Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia; Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia; Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Artif Intell Med. 2021 Mar;113:102022. doi: 10.1016/j.artmed.2021.102022. Epub 2021 Feb 2.
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.
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.
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.
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.
准确的临床诊断淋巴结转移在治疗腹盆腔恶性肿瘤患者中至关重要。本综述评估了深度学习算法和放射组学模型在腹盆腔恶性肿瘤淋巴结转移中的诊断性能。
通过 Embase（PubMed、MEDLINE）、Science Direct 和 IEEE Xplore 数据库，检索了 2009 年 1 月至 2019 年 3 月期间发表的符合条件的研究。选择报告了基于 CT 或 MRI 的深度学习算法或放射组学模型在腹盆腔恶性肿瘤中的准确性的研究。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 工具评估偏倚风险。
共确定了 498 项潜在的合格研究，其中 21 项研究被纳入，17 项研究提供了足够的定量分析信息。研究存在异质性，18 项研究存在较大的偏倚风险。几乎所有研究都使用了放射组学模型（n=20）。唯一发表的深度学习模型的 AUC（0.912 对 0.895）优于放射组学模型，但放射组学和深度学习模型均优于单独的放射科医生解释（0.774）。在肿瘤亚型中，放射组学列线图的汇总结果显示，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）。
与放射科医生的评估相比，放射组学模型可提高腹盆腔恶性肿瘤淋巴结分期的诊断准确性。深度学习模型可能在此基础上进一步提高，但数据仍然有限。
