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INTRODUCTION: The commonly used clinical indicators are not sensitive and comprehensive enough to evaluate the early staging of chronic kidney disease (CKD). This study aimed to evaluate the differences in arterial spin labeling (ASL) and blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-MRI) parameter values among patients at various stages of chronic kidney disease and healthy individuals. METHODS: Electronic databases PubMed, Web of Science, Cochrane, and Embase were searched from inception to March 29, 2024, to identify relevant studies on ASL and BOLD in CKD. The renal blood flow (RBF) and apparent relaxation rate (R2*) values were obtained from healthy individuals and patients with various stages of CKD. The meta-analysis was conducted using STATA version 12.0. The random-effects model was used to obtain estimates of the effects, and the results were expressed as 95% confidence intervals (CIs) and mean differences (MDs) of continuous variables. RESULTS: A total of 18 published studies were included in this meta-analysis. The cortical RBF and R2* values and medulla RBF values were considerably distinct between patients with various stages of CKD and healthy controls (MD, - 78.162; 95% CI, - 85.103 to - 71.221; MD, 2.440; 95% CI, 1.843 to 3.037; and MD, - 36.787; 95% CI, - 47.107 to - 26.468, respectively). No obvious difference in medulla R2* values was noted between patients with various stages of CKD and healthy controls (MD, - 1.475; 95% CI, - 4.646 to 1.696). CONCLUSION: ASL and BOLD may provide complementary and distinct information regarding renal function and could potentially be used together to gain a more comprehensive understanding of renal physiology.
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Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.
ABSTRACT
Purpose: The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods: We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results: We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions: Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.
