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.
Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of Variant Allele Frequencies (VAF) under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred in any sampling size. However, the accuracy compared to the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slower such as in surface growth.
BACKGROUND Childhood leukemia is one of the most prevalent forms of pediatric cancer and occurs in two forms; Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). Prompt treatment of these ailments has been shown to significantly improve the survival rate of children afflicted with acute leukemia. OBJECTIVE In an effort to develop an early and comprehensive predictor of hematologic malignancy in children. METHODS we studied nutritional markers, key indicators of leukemia, and granulocytes in the patient's blood. Applying a machine learning algorithm and ten indices, our team analyzed 826 pediatric patients with ALL and 255 children with AML, comparing them with a control group of 200 healthy children. RESULTS The study uncovered noteworthy distinctions between boys and girls, as well as the relationship between blood biochemical markers. Through the use of a random forest model, we achieved an Area Under the Curve (AUC) of 0.950 for the prediction of leukemia subtypes, and an AUC of 0.909 for the prediction of AML. CONCLUSIONS This research provides an efficient and auxiliary diagnostic tool for the early screening of childhood blood cancers, and demonstrates the potential of artificial intelligence in modern healthcare.
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