Surgical treatments for pelvic sarcomas have been historically challenging due to the complex anatomy of the pelvic region and large heterogeneity in tumor conditions across patients. Orthopedic oncologists and implant companies must therefore make many decisions in the design of the surgical treatments and the prostheses to address these challenges. However, most of these decisions were still made based on subjective judgments, which raised the question of whether a more objective decision-making approach can be used to further improve the patients’ walking function post-surgery.

Predictive simulations of human movement using personalized neuromusculoskeletal models is one approach to design interventions. Surgeons and biomedical engineers can use this approach to generate exhaustive set of treatment designs and objectively evaluate all of them before deciding on the best option. Before the great potential of clinical translation of this technology in treating pelvic sarcomas can be fully realized, it is important to make sure the predictive simulations can reproduce reality at first. This dissertation seeks to use the three technical chapters to methodically address the technical challenges associated with generating realistic model-based predictive simulations.

In the first technical chapter (Chapter 2), a personalized musculoskeletal model was created to simulate the gait movement of a pelvic sarcoma patient. The model met our need for being capable of independently simulating movement actuated by both their trunk muscles and lower extremity muscles, since most existing models were usually specialized for musculoskeletal system in only one region of the body e.g. trunk only or leg only. In addition to detailing model development, this technical chapter also addressed the practical problem where insufficient electromyography channels were available to record muscle activities of both trunk and leg muscles. The proposed computational approach would use muscle synergies extracted from the measured activity of leg muscles to provide realistic estimates of trunk muscle activities.

In the second technical chapter (Chapter 3), experimental gait data of a pelvic sarcoma patient pre- and post-surgery were collected, analyzed, and compared to provide an understanding on how neural controls of the lower extremity muscles changed after the surgery. Three hypotheses about how partial information of pre-surgery neural controls could be used in predict post-surgery muscle activities (1. Fixed SynVec, pre-surgery synergy vectors were retained, 2. Fixed SynCmd, pre-surgery synergy commands were retained, and 3. Shifted SynCmd, pre-surgery synergy commands were retained but allowed to shift in time) were evaluated. The Fixed SynCmd and Shifted SynCmd hypotheses accurately reconstructed post-surgery muscle activities.

In the third technical chapter (Chapter 4), optimizations based on four different assumptions about optimality principles for predicting post-surgery walking were formulated and evaluated. The first proposed minimization of changes in muscle synergies from pre-surgery models. The second proposed minimization of deviations in synergy vector weights from set values. The third proposed minimization of changes in muscle activations from pre-surgery activations, and the fourth proposed minimization of muscle activations during post-surgery walking. The optimization based on minimization of changes in muscle synergies most accurately predicted the muscle activations during post-surgery walking.

The research reported in this dissertation establishes the foundation for future works in model-based prediction of post-surgery gait of pelvic sarcoma patients. As more experimental and clinical data are collected, more analyses can be performed and more predictive simulations can be generated using the methods develop in this dissertation, to shed more lights on the appropriate methods to use for predicting post-surgery walking.