Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

Objectives: This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. Methods: A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Results: Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Conclusion: Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.

Upregulation of BMP1 through ncRNAs correlates with adverse outcomes and immune infiltration in clear cell renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) accounts for approximately 2-3% of all adult malignancies. Clear cell renal cell carcinoma (ccRCC), which comprises 70-80% of all RCC cases, is the most common histological subtype. METHODS: ccRCC transcriptome data and clinical information were downloaded from the TCGA database. We used the TCGA and GEPIA databases to analyze relative expression of BMP1 in various types of human cancer. GEPIA was used to perform survival analysis for BMP1 in various cancer types. Upstream binding miRNAs of BMP1 were obtained through several important target gene prediction tools. StarBase was used to predict candidate miRNAs that may bind to BMP1 and candidate lncRNAs that may bind to hsa-miR-532-3p. We analyzed the association between expression of BMP1 and immune cell infiltration levels in ccRCC using the TIMER website. The relationship between BMP1 expression levels and immune checkpoint expression levels was also investigated. RESULTS: BMP1 was upregulated in GBM, HNSC, KIRC, KIRP and STAD and downregulated in KICH and PRAD. Combined with OS and DFS, BMP1 can be used as a biomarker for poor prognosis among patients with KIRC. Through expression analysis, survival analysis and correlation analysis, LINC00685, SLC16A1-AS1, PVT1, VPS9D1-AS1, SNHG15 and the CCDC18-AS1/hsa-miR-532-3p/BMP1 axis were established as the most potential upstream ncRNA-related pathways of BMP1 in ccRCC. Furthermore, we found that BMP1 levels correlated significantly positively with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression. CONCLUSION: Our results demonstrate that ncRNA-mediated high expression of BMP1 is associated with poor prognosis and tumor immune infiltration in ccRCC.

A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).

RATIONALE AND OBJECTIVES: Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM). MATERIALS AND METHODS: One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness. RESULTS: Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature. CONCLUSIONS: The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.

CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer.

Objectives: Although the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa. Methods: A retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non-muscle-invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model. Results: According to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis. Conclusions: A deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer.