Quality Management for Radiation Oncology In-House Software Products.

Abstract

(1) Objective: To develop and implement an in-house software quality management (ISQM) program to ensure the continuous, consistent quality of in-house software products. (2) Methods: The ISQM program consists of two key components: quality assurance (QA) and comprehensive documentation. The QA component involves code review, acceptance testing, commissioning, and routine quality checks. Documentation requirements encompass a product report, user manual, QA procedures and reports, release notes, version control, and logs. For each software product, the QA process must be completed, and all required documentation must be finalized before clinical deployment. (3) Results: The ISQM program was successfully developed and retrospectively implemented for existing software products. Future software products will adhere to the ISQM framework, ensuring compliance before clinical release. (4) Conclusions: This study demonstrates the successful development and implementation of the ISQM program-a standardized framework for managing the quality of in-house software products. The ISQM program ensures continuous and consistent oversight, playing a critical role in guaranteeing safe and high-quality patient care in the field of radiation oncology.

Department

Description

Provenance

Subjects

in-house software, quality management, radiotherapy

Citation

Published Version (Please cite this version)

10.3390/bioengineering12040352

Publication Info

Yoo, Sua, Phillip Antoine, Chunhao Wang, Yang Sheng, Q Jackie Wu, Joseph Kowalski, Qiuwen Wu, Fang-Fang Yin, et al. (2025). Quality Management for Radiation Oncology In-House Software Products. Bioengineering (Basel, Switzerland), 12(4). p. 352. 10.3390/bioengineering12040352 Retrieved from https://hdl.handle.net/10161/33601.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Yoo

Sua Yoo

Associate Professor of Radiation Oncology

Patient positioning verification for radiation therapy using OBI/CBCT; Treatment planning for breast cancer radiotherapy;

Wang

Chunhao Wang

Associate Professor of Radiation Oncology
  • Deep learning methods for image-based radiotherapy outcome prediction and assessment
  • Machine learning in outcome modelling
  • Automation in radiotherapy planning and delivery



Wu

Qingrong Jackie Wu

Professor of Radiation Oncology

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.