Jong Bor-Kang, Yu Zhen-Hao, Hsu Yu-Jen, Chiang Sum-Fu, You Jeng-Fu, Chern Yih-Jong
Colorectal Section, Department of Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
School of Medicine, Chang Gung University, Taoyuan, Taiwan.
Int J Colorectal Dis. 2025 Jan 20;40(1):19. doi: 10.1007/s00384-025-04809-w.
This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy.
The review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as "artificial intelligence," "rectal cancer," "MRI," and "pathological complete response." Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applications. Data on study characteristics, MRI sequences, AI model details, and performance metrics were extracted. Quality assessment was performed using the PROBAST tool.
Out of 512 initial records, 26 studies met the inclusion criteria. Most studies demonstrated promising diagnostic performance, with AUC values for external validation typically exceeding 0.8. The use of T2W and diffusion-weighted imaging (DWI) MRI phases enhanced model accuracy compared to T2W alone. Larger datasets generally correlated with improved model performance. However, heterogeneity in model designs, MRI protocols, and the limited integration of clinical data were noted as challenges.
AI-enhanced MRI demonstrates significant potential in predicting pCR in rectal cancer, particularly with T2W + DWI sequences and larger datasets. While integrating clinical data remains controversial, standardizing methodologies and expanding datasets will further enhance model robustness and clinical utility.
本系统评价探讨深度学习算法在预测接受新辅助放化疗（nCRT）的直肠癌患者病理完全缓解（pCR）中的效用。主要目标是评估基于磁共振成像（MRI）的人工智能（AI）模型的性能，并探索影响其诊断准确性的因素。
本评价遵循PRISMA指南，并在国际前瞻性系统评价注册库（PROSPERO，注册号：CRD42024628017）进行了注册。在PubMed、Embase和Cochrane图书馆中使用“人工智能”、“直肠癌”、“MRI”和“病理完全缓解”等关键词进行文献检索。纳入涉及应用于MRI以预测pCR的深度学习模型的文章，排除非MRI数据和未应用AI的研究。提取有关研究特征、MRI序列、AI模型细节和性能指标的数据。使用PROBAST工具进行质量评估。
在512条初始记录中，26项研究符合纳入标准。大多数研究显示出有前景的诊断性能，外部验证的曲线下面积（AUC）值通常超过0.8。与单独使用T2加权成像（T2W）相比，使用T2W和扩散加权成像（DWI）MRI期可提高模型准确性。更大的数据集通常与模型性能的改善相关。然而，模型设计、MRI协议的异质性以及临床数据的有限整合被视为挑战。
AI增强的MRI在预测直肠癌的pCR方面显示出巨大潜力，特别是使用T2W + DWI序列和更大的数据集时。虽然整合临床数据仍存在争议，但标准化方法和扩大数据集将进一步提高模型的稳健性和临床效用。
