โก Quick Summary
This study developed a machine learning model for predicting renal cell carcinoma (RCC) using routine clinical data, achieving an impressive AUC of 0.955 in validation. This innovative approach aims to enhance early detection and improve patient outcomes in RCC.
๐ Key Details
- ๐ Dataset: Clinical data from Quanzhou First Hospital, covering March 2014 to March 2024
- ๐งฉ Features used: 21 clinically relevant variables including age, total protein, and hemoglobin
- โ๏ธ Technology: eXtreme Gradient Boosting (XGBoost)
- ๐ Performance: AUC of 0.955 and average precision of 0.923 in the validation cohort
๐ Key Takeaways
- ๐ Early detection of RCC is crucial for improving patient outcomes.
- ๐ค Machine learning was effectively utilized to construct a predictive model for RCC.
- ๐ The model achieved an AUC of 0.955, indicating high predictive accuracy.
- ๐ก 21 variables were identified as significant predictors for RCC.
- ๐ The Shapley Additive Explanations method provided insights into model features and individual predictions.
- ๐ This approach offers a cost-effective solution for large-scale RCC screening.
- ๐ฅ Conducted at Quanzhou First Hospital Affiliated with Fujian Medical University.
- ๐๏ธ Study period: Data collected over a decade, from 2014 to 2024.
๐ Background
Renal cell carcinoma (RCC) is a significant health concern, being the leading cause of morbidity and mortality in the urinary system. Early identification of RCC is essential for improving treatment outcomes and survival rates. Traditional diagnostic methods often fall short in terms of sensitivity and specificity, highlighting the need for innovative approaches such as machine learning to enhance predictive capabilities.
๐๏ธ Study
This study aimed to develop and validate a machine learning model for predicting RCC in at-risk individuals. Researchers collected retrospective data from the Quanzhou First Hospital, employing various machine learning algorithms to identify the most effective model. The study utilized univariate and hierarchical clustering methods to select features that would optimize model performance.
๐ Results
The eXtreme Gradient Boosting algorithm emerged as the top performer, achieving an AUC of 0.955 and an average precision of 0.923 in the validation cohort. The model demonstrated high discrimination and calibration, indicating a strong agreement between predicted and observed risks. The Shapley Additive Explanations method effectively illustrated the importance of each feature, providing valuable insights for clinicians.
๐ Impact and Implications
The development of this machine learning model represents a significant advancement in the early detection of RCC. By leveraging routine clinical data, this cost-effective approach can facilitate large-scale screening, potentially leading to earlier interventions and improved patient outcomes. The integration of machine learning in clinical practice could transform how RCC is diagnosed and managed, ultimately saving lives.
๐ฎ Conclusion
This study highlights the remarkable potential of machine learning in predicting renal cell carcinoma. With an impressive AUC of 0.955, the model offers a promising tool for early detection and intervention. As we continue to explore the integration of AI in healthcare, the future looks bright for improving patient outcomes in RCC and beyond.
๐ฌ Your comments
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Prediction of renal cell carcinoma: Development and validation of machine learning model.
Abstract
Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient outcomes. This study aims to construct and validate an RCC prediction model for at-risk individuals using machine learning (ML) based on routine clinical data. Data from the Quanzhou First Hospital Affiliated with Fujian Medical University between March 2014 and March 2024 were retrospectively collected, with 70% randomly assigned to the training cohort and 30% to the validation cohort. Univariate and hierarchical clustering methods were employed to identify discriminatory features to enable optimal ML algorithm selection. The performance of 7 kinds of ML algorithms-based models was evaluated based on sensitivity (recall), accuracy, F1-score, area under the receiver operating curve (AUC), discrimination, calibration, and clinical net benefit. The algorithm achieving the best AUC was selected for combination with recursive feature elimination to identify features that maximize model performance and stability. After that, the RCC prediction model was finally constructed, and the Shapley Additive Explanations method was used to visualize model characteristics and individual case predictions. Among those algorithms, the eXtreme Gradient Boosting algorithm achieving the best performance was selected for final construction. Combined with the recursive feature elimination method, it identified 21 clinically relevant variables, including age, total protein, albumin, total bilirubin, alanine aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, glucose, lactate dehydrogenase, creatine kinase-MB, creatinine, potassium-chloride ratio, sodium ion, calcium ion, eosinophil count, hemoglobin, platelet count, Systemic Immune-Inflammation Index, Pan-Immune-Inflammation Value, platelet-lymphocyte ratio, and sodium-chloride ratio for RCC model construction. Subsequently, a RCC prediction model and eXtreme Gradient Boosting using these 21 variables was built, achieving AUC of 0.955 (95% CI: 0.938-0.976) and an average precision of 0.923 in the validation cohort. The additional calibration curve showed high agreement between predicted and observed risks. Finally, the Shapley Additive Explanations method well demonstrated the importance of all model features and provided case-specific interpretation for clinicians. We developed and validated an ML model using routine clinical data for large-scale RCC screening. This cost-effective approach facilitates the early detection of and intervention for RCC, which may lead to improved clinical outcomes.
Author: [‘Zheng T’, ‘Xu R’, ‘Zhang J’, ‘Xu Y’, ‘Zeng C’, ‘Zhang Z’]
Journal: Medicine (Baltimore)
Citation: Zheng T, et al. Prediction of renal cell carcinoma: Development and validation of machine learning model. Prediction of renal cell carcinoma: Development and validation of machine learning model. 2026; 105:e47205. doi: 10.1097/MD.0000000000047205