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Patients were categorized into two groups based on surgical outcomes: no residual disease (R0, n = 14) and suboptimal residual disease (R1, n = 6)
In this study, three different machine learning algorithms, random forest, support vector machines and bootstrap aggregation with classification and regression trees, were used to predict pre-NACT residual disease (R0, R1) on the basis of the pre-NACT values of 97 different protein levels of HGSOC patients with statistically significant differences before and after NACT
Current epidemiological data rank it as the seventh leading cause of cancer-related mortality worldwide, with projections indicating it may become the second leading cause in the United States by 2030 [2, 3]
Regarding Tylenoland Critical Thinking in Matters of Health
The table below gives the equivalent figures across every commonly used solvent volume
Tht ra, vic mt nc v mt cn bng in gii l 2 khi nim v hot ng khc nhau
