Information Systems and Technology

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https://hdl.handle.net/10315/27588

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Open Access
Characterizing Osteosarcopenia In Spinal Metastases Patients Undergoing Stereotactic Body Radiotherapy (Sbrt): Leveraging Deep Learning For Improved Outcome Prediction
(2025-07-23) Castano Sainz, Yessica Caridad; Chen, Stephen
Stereotactic body radiotherapy (SBRT) is commonly used to treat spinal metastases, offering excellent local control and pain relief. However, it carries an average 14% risk of vertebral compression fractures (VCFs), and despite growing evidence linking osteosarcopenia to adverse clinical outcomes, musculoskeletal health is not routinely assessed during SBRT treatment planning. This thesis introduces a fully automated pipeline for extracting musculoskeletal biomarkers from CT, combining deep learning–based segmentation with vertebral landmark–guided cropping and volumetric analysis. Sarcopenia thresholds were derived for volumetric indices using height-based and vertebral-based normalization, guided by established literature cutoffs for the Psoas Muscle Index (PMI). Osteoporosis was defined using trabecular bone density. In this SBRT cohort, 58% of patients met criteria for osteoporosis, 45% for sarcopenia, and 31% for osteosarcopenia. In multivariable logistic regression analyses, significant associations between fracture risk and both osteoporosis and lower psoas muscle density were observed in specific models, warranting further investigation. Additionally, categorical definitions of sarcopenia and osteosarcopenia were significantly associated with reduced overall survival. The pipeline was extended to MRI using CT-based segmentations as weak labels for training nnU-Net models, achieving high segmentation accuracy and supporting future radiation-free musculoskeletal biomarker assessment.
Open Access
Efficient Calculation of Optimal Configuration Processes
(2015-12-16) Fernandez, Yasser Gonzalez; Chen, Stephen; Liaskos, Sotirios
Customers are getting increasingly involved in the design of the products and services they choose by specifying their desired characteristics. As a result, configuration systems have become essential technologies to support the development of mass-customization business models. These technologies facilitate the configuration of complex products and services that otherwise could generate many incorrect configurations and overwhelm users with confusion. This thesis studies the problem of optimizing the user interaction in a configuration process – as in minimizing the number of questions asked to a user in order to obtain a fully-specified product or service configuration. The work carried out builds upon a previously existing framework to optimize the process of configuring a software system, and focuses on improving its efficiency and generalizing its application to a wider range of configuration domains. Two solution methods along with two alternative ways of specifying the configuration models are proposed and studied on different configuration scenarios. The experimental study evidences that the introduced solutions overcome the limitations of the existing framework, resulting in more suitable algorithms to work with models involving a large number of configuration variables.
Open Access
Experimental analysis on the operation of Particle Swarm Optimization
(2021-07-06) Yadollahpour, Naeemeh; Chen, Stephen
In Particle Swarm Optimization, it has been observed that swarms often stall as opposed to converge. A stall occurs when all of the forward progress that could occur is instead rejected as Failed Exploration. Since the swarms particles are in good regions of the search space with the potential to make more progress, the introduction of perturbations to the pbest positions can lead to significant improvements in the performance of standard Particle Swarm Optimization. The pbest perturbation has been supported by a line search technique that can identify unimodal, globally convex, and non-globally convex search spaces, as well as the approximate size of attraction basin. A deeper analysis of the stall condition reveals that it involves clusters of particles that are performing exploitation, and these clusters are separated by individual particles that are performing exploration. This stall pattern can be identified by a newly developed method that is efficient, accurate, real-time, and search space independent. A more targeted (heterogenous) modification for stall is presented for globally convex search spaces.
Open Access
Progressive Hierarchical Classification For Multi-Category Image Classification
(2024-10-28) Kuo, Te-Chuan; Chen, Stephen
This thesis evaluates a hierarchical classification model applied to the CIFAR-10 dataset, focusing on addressing the limitations of existing methods, which often struggle with (i) overlapping features and (ii) poor interpretability of classification decisions. The hierarchical model was implemented to mitigate these issues by refining classification through a multi-stage process that narrows the focus progressively. Our hierarchical approach has demonstrated its ability to focus on distinguishing features critical to specific classes and groups/pairs. Furthermore, the hierarchical models provide enhanced transparency over the baseline model by allowing a granular examination of classification performance across multiple stages.
Open Access
Simulation Optimization Of Operating Room Schedules For Elective Orthopaedic Surgeries
(2025-04-10) Maltseva, Daria Victorovna; Chen, Stephen
The aim of this thesis was to solve the problem of scheduling elective surgeries in a multiple operating room setting with the goal of minimizing the amount of overtime incurred. While surgical durations cannot always be perfectly estimated and vary by procedure and surgeon, we propose an approach that relies on leveraging the stochastic nature of surgical durations to simulate each operating day and understand the probability of incurring overtime under a certain schedule of surgeries. Through experimentation with three optimization techniques that strategically re-schedule surgeries, two showed promising results being able to reduce the total number of overtime surgeries by 12-15%, equivalent to approximately 1h of total monthly overtime. This approach serves as a tool for improving schedules and supporting decision makers at any hospital dealing with elective surgeries. Our contribution involves introducing the simulation optimization model and describing the data-driven approach to analyzing the scheduling problem.

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