Accelerated Dose Prediction for Radiotherapy

The quality of radiation treatment planning fundamentally depends on accurate delineation of anatomical structures. Each contour variation can potentially alter the planned radiation dose distribution, affecting both tumor coverage and normal tissue exposure. Traditional quality assurance approaches require time-consuming full dose calculations for each contour variation, creating a bottleneck in clinical workflows. Our research explores how machine learning models can predict dosimetric outcomes directly from segmentation data, enabling rapid quality assessment and supporting more efficient clinical decision-making.

This work sits at the intersection of medical imaging, radiation physics, and artificial intelligence. By developing models that can anticipate how contour changes will affect dose distributions, we aim to provide clinicians with immediate feedback about segmentation quality—feedback that is both dosimetrically informed and computationally efficient enough for routine clinical use.

The Challenge of Efficient Dosimetric Assessment

Traditional radiation therapy planning involves a computationally intensive process where treatment planning systems calculate dose distributions based on patient anatomy, beam configurations, and tissue properties. When evaluating segmentation quality or comparing alternative contours, each variation typically requires a separate dose calculation. This process, while necessary for final treatment plan approval, becomes prohibitively time-consuming when assessing multiple contour versions or validating automated segmentation algorithms.

The computational burden creates practical limitations for quality assurance workflows. Clinicians must make decisions about which contours to evaluate in detail, potentially missing important quality issues. Automated segmentation systems require extensive validation studies with thousands of dose calculations, limiting the pace of algorithm development and clinical translation. Research studies investigating contour variability and its clinical impact face similar computational challenges that constrain the scope and scale of investigation.

Machine learning offers a potential solution by learning patterns that connect anatomical information to dosimetric outcomes. Rather than performing full physics-based simulations for each quality assessment, a trained model can predict dose distributions in a fraction of the time. However, for such predictions to be clinically useful, the models must accurately capture not just typical dose patterns but also how dose distributions change in response to contour variations—the precise information needed for quality assessment.

Developing Sensitive Dose Prediction Models

Our research has focused on developing and validating deep learning models specifically designed to be sensitive to contour variations. Using 3D U-Net architectures, we created a cascaded prediction framework that first estimates the dose distribution and then refines the prediction based on detailed anatomical information (Poel et al., 2023). This approach allows the model to capture both the global dose patterns and the local variations that result from segmentation differences.

A critical aspect of this work involved demonstrating that the models truly respond to clinically meaningful contour changes rather than simply reproducing average dose patterns. Through systematic sensitivity analysis (Kamath et al., 2023), we showed that the predictions accurately reflect how dose distributions change with contour variations across different anatomical structures. The models maintain strong correlation with ground truth dose-volume histograms while achieving prediction times measured in seconds rather than minutes or hours.

This sensitivity to contour variations enables several important applications. The models can rank different segmentation versions according to their dosimetric impact, helping prioritize which cases need detailed expert review. They can identify specific anatomical regions where contour errors would have the greatest clinical consequences. And they can support the development of contour editing tools that provide real-time dosimetric feedback as clinicians refine segmentations.

Understanding Local Dosimetric Impact

Beyond global dose prediction, we developed methods to visualize and quantify the local dosimetric impact of contour variations. These visualization tools create detailed heatmaps that highlight which regions of a contour most strongly influence dose distributions. This spatial analysis helps clinicians understand not just whether a contour will affect the treatment plan, but specifically where the most critical regions are located.

These visualization tools serve multiple purposes in clinical workflows. They can guide quality assurance by focusing attention on the most dosimetrically sensitive anatomical regions. They support clinician training by illustrating the relationship between geometric errors and clinical consequences. And they provide interpretability for automated quality assessment systems, helping build clinical trust in algorithmic predictions by showing which features drive the assessments.

The approach also revealed important insights about the spatial relationship between anatomy and dosimetry. Certain anatomical regions consistently show high dosimetric sensitivity across patients, suggesting opportunities for targeted quality assurance protocols. The patterns learned by the models align with clinical understanding about critical regions, providing validation that the automated assessments capture relevant clinical knowledge.

Clinical Integration and Validation

Translating these predictive models into clinical practice requires careful validation across diverse patient populations and treatment sites. Our multi-institutional studies have demonstrated that models can generalize across different centers when trained on appropriately diverse data. The prediction accuracy remains stable for the range of contour variations typically encountered in clinical practice, suggesting robust performance in real-world applications.

Integration with existing clinical systems presents both technical and workflow challenges. The models need to interface with treatment planning databases, access the relevant imaging and contour data, and present results in formats that align with clinical decision-making processes. We have worked on developing APIs and user interfaces that balance computational efficiency with the detailed information clinicians need for quality assurance decisions.

User studies have emphasized the importance of transparency and interpretability. Clinicians appropriately want to understand why a system flags a particular contour as potentially problematic. By combining prediction models with visualization tools that show the predicted dose distribution and highlight critical regions, we can provide the context clinicians need to make informed decisions about whether to accept, edit, or further investigate specific segmentations.

Future Directions in Predictive Planning

Several promising directions emerge from this foundation. Physics-informed neural networks that incorporate radiation transport principles directly into the model architecture may improve accuracy and generalization while reducing data requirements. Multi-modal integration could enhance predictions by incorporating additional patient-specific information such as previous treatment histories or functional imaging data. Temporal modeling might enable predictions that account for anatomical changes during treatment courses.

Real-time prediction capabilities could transform contouring workflows by providing immediate dosimetric feedback as clinicians draw or edit contours. This interactive approach might improve both efficiency and quality by helping clinicians understand the dosimetric implications of their contouring decisions in real time. Such tools could be particularly valuable for training, allowing learners to see immediate dosimetric consequences of different contouring choices.

The ultimate goal is to develop integrated planning assistance systems that combine segmentation, dose prediction, and quality assessment in cohesive workflows. These systems would support clinicians in making rapid, informed decisions about treatment planning while maintaining the dosimetric quality essential for effective radiation therapy. By continuing to advance the speed, accuracy, and interpretability of predictive models, we can help realize the potential of AI-assisted radiation therapy planning.