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Fig. 6 | Breast Cancer Research

Fig. 6

From: Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study

Fig. 6

Association of lncRNA KRT7-AS with RFS and radiomics. a Kaplan–Meier curves of RFS according to the expression of lncRNA KRT7-AS. b Overall distribution and c differential expression of the radiomic features from T1 + C and T2WI sequences in patients with high and low expression of lncRNA KRT7-AS, *P < 0.05, **P < 0.01. d The lncRNA KRT7-AS-related pathways and immune cells. e The GSVA pathway enrichment analysis of lncRNA KRT7-AS-based genes. f ROC curves and AUCs were used to assess the accuracy of the deep learning model for predicting lncRNA KRT7-AS expression. P values were calculated using the unadjusted log-rank test, and hazard ratios were calculated by a univariate Cox regression analysis. AUC, area under the receiver operating characteristics curve; CI, confidence interval; GSVA, gene set variation analysis; HR, hazard ratio; LncRNA, long non-coding RNA; ROC, receiver operating characteristic; T1 + C, contrast-enhanced T1-weighted imaging; T2WI, T2-weighted imaging

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Breast Cancer Research

ISSN: 1465-542X

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