Tang Ning, Pan Shicen, Zhang Qirong, Zhou Jian, Zuo Zhiwei, Jiang Rui, Sheng Jinping
The General Hospital of Western Theater Command, Chengdu, Sichuan 610083, China, Chengdu, China.
Joint Security Forces 945 Hospital, Yaan, China.
Abdom Radiol (NY). 2025 Jan 22. doi: 10.1007/s00261-024-04713-x.
Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC.
A comprehensive literature search was conducted across PubMed, Embase, and Web of Science for studies published up to July 28, 2024. Inclusion criteria focused on studies using radiomics models to predict PNI in CRC with sufficient data to construct diagnostic accuracy metrics. The quality of the included studies was assessed using QUADAS-2 and METRICS tools. Pooled estimates of sensitivity, specificity, and area under the curve (AUC) were calculated. Subgroup analyses were performed based on imaging modalities, segmentation methods, and other variables.
Twelve studies comprising 2853 patients were included in the systematic review, with ten studies contributing to the meta-analysis. The pooled sensitivity and specificity for radiomics models in predicting PNI were 0.74 (95% CI: 0.63-0.82) and 0.85 (95% CI: 0.79-0.90), respectively, in the training cohorts. In the validation cohorts, the sensitivity was 0.65 (95% CI: 0.57-0.72), and specificity was 0.85 (95% CI: 0.81-0.89). The AUC was 0.87 (95% CI: 0.63-0.82) for the training cohorts and 0.84 (95% CI: 0.81-0.87) for the validation cohorts, indicating good diagnostic accuracy. The METRICS scores for the included studies ranged from 65.8 to 85.1%, with an overall average score of 67.25%, reflecting good methodological quality. However, significant heterogeneity was observed across studies, particularly in sensitivity and specificity estimates.
Radiomics models show promise as a non-invasive tool for predicting PNI in CRC, with moderate to good diagnostic accuracy. However, the current study's limitations, including reliance on retrospective data, geographic concentration in China, and methodological variability, suggest that further research is needed. Future studies should focus on prospective designs, standardization of methodologies, and the integration of advanced machine-learning techniques to improve the clinical applicability and reliability of radiomics models in CRC management.
结直肠癌（CRC）中的神经周围侵犯（PNI）是一个与不良预后相关的重要预后因素。放射组学是一种从医学影像中提取定量特征的技术，已成为预测PNI的潜在工具。本系统评价和荟萃分析旨在评估放射组学模型在预测CRC中PNI的诊断准确性。
在PubMed、Embase和Web of Science上进行了全面的文献检索，以查找截至2024年7月28日发表的研究。纳入标准侧重于使用放射组学模型预测CRC中PNI且有足够数据构建诊断准确性指标的研究。使用QUADAS-2和METRICS工具评估纳入研究的质量。计算敏感性、特异性和曲线下面积（AUC）的合并估计值。基于成像方式、分割方法和其他变量进行亚组分析。
系统评价纳入了12项研究，共2853例患者，其中10项研究纳入荟萃分析。在训练队列中，放射组学模型预测PNI的合并敏感性和特异性分别为0.74（95%CI：0.63-0.82）和0.85（95%CI：0.79-0.90）。在验证队列中，敏感性为0.65（9%CI：0.57-0.72），特异性为0.85（95%CI：0.81-0.89）。训练队列的AUC为0.87（95%CI：0.63-0.82），验证队列的AUC为0.84（95%CI：0.81-0.87），表明诊断准确性良好。纳入研究的METRICS评分范围为65.8%至85.1%，总体平均评分为67.25%，反映出良好的方法学质量。然而，各研究之间存在显著异质性，尤其是在敏感性和特异性估计方面。
放射组学模型有望成为预测CRC中PNI的非侵入性工具，具有中等至良好的诊断准确性。然而，当前研究存在局限性，包括依赖回顾性数据、在中国的地域集中性以及方法学的变异性，这表明需要进一步研究。未来的研究应侧重于前瞻性设计、方法学标准化以及先进机器学习技术的整合，以提高放射组学模型在CRC管理中的临床适用性和可靠性。
