## 5 Conclusions  
In conclusion, our research represents a significant advancement in renal cell carcinoma (RCC) prognosis through multimodal machine learning. We highlight four key contributions that collectively enhance the understanding and utility of predictive models in clinical practice.  
Firstly, our innovative approach consolidates features from diverse modalities, departing from conventional methods and providing a holistic input for machine learning models. This empowers healthcare professionals with a comprehensive tool for RCC prediction.  
Secondly, our introduction of a distinctive model fusion technique offers a more stable and accurate alternative to traditional methods, improving the reliability of prognostic assessments in real-world scenarios. In the prediction framework, machine learning prediction based on multimodal data is better than machine learning prediction based on unimodal data. The accuracy of predicting the survival status of RCC patients is higher than 75%. Moreover, in terms of machine learning model selection, the performance of fusion models is also more stable than that of single machine learning models. The interpretability aspect of the analysis is also the same as that of the single machine learning model, both of which can be disentangled relatively easily in terms of model importance.  
Thirdly, our two-stage model training framework, focusing on survival status prediction and prognosis forecasting, represents a paradigm shift in RCC prognosis. This temporal disaggregation enables unprecedented granularity in survival predictions, enhancing clinical decision-making.  
Lastly, our selection of optimal models, coupled with visualization of variable importance using the SHAP package, enables a good understanding of variables influencing RCC diagnosis and survival prediction. This helps explainability in machine learning predictions and provides invaluable guidance for medical practitioners and RCC patients in real-worldapplications. In our future research, we plan to use similar logical frameworks and ideas for the multimodal machine learning analysis of different cancers or other diseases, in which we will expand the choice of data sources, and we can consider using deep learning algorithms for the feature extraction of medical images, audio, and other kinds of data, and integrate different multimodal data into a single one to improve the utility of two-stage multimodal machine learning.  
Conceptualization, K.Y. and S.F.; methodology, K.Y.; software, K.Y.; validation, S.F.; writing--original draft preparation, K.Y.; writing--review and editing, T.L. and Q.S.; supervision, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chongqing Technology and Business University 2023 High-level Talent Research Initiation Project (Grant No. 2356013), Natural Science Foundation of Chongqing, China (CSTB2022 NSCQ-MSX1571), Guangzhou Development Zone Science and Technology (2021 GH10, 2020 GH10, EF003/FST-FSJ/2019/GSTIC), Macau FDCT (0032/2022/A, 0091/2020/A2) and University of Macau (MYRG2022-00271-FST, Collaborative Research Grant (MYRG-CRG)--CRG2021-00002-ICI).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that there are no personal or organizational conflicts of interest with this work.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RCC & renal cell carcinoma \\ SEER & Surveillance, Epidemiology, and End Results \\ SHAP & SHapley Additive exPlanations \\ ADA & Adaptive Boosting \\ ET & Extra Trees \\ GB & Gradient Boosting \\ RF & Random Forest \\ XGB & Extreme Gradient Boosting \\ \end{tabular}