Varga Petra, Obeidat Mahmoud, Máté Vanda, Kói Tamás, Kiss-Dala Szilvia, Major Gréta Szilvia, Tímár Ágnes Eszter, Li Ximeng, Szilágyi Ádám, Csáki Zsófia, Engh Marie Anne, Garami Miklós, Hegyi Péter, Túri Ibolya, Tuboly Eszter
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
Heim Pál National Pediatric Institute, Budapest, Hungary.
EClinicalMedicine. 2024 Nov 21;78:102902. doi: 10.1016/j.eclinm.2024.102902. eCollection 2024 Dec.
Current paediatric cancer care requires innovative approaches to predict prognosis that facilitates personalised stratification, yet studies on the performance, composition and limitations of contemporary prognostic models are lacking. We aimed to compare the accuracy of traditional and advanced prognostic models.
A systematic search for this systematic review and meta-analysis (CRTN42022370251) was conducted in PubMed, Embase, Scopus, and the Cochrane Library databases on 28 June 2024. Studies on the accuracy of prognostic markers or models used in paediatric haematological malignancies, central nervous system (CNS), or non-CNS solid tumours (NCNSST) were included. Three model categories were defined using: 1-clinical parameters, 2-genomic-transcriptomic data, and 3-artificial intelligence (AI). Primary outcomes were area under the receiver operating characteristic curve with a 95% confidence interval (CI) for various overall survival intervals and event-free survival. Two independent groups performed selection and data extraction. We used data published by the authors and publicly available databases.
Of 12,982 studies, 358 were included in the meta-analysis and 27 in the systematic review, with limited data on AI-approaches. Most data were reported on NCNSST at 5-year OS, where a statistically significant difference was observed between Category-1 (0.75 CI: 0.72-0.79) and Category-2 (0.85 CI: 0.82-0.88) (p < 0.001), but not between Categories-2 and -3 (p = 0.2834) (0.82 CI: 0.77-0.88). Internal validation studies showed significantly better performance compared to those using external validation, highlighting the high risk of bias (ROB) inherent in internal validation. High ROB was most commonly experienced in the outcomes and statistical analysis domains, assessed using PROBAST and QUIPS.
It is advisable to introduce Category-2 and -3 models in a clinical setting, especially for NCNSST prognostic for aiding risk-stratification. Although AI-supported predictions in paediatric oncology are at an early stage of development, it is imperative to further explore their potential. This requires structured data collection and ethical sharing from paediatric oncology patients in sufficient quantity and quality.
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当前的儿科癌症护理需要创新方法来预测预后，以促进个性化分层，但缺乏关于当代预后模型的性能、组成和局限性的研究。我们旨在比较传统和先进预后模型的准确性。
2024年6月28日，在PubMed、Embase、Scopus和Cochrane图书馆数据库中对本系统评价和荟萃分析（CRTN42022370251）进行了系统检索。纳入了关于儿科血液系统恶性肿瘤、中枢神经系统（CNS）或非CNS实体瘤（NCNSST）中使用的预后标志物或模型准确性的研究。使用以下标准定义了三类模型：1-临床参数，2-基因组-转录组数据，3-人工智能（AI）。主要结局是不同总生存间隔和无事件生存的受试者工作特征曲线下面积及其95%置信区间（CI）。由两个独立的小组进行筛选和数据提取。我们使用作者发表的数据和公开可用的数据库。
在12982项研究中，358项纳入荟萃分析，27项纳入系统评价，关于AI方法的数据有限。大多数数据是关于NCNSST的5年总生存情况，其中第1类（0.75，CI：0.72 - 0.79）和第2类（0.85，CI：0.82 - 0.88）之间观察到统计学显著差异（p < 0.001），但第2类和第3类之间没有差异（p = 0.2834）（0.82，CI：0.77 - 0.88）。内部验证研究显示与使用外部验证的研究相比性能显著更好，突出了内部验证中固有的高偏倚风险（ROB）。使用PROBAST和QUIPS评估发现，高ROB最常出现在结局和统计分析领域。
在临床环境中引入第2类和第3类模型是可取的，特别是对于NCNSST的预后以辅助风险分层。尽管儿科肿瘤学中人工智能支持的预测尚处于早期发展阶段，但必须进一步探索其潜力。这需要从儿科肿瘤患者那里进行结构化的数据收集和符合伦理的高质量、足量的共享。
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