Real-time analysis and display of quantitative measures to track and improve clinical workflow

Technical Note: Improving the workflow in a carbon ion therapy center with custom software for enhanced patient care

Carbon-ion radiation therapy (CIRT) is an up-and-coming modality for cancer treatment. Implementation of CIRT requires collaboration among specialists like radiation oncologists, medical physicists, and other healthcare professionals. Effective communication among team members is necessary for the success of CIRT. However, the current workflows involving data management, treatment planning, scheduling, and quality assurance (QA) can be susceptible to errors, leading to delays and decreased efficiency. With the aim of addressing these challenges, a team of medical physicists developed an in-house workflow management software using FileMaker Pro. This tool has streamlined the workflow and improved the efficiency and quality of patient care.

Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

Purpose

The study's purpose was to compare the performance of artificial intelligence (AI) in auto-contouring compared with a human practitioner in terms of precision, differences in dose distribution, and time consumption.

Methods and Materials

Datasets of previously irradiated patients in 3 different segments (head and neck, breast, and prostate cancer) were retrospectively collected. An experienced radiation oncologist (MD) performed organs-at-risk (OARs) and standard clinical target volume delineations as baseline structures for comparison. AI-based autocontours were generated in 2 additional CT copies; therefore, 3 groups were assessed: MD alone, AI alone, and AI plus MD corrections (AI+C). Differences in Dice similarity coefficient (DSC) and person-hour burden were assessed. Furthermore, changes in clinically relevant dose-volume parameters were evaluated and compared.

Results

Seventy-five previously treated cases were collected (25 per segment) for the analysis. Compared with MD contours, the mean DSC scores were higher than 0.7 for 74% and 80% of AI and AI+C, respectively. After corrections, 17.1% structures presented DSC score deviations higher than 0.1 and 10.4% dose-volume parameters significantly changed in AI-contoured structures. The time consumption assessment yielded mean person-hour reductions of 68%, 51%, and 71% for breast, prostate, and head and neck cancer, respectively.

Conclusions

In great extent, AI yielded clinically acceptable OARs and certain clinical target volumes in the explored anatomic segments. Sparse correction and assessment requirements place AI+C as a standard workflow. Minimal clinically relevant differences in OAR exposure were identified. A substantial amount of person-hours could be repurposed with this technology.