Academic radiology · 2026
Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis.
СтатьяСистематический обзорМетаанализПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Aljbri Afaf, You Qiwen, Aljbri Abdullah, Aljbri Kholoud, Qiao Xin, Liu Jiaxi, Shao Haibo
Аннотация
RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of preoperative computed tomography (CT) and magnetic resonance imaging (MRI)-based radiomics models in detecting liver metastases in patients with colorectal cancer (CRC).
MATERIALS AND METHODS: Following PRISMA 2020 guidelines, we systematically searched major databases up to July 2025. Study selection, data extraction, and quality assessment (Radiomics Quality Score and QUADAS-2) were performed independently. Separate bivariate random-effects meta-analyses were conducted for prognostic (metachronous) and diagnostic (synchronous) predictions.
RESULTS: Twenty studies (3765 patients) were included in the systematic review. Twenty studies were included in the systematic review. Of these, 18 studies were included in the quantitative meta-analysis. For predicting metachronous metastases (13 studies), the pooled AUC was 0.83 (95% CI: 0.73-0.90), although significant publication bias suggested that this estimate may be optimistically inflated. For the detection of synchronous metastases (five studies), the pooled AUC was 0.85 (95% CI: 0.76-0.91). Heterogeneity was moderate to substantial. However, significant publication bias was detected for prognostic models (Deeks' test, P < 0.001), suggesting that these pooled estimates may be optimistically inflated.
CONCLUSION: Radiomics has the potential to predict metachronous and detect synchronous liver metastases in CRC. However, methodological weaknesses (mean Radiomics Quality Score ∼48%), geographic bias, and publication bias limit this evidence. Multinational validation is required before clinical application of the findings.
MATERIALS AND METHODS: Following PRISMA 2020 guidelines, we systematically searched major databases up to July 2025. Study selection, data extraction, and quality assessment (Radiomics Quality Score and QUADAS-2) were performed independently. Separate bivariate random-effects meta-analyses were conducted for prognostic (metachronous) and diagnostic (synchronous) predictions.
RESULTS: Twenty studies (3765 patients) were included in the systematic review. Twenty studies were included in the systematic review. Of these, 18 studies were included in the quantitative meta-analysis. For predicting metachronous metastases (13 studies), the pooled AUC was 0.83 (95% CI: 0.73-0.90), although significant publication bias suggested that this estimate may be optimistically inflated. For the detection of synchronous metastases (five studies), the pooled AUC was 0.85 (95% CI: 0.76-0.91). Heterogeneity was moderate to substantial. However, significant publication bias was detected for prognostic models (Deeks' test, P < 0.001), suggesting that these pooled estimates may be optimistically inflated.
CONCLUSION: Radiomics has the potential to predict metachronous and detect synchronous liver metastases in CRC. However, methodological weaknesses (mean Radiomics Quality Score ∼48%), geographic bias, and publication bias limit this evidence. Multinational validation is required before clinical application of the findings.