Author: Vishruta A. Dumane, Andrew Lukban, Kiran Pant, Charlotte Elizabeth Read, Ren-Dih Sheu, Nadia M. Vassell 👨🔬
Affiliation: Icahn School of Medicine at Mount Sinai, Mount Sinai Beth Israel, Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology 🌍
Purpose: This work introduces a refined in-house modified COMS eye plaque management system to streamline processes, reduce redundancies, and enhance usability.
Methods: A web-based application with a centralized database replaced the existing Excel-based workflow. The new system integrates with department EMR (MOSAIQ) and hotlab inventory system (eHotlab) via ODBC, significantly reducing redundant data entry and manual errors. HTML/CSS and WebGL technologies were employed to provide real-time 3D visualization and improved interactivity, enabling users to more effectively associate the relative locations of tumors and plaques within the eyeball. The TG-43 formalism was implemented to support dosimetry for I-125 and Pd-103 seeds, with isotropic point and line models used for dose calculation and independent verification, respectively.
Results: The new system supports all key workflow steps, including pre-planning for isotope selection, source ordering, inventory management, assay, final dose calculation, independent checks, and generating plan documents. By centralizing data and application, the system enhanced integrity and eliminated the inconsistencies present in the previous Excel-based approach. Users gained the flexibility to select from additional source models, expanding vendor options. Enhanced visualization features bridged Fundus diagrams and 3D rendering for precise tumor and plaque positioning. The automated document generation feature reduced the preparation time for clinical plans from over an hour to just minutes, freeing up valuable clinical resources.
Conclusion: The refined COMS eye plaque management system eliminated redundant data entries, streamlined workflows, and improved data integrity. Enhanced visualization and automation reduced user errors and increased efficiency. The intuitive interface and robust features provide a scalable solution for clinical implementation, lowering the learning curve for new users.