Objective

Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer.

Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374).

Results

Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76–87%), 84% (95% CI: 79–88%), and 90% (95% CI: 87–92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005).

Conclusions

Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

中文翻译：

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基于图像的人工智能预测直肠癌患者新辅助放化疗的病理完全反应：系统评价和荟萃分析

客观的

人工智能 (AI) 在无创识别直肠癌患者方面具有巨大潜力，这些患者在新辅助放化疗 (nCRT) 后可以实现病理完全缓解 (pCR)。我们的目的是进行一项荟萃分析，总结基于图像的 AI 模型在预测直肠癌患者的 pCR 到 nCRT 方面的诊断性能。

方法

本研究遵循系统评价和荟萃分析指南的首选报告项目。从开始到 2023 年 7 月 29 日，对 PubMed、Embase、Cochrane 图书馆和 Web of Science 进行了文献检索。其中包括开发或利用 AI 模型从医学图像预测直肠癌的 pCR 到 nCRT 的研究。诊断准确性研究的质量评估-AI用于评估研究的方法学质量。双变量随机效应模型用于总结个体敏感性、特异性和曲线下面积 (AUC)。进行亚组和荟萃回归分析以确定异质性的潜在来源。本研究的方案已在 PROSPERO 注册（CRD42022382374）。

结果

确定了 34 项研究（9933 名患者）。用于 pCR 预测的 AI 模型的敏感性、特异性和 AUC 的汇总估计值分别为 82%（95% CI：76-87%）、84%（95% CI：79-88%）和 90%（95% CI： 87-92%），分别。与非亚洲人群、高偏倚风险和放射组学相比，亚洲人群、低偏倚风险和深度学习的特异性更高（所有P  < 0.05）。单中心的敏感性高于多中心（P  = 0.001）。 与前瞻性设计相比，回顾性设计的敏感性较低（P  =0.012），但特异性较高（P <0.001）。 与非 MRI 相比，MRI 的敏感性较高（P  = 0.001），但特异性较低（P = 0.044）。内部验证的敏感性和特异性均高于外部验证（P  =0.005）。

结论

基于图像的 AI 模型在预测直肠癌的 pCR 到 nCRT 方面表现出良好的性能。然而，需要进一步的临床试验来验证这些发现。