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Purpose <p>Postoperative remission in pituitary adenoma (PA) patients significantly affects treatment outcomes and quality of life. Accurate prediction of remission is crucial for neurosurgeons and oncologists as it aids in personalizing treatment plans, optimizing follow-up care, and preventing unnecessary interventions. Unlike diagnostic classification, this review specifically focuses on remission prediction as a distinct prognostic application of AI. This systematic review and meta-analysis aim to evaluate the performance of machine learning (ML) algorithms in predicting remission outcomes in PA patients.</p> Methods <p>A comprehensive search of PubMed, Scopus, Embase, Web of Science, and the google scholar was conducted to identify eligible studies until Dec 2024. Data on sensitivity, specificity, accuracy, precision, F1-score, and area under the curve (AUC) were extracted from the included studies.</p> Results <p>Out of 1530 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed PA. ML algorithms, particularly artificial neural networks (ANN), offer promising performance for predicting remission outcomes in PA patients. Meta-analysis of 10 studies resulted in a pooled sensitivity of 0.84 (95% CI: 0.74–0.91), specificity of 0.84 (95% CI: 0.74–0.91), positive diagnostic likelihood ratio (DLR) of 0.19 (95% CI: 0.11–0.32), negative DLR of 15.26 (95% CI: 8.23–28.26), diagnostic odds ratio (DOR) of 28.25 (95% CI: 10.85–73.57), the diagnostic score was 3.34 (95% CI: 2.38–4.3) and an AUC of 0.91 (95% CI: 0.88–0.93).</p> Conclusion <p>ML-based models demonstrate moderate to high diagnostic accuracy in predicting remission outcomes in PA patients. While these models show promise in enhancing clinical decision-making post-surgery, further prospective validation and larger studies are necessary before their routine clinical integration.</p>

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Using machine learning to predict remission after surgery for pituitary adenoma: a systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Ibrahim Mohammadzadeh,
  • Bardia Hajikarimloo,
  • Behnaz Niroomand,
  • Pooya Eini,
  • Mohammad Amin Habibi,
  • Ali Mortezaei,
  • Mohammad Hassan Bagheri,
  • Ahmet Günkan,
  • Daniel M. Aaronson,
  • Vratko Himic,
  • Ricardo J. Komotar

摘要

Purpose

Postoperative remission in pituitary adenoma (PA) patients significantly affects treatment outcomes and quality of life. Accurate prediction of remission is crucial for neurosurgeons and oncologists as it aids in personalizing treatment plans, optimizing follow-up care, and preventing unnecessary interventions. Unlike diagnostic classification, this review specifically focuses on remission prediction as a distinct prognostic application of AI. This systematic review and meta-analysis aim to evaluate the performance of machine learning (ML) algorithms in predicting remission outcomes in PA patients.

Methods

A comprehensive search of PubMed, Scopus, Embase, Web of Science, and the google scholar was conducted to identify eligible studies until Dec 2024. Data on sensitivity, specificity, accuracy, precision, F1-score, and area under the curve (AUC) were extracted from the included studies.

Results

Out of 1530 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed PA. ML algorithms, particularly artificial neural networks (ANN), offer promising performance for predicting remission outcomes in PA patients. Meta-analysis of 10 studies resulted in a pooled sensitivity of 0.84 (95% CI: 0.74–0.91), specificity of 0.84 (95% CI: 0.74–0.91), positive diagnostic likelihood ratio (DLR) of 0.19 (95% CI: 0.11–0.32), negative DLR of 15.26 (95% CI: 8.23–28.26), diagnostic odds ratio (DOR) of 28.25 (95% CI: 10.85–73.57), the diagnostic score was 3.34 (95% CI: 2.38–4.3) and an AUC of 0.91 (95% CI: 0.88–0.93).

Conclusion

ML-based models demonstrate moderate to high diagnostic accuracy in predicting remission outcomes in PA patients. While these models show promise in enhancing clinical decision-making post-surgery, further prospective validation and larger studies are necessary before their routine clinical integration.