Gouravani Mahdi, Shahrabi Farahani Mohammad, Salehi Mohammad Amin, Shojaei Shayan, Mirakhori Sina, Harandi Hamid, Mohammadi Soheil, Saleh Ramy R
Musculoskeletal Imaging Research Center (MIRC), Tehran University of Medical Sciences, Tehran, Iran.
Medical Students Research Committee, Shahed University, Tehran, Iran.
BMC Cancer. 2025 Jan 27;25(1):155. doi: 10.1186/s12885-025-13547-9.
The detection of renal cell carcinoma (RCC) tumors in the earlier stages is of great importance for more effective treatment. Encouraged by the key role of imaging in the management of RCC, we conducted a systematic review and meta-analysis of the studies that made use of artificial intelligence (AI) for the detection of RCC to quantitatively determine the performance of AI for distinguishing related renal lesions.
PubMed, Scopus, CENTRAL, and Embase electronic databases were systematically searched in November 2024 to identify studies that applied AI for the detection or classification of RCC. We conducted a meta-analysis to evaluate the diagnostic performance of utilized algorithms. Moreover, meta-regression was conducted over suspected covariates to evaluate potential sources of inter-study heterogeneity. Publication bias and quality assessment were also done for the included studies.
Sixty-four studies were included in this systematic review, of which 31 studies were selected for meta-analysis. The studies assessing algorithms' performance on internal validation showed pooled sensitivity and specificity of 85% (95% confidence interval [CI], 82 to 87) and 76% (95% CI, 70 to 80), respectively. Moreover, externally validated Al algorithms had a pooled sensitivity and specificity of 80% (95% CI, 73 to 84) and 90% (95% CI, 84 to 93), respectively. Studies that performed internal validation for clinician performance had a pooled sensitivity of 79% (95% CI, 72 to 85) and specificity of 60% (95% CI, 49 to 70).
The findings of the present study validate the acceptable performance of AI algorithms when contrasted with medical professionals in the identification and categorization of RCC. Nevertheless, the presence of heterogeneity between studies and the absence of coherence in the results underscore the necessity for the cautious interpretation of these results and additional prospective studies.
早期检测肾细胞癌(RCC)肿瘤对于更有效的治疗至关重要。受影像学在RCC管理中的关键作用的鼓舞,我们对利用人工智能(AI)检测RCC的研究进行了系统评价和荟萃分析,以定量确定AI区分相关肾脏病变的性能。
2024年11月对PubMed、Scopus、CENTRAL和Embase电子数据库进行系统检索,以识别应用AI检测或分类RCC的研究。我们进行了荟萃分析以评估所使用算法的诊断性能。此外,对可疑协变量进行了荟萃回归,以评估研究间异质性的潜在来源。还对纳入的研究进行了发表偏倚和质量评估。
本系统评价纳入了64项研究,其中31项研究被选入荟萃分析。评估算法内部验证性能的研究显示,合并敏感性和特异性分别为85%(95%置信区间[CI],82至87)和76%(95%CI,70至80)。此外,外部验证的AI算法的合并敏感性和特异性分别为80%(95%CI,73至84)和90%(95%CI,84至93)。对临床医生性能进行内部验证的研究的合并敏感性为79%(95%CI,72至85),特异性为60%(95%CI,49至70)。
本研究结果验证了与医学专业人员相比,AI算法在识别和分类RCC方面具有可接受的性能。然而,研究之间存在异质性且结果缺乏一致性,这突出了谨慎解释这些结果和进行更多前瞻性研究的必要性。