A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis

Abstract

Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies.

Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices

“…The burgeoning field of machine learning (ML) has ushered in revolutionary capabilities in clinical medicine. Their vast potential in diagnostic and prognostic contexts is gaining traction, particularly in cancer diseases, 50 , 51 and the challenging domain of COVID-19. 52 However, the actual deployment of AI and ML in clinical decision-making is not without its challenges.…”

“…As observed in infectious disease research, many ML models are constructed on data from high-income countries, possibly skewing their efficacy in diverse socioeconomic settings. 50 Furthermore, while algorithmic performance metrics such as sensitivity and specificity are impressive, the real-world applicability and interpretability of these systems remain paramount concerns. Diving into our research, after comparing the acute myocarditis and COVID-19 groups, it is evident that the ROC of MB and LDH ( Figure 6 A) differ substantially between the two groups.…”