Purpose

Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies.

Methodology

Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool.

Results

In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist’s interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810−0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744−0.852) for colorectal malignancy.

Conclusion

Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist’s assessment. Deep learning models may further improve on this, but data remain limited.

中文翻译：

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人工智能在腹盆腔恶性肿瘤患者淋巴结转移诊断中的应用：系统评价和荟萃分析

目的

淋巴结转移的准确临床诊断对于腹盆腔恶性肿瘤患者的治疗至关重要。本综述评估了深度学习算法和放射组学模型对腹盆腔恶性肿瘤淋巴结转移的诊断性能。

方法

搜索了 Embase（PubMed、MEDLINE）、Science Direct 和 IEEE Xplore 数据库，以确定 2009 年 1 月至 2019 年 3 月期间发表的合格研究。选择了通过 CT 或 MRI 报告腹盆腔恶性肿瘤的深度学习算法或放射组学模型准确性的研究。提取研究特征和诊断措施。使用随机效应荟萃分析汇总估计值。使用 QUADAS-2 工具进行偏倚风险评估。

结果

总共确定了 498 项可能符合条件的研究，其中 21 项被纳入，17 项为定量分析提供了足够的信息。研究具有异质性，在 18 项研究中发现了相当大的偏倚风险。几乎所有研究都采用放射组学模型（n = 20）。单一发布的深度学习模型在 AUROC 较高的情况下优于放射组学模型（0.912 对 0.895），但放射组学和深度学习模型都优于放射科医师的单独解释（0.774）。肿瘤亚型中放射组学列线图的汇总结果表明，泌尿系统恶性肿瘤的 AUC 最高为 0.895（95%CI，0.810-0.980），结直肠恶性肿瘤的 AUC 最低为 0.798（95%CI，0.744-0.852）。

结论

与放射科医师的评估相比，放射组学模型提高了腹盆腔恶性肿瘤淋巴结分期的诊断准确性。深度学习模型可能会在这方面进一步改进，但数据仍然有限。