Abstract

The treatment of articular cartilage injury and disease has become an increasingly relevant part of orthopaedic care. Articular cartilage transplantation, in the form of osteochondral allografting, is one of the most established techniques for restoration of articular cartilage. Our research efforts over the last two decades have supported the transformation of this procedure from experimental “niche” status to a cornerstone of orthopaedic practice. In this paper, we describe our translational and clinical science contributions to this transformation: (1) to enhance the ability of tissue banks to process and deliver viable tissue to surgeons and patients, (2) to improve the biological understanding of in vivo cartilage and bone remodeling following osteochondral allograft (OCA) transplantation in an animal model system, (3) to define effective surgical techniques and pitfalls, and (4) to identify and clarify clinical indications and outcomes. The combination of coordinated basic and clinical studies is part of our continuing comprehensive academic OCA transplant program. Taken together, the results have led to the current standards for OCA processing and storage prior to implantation and also novel observations and mechanisms of the biological and clinical behavior of OCA transplants in vivo. Thus, OCA transplantation is now a successful and increasingly available treatment for patients with disabling osteoarticular cartilage pathology. The purpose of this study is to evaluate dose prediction errors (DPEs) and optimization convergence errors (OCEs) resulting from use of a superposition∕convolution dose calculation algorithm in deliverable osteochondral allograft (OCA) transplantation therapy optimization for OCA patients. The IMRT optimization was performed in three sequential steps: (1) fast optimization in which an initial nondeliverable IMRT solution was achieved and then converted to multileaf collimator (MLC) leaf sequences; (2) mixed deliverable optimization that used a Monte Carlo (MC) algorithm to account for the incident photon fluence modulation by the MLC, whereas a superposition∕convolution (SC) dose calculation algorithm was utilized for the patient dose calculations; and (3) MC deliverable-based optimization in which both fluence and patient dose calculations were performed with a MC algorithm. DPEs of the mixed method were quantified by evaluating the differences between the mixed optimization SC dose result and a MC dose recalculation of the mixed optimization solution. OCEs of the mixed method were quantified by evaluating the differences between the MC recalculation of the mixed optimization solution and the final MC optimization solution. The results were analyzed through dose volume indices derived from the cumulative dose-volume histograms for selected anatomic osteochondral allograft (OCA) transplantation structures.