The Challenge
The Challenge in Radiation Oncology
Radiation therapy is a cornerstone of cancer treatment, used in over half of all cancer cases. However, the treatment planning process faces significant challenges that impact both clinical efficiency and patient outcomes.
Time-Consuming Planning
Treatment planning for complex cases like IMRT/VMAT requires multiple iterations over several days[1], with each adjustment requiring expert clinical judgment. Studies show that treatment delays are associated with increased mortality risk, with every four-week delay linked to a 6-13% increased risk of death[2].
Inter-Observer Variability
Contouring variability among clinicians leads to inconsistent plans. Research indicates that significant variation exists in organ contouring[4], with delineation differences potentially impacting dose distributions and clinical outcomes[5].
Manual Quality Assurance
QA relies on manual review, adding significant time to the planning process[6]. Traditional manual QA approaches have limitations in error detection[7], highlighting the need for improved quality assurance methods.
Limited AI Reliability
Current AI tools can struggle with diverse protocols, with cross-institutional deployment often requiring substantial manual refinement per case[8] due to domain adaptation challenges and protocol variations.
The Bottom Line: These workflow challenges contribute to increased treatment costs and resource utilization[9] and may compromise treatment outcomes, potentially affecting both survival rates and quality of life.
Research Foundation
Our Research Journey
ContourAId is built on years of peer-reviewed research in medical AI, radiotherapy planning, and quality assurance. Our work has been published at leading conferences and journals, addressing key challenges in automated segmentation, dose prediction, and quality control.
Key Publications:
→ MICCAI 2023: Research on segmentation architecture optimization → IEEE EMBC 2023: ASTRA framework for quality assurance (Best Paper Award) → MIDL 2024: Comparing AI dose predictors with expert clinicians → Multiple workshop papers on dose-guided segmentation and automated QAThis research directly informs ContourAId's approach to solving real-world clinical challenges, ensuring our solutions are grounded in rigorous scientific validation.