ABSTRACT
OBJECTIVES: This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC). METHODS: In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell's concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration. RESULTS: The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912-0.980) and 0.864 (95% CI: 0.734-0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group. CONCLUSIONS: The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC. CRITICAL RELEVANCE STATEMENT: We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades. KEY POINTS: 1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells.
ABSTRACT
Coal is a typical dual-porosity medium. The implementation process of water invasion technology in coal is actually a process of spontaneous imbibition of external water. To obtain a model of spontaneous capillary imbibition in coal, the spontaneous imbibition of water in coal samples with different production loads is conducted experimentally. Due to the coal particle deformation and the cohesive forces, the porosity and maximum diameter decrease gradually with increasing pressing loads. Due to the filling effects and occupying effects, the proper particle grading can reduce the porosity and tortuosity. The Comiti model can be used to describe the tortuosity. The tortuosity increases with decreasing porosity. The smaller the porosity, the smoother the surface of the coal sample. The contact angle is negatively correlated with the surface roughness. The fractal dimension decreases with increasing pressing load. The difference in the pore characteristics between particles is the main reason for the difference in the fractal dimension. The proposed model of spontaneous capillary imbibition in coal is consistent with the experimental data. The implications of this study are important for understanding the law of spontaneous imbibition in coal and the displacement of gas by spontaneous capillary imbibition in coal, which is important for optimizing the parameters of coal seam water injection.
