The aim of this study was to investigate the clinical benefit of presurgical therapy with pazopanib in renal cell carcinoma (RCC) patients with a tumor thrombus extending to a high level in the vena cava. A retrospective review was performed for seven consecutive patients with RCC and tumor thrombus involving the vena cava above the hepatic vein (level 3-4, Mayo Clinic classification) treated with pazopanib without initial cytoreductive nephrectomy at our institution. The effect of pazopanib was assessed in terms of the primary site response, thrombus diameter, and height (before and after treatment) on computed tomography or MRI. The tumor thrombus level before the induction of pazopanib was 3 in one patient and 4 in the remaining six patients. After pazopanib, shrinkage of the primary site and thrombus diameter and length were observed in all patients except one (with a rhabdoid tumor). The mean decreases of primary tumor diameter, tumor thrombus diameter, and length were 14, 9, and 31 mm, respectively. The tumor thrombus level decreased in three (43%) patients and remained stable in the remaining patient. Our findings suggest that presurgical treatment with pazopanib may shrink the tumor thrombus and decrease the surgical invasiveness in RCC patients with a high-level tumor thrombus.

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.