Abstract
Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.
Highlights
Renal cell carcinoma (RCC) is the primary malignant tumor in renal tumors, occupying the sixth place globally with regard to tumor death; it is the second leading cause of death among urinary system tumors only after bladder cancers [1]
These results indicate that the model formed by the complex combination of 18 routine blood and urine indices exhibited good performance in identifying ccRCC; the model could help
The importance of an appropriate number of top-ranking indices should be highlighted in the context of an urgent requirement for high-efficiency solutions and easy-to-perform models
Summary
Renal cell carcinoma (RCC) is the primary malignant tumor in renal tumors, occupying the sixth place globally with regard to tumor death; it is the second leading cause of death among urinary system tumors only after bladder cancers [1]. There are several subtypes of RCC, for which the growth rate, mode, and metastasis rate vary greatly. Among these subtypes, clear cell renal cell. The surgical methods and prognosis for ccRCC exhibit great differences compared with other subtypes. An accurate preoperative identification of ccRCC will contribute significantly to the success rate of surgery and survival rate of patients. CT or MR enhancement, which are currently the most dominant imaging diagnostic methods for renal cancers, have frequently failed to differentiate between different subtypes of RCC; this affects the treatment scheme, surgical approach, and prognosis of patients. Exploring new methods for and quickly distinguishing ccRCC from other subtypes is still a serious challenge for doctors
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