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PurposeThe 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.MethodsWe 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.ResultsWe 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.ConclusionsRadiomics 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 Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

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Purpose: Our study aims to investigate the underlying molecular mechanism between Lymphatic vascular space invasion (LVSI) and parametrial invasion (PMI) patients, and we screen biomarkers for patients with LVSI+ and PMI+.Methods: The main molecular mechanism of the LVSI+ and PMI+ groups was observed by using differential expression analysis and GO enrichment. Based on the results of Go enrichment, the distribution of immune infiltration was compared between the LVSI+ group and the PMI+ group by using ssGSEA analysis. Then we identified immunological differentially expressed genes (IDGs) by taking the intersection of DEGs and immune-related genes. The prognostic IDGs were screened by univariate Cox regression analysis. The Cox model was constructed by multivariate Cox regression. The prognostic ability of the two subgroups’ models was evaluated by receiver operating characteristic (ROC) curves and the area under the curve (AUC) values. Based on the genes chosen for the LVSI and PMI models, the drug sensitivity was determined on the ImmPort website.Results: The immune-related pathway differentiate LVSI from PMI in cervical cancer. The ssGSEA result showed that adaptive immunity was suppressed in LVSI+ patients, whereas in PMI+ patients, innate immunity was suppressed. The Cox model was constructed using interaction genes EREG and IL-9R for LVSI+ patients, and NODAL and IL-12A for PMI+ patients, respectively. The LVSI model and the PMI model all had better prediction power in the TCGA and GEO cohorts. we found difference in drug sensitivity between the LVSI and the PMI group.Conclusion: We proposed that the distribution of immune infiltration was the fundamental distinction in the molecular mechanism between LVSI and PMI. This study identified four metastasis mode-specific genes related to the immune infiltration, these genes strongly influenced the prognosis of LVSI+ and PMI+ cervical cancer patients, respectively.

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