Yang Darrion B, Smith Alexander D, Smith Emily J, Naik Anant, Janbahan Mika, Thompson Charee M, Varshney Lav R, Hassaneen Wael
Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States.
Department of Communication, University of Illinois Urbana Champaign, Champaign, Illinois, United States.
J Neurol Surg B Skull Base. 2022 Nov 23;84(6):548-559. doi: 10.1055/a-1941-3618. eCollection 2023 Dec.
The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model-agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.
本分析的目的是评估机器学习（ML）算法在预测经蝶窦手术的术后结果（包括并发症、复发和死亡）中的应用。按照系统评价和荟萃分析的首选报告项目（PRISMA）指南，我们系统地回顾了所有使用至少一种ML算法来预测经蝶窦手术后结果的论文。我们在Scopus、PubMed和科学网数据库中搜索了2021年5月12日之前发表的研究。我们确定了13项研究，共纳入5048例患者。我们提取了每项研究的一般特征；所开发的ML模型的敏感性、特异性、曲线下面积（AUC）以及被ML模型确定为重要的特征。我们确定了12项涉及5048例患者的研究，这些研究包括针对腺瘤的ML算法，3项涉及1807例专门针对肢端肥大症患者的研究，以及5项涉及2105例专门针对库欣病患者的研究。几乎所有研究均为单机构研究。这些研究使用了多种不同的ML算法和特征来构建预测模型。所有论文报告的AUC均大于0.7，这表明具有临床实用性。ML算法有潜力预测经蝶窦手术的术后结果，并可改善患者护理。与其他ML算法相比，集成算法和神经网络通常表现最佳。生化和术前特征最有可能被ML模型选为重要特征。难以解释仍然是一个挑战，但诸如局部可解释模型无关解释或沙普利值等算法可以提高ML算法的可解释性。我们的分析表明，ML算法有潜力在很大程度上协助外科医生进行临床决策。
