# A prescription‐free, radiobiology‐based framework for automated VMAT planning: A feasibility study in primary prostate cancer radiotherapy

## TL;DR

This study introduces an automated prostate cancer radiotherapy planning system that uses radiobiology to optimize treatment plans without fixed dose prescriptions.

## Contribution

A prescription-free, radiobiology-based automated VMAT planning framework that generates patient-specific Pareto-optimal plans in radiobiological objective space.

## Key findings

- The framework successfully generated individualized pseudo-Pareto fronts for 17 patients in radiobiological space.
- Plans with highest P+ achieved better tumor control and reduced toxicity compared to clinical plans.
- The method fulfilled clinical dose requirements and showed robustness to TCP model parameter variations.

## Abstract

Current VMAT planning workflows for prostate cancer primarily depend on conventional dose–volume criteria specified at discrete dose or volume points. These point‐based objectives, however, do not necessarily lead to globally optimal, patient‐specific treatment plans. While radiobiological models such as Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) can provide more meaningful, individualized targets, previous implementations have either employed these for plan evaluation or integrated biological objectives without providing a comprehensive set of deliverable trade‐off plans. To date, no prescription‐free, automated VMAT planning method has been introduced that generates clinically deliverable, patient‐specific Pareto fronts that are biologically interpretable and useful for radiobiological trade‐off analysis.

The purpose of this study was to develop and clinically evaluate a fully automated, prescription‐free VMAT planning framework for primary prostate cancer that generates Pareto‐optimal, clinically deliverable treatment plans in radiobiological objective space, constrained by predefined TCP and NTCP levels.

The proposed framework was implemented within a commercial treatment planning system (TPS). 17 patients with unfavorable intermediate‐risk prostate cancer were retrospectively selected for evaluation. For each patient, TCP and NTCP levels were predefined for three target volumes and seven organs at risk (OARs), restricting the optimization to clinically meaningful regions of the solution space. Plan optimization was performed using Particle Swarm Optimization (PSO) to iteratively adjust VMAT parameters, with the complication‐free tumor control probability (P+
) serving as the sole objective function. All resulting, clinically deliverable plans were generated in the TPS and subsequently analyzed in the bi‐objective radiobiological space defined by injury probability (PI
) versus one minus the benefit probability (1 − PB
). The plan yielding the highest P+
and the corresponding individualized pseudo‐Pareto front were identified for each patient. The proposed method was benchmarked against clinical moderately hypofractionated simultaneous integrated boost (SIB) plans.

The proposed prescription‐independent planning approach successfully generated individualized pseudo‐Pareto fronts for all 17 patients in the radiobiological space of PI
versus (1 − PB
). This enabled clinicians to visualize and interpret trade‐offs between tumor control and normal tissue complication risk within the predefined TCP and NTCP levels. For each patient, the plan with highest P+
achieved superior predicted tumor control and reduced normal tissue toxicity compared to manually optimized clinical plans. The method effectively individualized dose distributions according to patient‐specific anatomy and tumor biology, without reliance on fixed dose prescriptions or conventional constraints. All highest P+
treatment plans fulfilled the clinical dose requirements. Sensitivity analyses demonstrated robustness of the framework with respect to variations in TCP model parameters.

This study demonstrated the feasibility of a fully automated, prescription‐free VMAT planning framework for primary prostate cancer, indicating its potential for future clinical implementation. The proposed framework directly optimized treatment plans in radiobiological objective space, producing Pareto‐optimal, clinically deliverable solutions using predefined TCP and NTCP levels. It enables patient‐specific trade‐off analysis taking into account tumor control and normal tissue complication risk. The work provides a foundation for further development, including the incorporation of geometric uncertainties, acceleration through parallel or GPU‐based computation, and application to additional tumor sites.