Palavani Lucca B, Nogueira Bernardo Vieira, Mitre Lucas Pari, Chen Hsien-Chung, Müller Gean Carlo, Vilardo Marina, Pereira Vinicius G, Paleare Luis F Fabrini, Ribeiro Filipe Virgilio, Araujo Arthur Antônio Soutelo, Ferreira Marcio Yuri, Varre Harivardhani, Ferreira Christian, Paiva Wellingson Silva, Bertani Raphael, D Amico Randy S, Neville Iuri Santana
Max Planck University Center, Indaiatuba, Brazil.
Serra Dos Órgãos University Center, Teresópolis, Brazil.
Neurosurg Rev. 2025 Aug 6;48(1):591. doi: 10.1007/s10143-025-03718-4.
Differentiating pseudoprogression (PsP) from true progression (TP) in high-grade glioma (HGG) patients is still challenging and critical for effective treatment management. This meta-analysis evaluates the diagnostic accuracy of artificial intelligence (AI) algorithms in making this distinction. We aimed to assess the performance of AI algorithms in distinguishing between pseudoprogression and true progression in patients with high-grade glioma. We searched PubMed, Cochrane, and Embase databases for studies reporting on AI algorithms that differentiate pseudoprogression from true progression in high-grade gliomas. The analysis evaluated reported metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. The meta-analysis included 26 articles involving 1,972 patients. In the high-grade glioma group, AI algorithms demonstrated a sensitivity of 88% (95% CI: 77%-100%) and a specificity of 75% (95% CI: 54%-97%). For the glioblastoma (GBM) group, accuracy was 77% (95% CI: 68%-86%), with sensitivity of 77% (95% CI: 67%-86%) and specificity of 63% (95% CI: 43%-82%). Overall, the algorithms achieved an accuracy of 80% (95% CI: 76%-85%), sensitivity of 85% (95% CI: 80%-91%), specificity of 69% (95% CI: 58%-80%), a PPV of 79% (95% CI: 58%-100%), a NPV of 97% (95% CI: 90%-100%), and an F1 score of 74% (95% CI: 67%-81%). AI algorithms show significant promise in accurately distinguishing between pseudoprogression and true progression in high-grade gliomas, suggesting their potential utility in clinical decision-making.
在高级别胶质瘤（HGG）患者中区分假性进展（PsP）和真性进展（TP）仍然具有挑战性，且对于有效的治疗管理至关重要。这项荟萃分析评估了人工智能（AI）算法在进行这种区分时的诊断准确性。我们旨在评估AI算法在区分高级别胶质瘤患者假性进展和真性进展方面的性能。我们在PubMed、Cochrane和Embase数据库中搜索了关于区分高级别胶质瘤假性进展和真性进展的AI算法的研究报告。该分析评估了报告的指标，如准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和F1分数。该荟萃分析纳入了26篇涉及1972例患者的文章。在高级别胶质瘤组中，AI算法的敏感性为88%（95%CI：77%-100%），特异性为75%（95%CI：54%-97%）。对于胶质母细胞瘤（GBM）组，准确性为77%（95%CI：68%-86%），敏感性为77%（95%CI：67%-86%），特异性为63%（95%CI：43%-82%）。总体而言，这些算法的准确性为80%（95%CI：76%-85%），敏感性为85%（95%CI：80%-91%），特异性为69%（95%CI：58%-80%），PPV为79%（95%CI：58%-100%），NPV为97%（95%CI：90%-100%），F1分数为74%（95%CI：67%-81%）。AI算法在准确区分高级别胶质瘤的假性进展和真性进展方面显示出巨大前景，表明它们在临床决策中具有潜在效用。
