Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

背景：对胰腺神经内分泌肿瘤（PNETs）的组织学分级进行准确检测对于患者的预后和治疗至关重要。本研究通过荟萃分析，探讨了基于放射影像的人工智能（AI）模型在预测组织学分级方面的性能。方法：对截至2023年9月之前发表的研究进行了系统文献检索，提取了研究特征和诊断指标。使用随机效应荟萃分析对估计值进行了汇总。通过QUADAS-2工具对偏倚风险进行了评估。结果：共纳入26项研究，其中20项符合荟萃分析标准。我们发现，基于AI的模型具有较高的曲线下面积（AUC）值，并显示出中等的预测价值。PNETs不同等级之间的汇总区分能力为0.89 [0.84-0.90]。通过进行亚组分析，我们发现仅包含放射组学特征的模型具有0.90 [0.87-0.92]的预测价值，I2 = 89.91%，而合并组的汇总AUC值为0.81 [0.77-0.84]，I2 = 41.54%。验证组具有0.84 [0.81-0.87]的汇总AUC值，且不存在异质性，而无需验证的组具有高度的异质性（I2 = 91.65%，P=0.000）。机器学习组的汇总AUC值为0.83 [0.80-0.86]，I2 = 82.28%。结论：AI可作为检测组织学PNETs等级的潜在工具。样本多样性、缺乏外部验证、成像方式、跨平台放射组学特征提取的不一致性、不同的建模算法和软件选择是异质性的来源。未来需要标准化成像、透明的特征选择和模型开发的统计方法，以实现放射组学结果向临床应用的转化。系统综述注册：https://www.crd.york.ac.uk/prospero/，标识符CRD42022341852。