenoma adenoma acromegaly |
| url | https://www.mdpi.com/2076-3425/13/3/495 |
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