Recurrent tibial intra-cortical osteosarcoma: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>Intra-cortical osteosarcoma is the rarest subtype of osseous-producing tumor. Most reported cases present a low-grade histology with slow progression and good oncological control after adequate treatment. In this report, we describe a case and review the literature to propose adequate treatment.</p> <p>Case presentation</p> <p>We present the case of a 21-year-old Thai woman who was thought to have an intra-cortical osteosarcoma of the right tibia. We performed a wide resection and reconstruction with bone transportation using an Ilizarov external fixator. The tumor recurred five years later at the same site with a similar histology. We performed a new resection and reconstruction by ankle arthrodesis with adjuvant chemotherapy. At the last follow-up, she had remained active and free from disease for seven years.</p> <p>Conclusion</p> <p>This case report of recurrent intra-cortical osteosarcoma describes an atypical presentation. The low-grade histology, adequate surgical margin and adjuvant chemotherapy of the recurrent lesion were favorable factors, and our patient has remained free of any tumor recurrence.</p

A New Accelerated Algorithm for Convex Bilevel Optimization Problems and Applications in Data Classification

In the development of algorithms for convex optimization problems, symmetry plays a very important role in the approximation of solutions in various real-world problems. In this paper, based on a fixed point algorithm with the inertial technique, we proposed and study a new accelerated algorithm for solving a convex bilevel optimization problem for which the inner level is the sum of smooth and nonsmooth convex functions and the outer level is a minimization of a smooth and strongly convex function over the set of solutions of the inner level. Then, we prove its strong convergence theorem under some conditions. As an application, we apply our proposed algorithm as a machine learning algorithm for solving some data classification problems. We also present some numerical experiments showing that our proposed algorithm has a better performance than the five other algorithms in the literature, namely BiG-SAM, iBiG-SAM, aiBiG-SAM, miBiG-SAM and amiBiG-SAM