Introduction:Following the rapid advances in minimally invasive surgery, there are a multitude of surgical modalities available for resecting rectal cancers. Robotic resections represent the current pinnacle of surgical approaches. Currently, decisions on the surgical modality depend on local resources and the expertise of the surgical team. Given limited access to robotic surgery, developing tools based on pre-operative data that can predict the difficulty of surgery would streamline the efficient utilisation of resources. This systematic review aims to appraise the existing literature on artificial intelligence (AI)-driven preoperative MRI analysis for surgical difficulty prediction to identify knowledge gaps and promising models warranting further clinical evaluation.Methods:A systematic review and narrative synthesis were undertaken in accordance with PRISMA and SWiM guidelines. Systematic searches were performed on Medline, Embase, and the CENTRAL Trials register. Studies published between 2012 and 2024 were included where AI was applied to preoperative MRI imaging of adult rectal cancer patients undergoing surgeries, of any approach, for the purpose of stratifying surgical difficulty. Data were extracted according to a pre-specified protocol to capture study characteristics and AI design; the objectives and performance outcome metrics were summarised.Results:Systematic database searches returned 568 articles, 40 ultimately included in this review. AI to support preoperative difficulty assessments were identified across eight domains (direct surgical difficulty grading, extramural vascular invasion (EMVI), lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), T staging, and the requirement for multiple linear stapler firings. For each, at least one model was identified with very good performance (AUC scores of >0.80), with several showing excellent performance considerably above this threshold.Conclusions:AI tools applied to preoperative rectal MRI to support preoperative difficulty assessment for rectal cancer surgeries are emerging, with the progressing development and strong performance of many promising models. These warrant further clinical evaluation, which can aid personalised surgical approaches and ensure the adequate utilisation of limited resources.
引言：随着微创手术技术的快速发展，目前已有多种手术方式可用于直肠癌切除。机器人切除术代表了当前手术方法的最高水平。目前，手术方式的选择取决于当地医疗资源和手术团队的专业水平。鉴于机器人手术的可及性有限，开发基于术前数据预测手术难度的工具将有助于优化资源配置效率。本系统综述旨在评估现有关于人工智能（AI）驱动的术前MRI分析用于预测手术难度的文献，以识别知识空白及值得进一步临床评估的潜力模型。 方法：根据PRISMA和SWiM指南进行系统综述与叙述性综合。在Medline、Embase和CENTRAL临床试验注册库中进行了系统性检索。纳入2012年至2024年间发表的研究，这些研究将AI应用于接受任何术式手术的成年直肠癌患者的术前MRI影像，旨在分层评估手术难度。按照预先制定的方案提取数据，涵盖研究特征与AI设计；对研究目标及性能评估指标进行总结。 结果：系统性数据库检索获得568篇文献，最终40篇纳入本综述。AI支持的术前难度评估涵盖八个领域（直接手术难度分级、壁外血管侵犯（EMVI）、淋巴结转移（LNM）、淋巴血管侵犯（LVI）、神经周围侵犯（PNI）、T分期及多重线性吻合器击发需求）。每个领域至少有一个模型表现出优异性能（AUC值>0.80），其中多个模型的性能远超该阈值。 结论：应用于直肠术前MRI以支持直肠癌手术难度评估的AI工具正在兴起，许多潜力模型在持续发展中展现出强劲性能。这些工具值得进一步临床评估，有助于实现个体化手术方案并确保有限医疗资源的合理利用。
肿瘤(癌症)患者之家