Sara Cammarota

RESEARCH OUTPUT

Selected publications

1. Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement.Medical physics · 2026
   BACKGROUND: Radiotherapy treatment planning (TP) aims to maximize radiation dose delivered to tumors while minimizing exposure to surrounding healthy tissues. Beam angle optimization (BAO) is a crucial component of TP, characterized by high dimensionality and non-convexity, and is traditionally solved via heuristic or manual iterative approaches. These conventional methods are time-consuming and often yield suboptimal solutions due to incomplete exploration of the vast solution space. PURPOSE: This study introduces a novel framework integrating a general-purpose large language model (LLM) within a reinforcementlearning (RL)-inspired iterative strategy to automate BAO in radiotherapy planning. Taking advantage of the inherent knowledge embedded in LLMs, the method uses visual and scalar feedback to produce clinically meaningful treatment plans without requiring any domain-specific fine-tuning or additional training. METHODS: The proposed framework employs an off-the-shelf Generative Pre-trained Transformer, GPT-4 model (denoted GPT-4o) in an inference-only setting. At each iteration, GPT-4o suggests a set of gantry angles, which are subsequently input into the MatRad software to generate a dose distribution. A scalar reward is computed from this distribution using a custom reward function designed to balance target dose conformity and sparing of organs-at-risk (OARs). This reward, along with the corresponding dose maps, serves as feedback for the LLM to iteratively refine its suggestions. The refinement process consists of distinct exploration and exploitation phases inspired by classical RL paradigms. We evaluated six configurations that varied in exploration duration and in the Computed Tomography (CT) slice inputs provided to the LLM (Single-View vs. Multi-View). Performance was benchmarked against a random-angle selection baseline across three anatomical sites: prostate, head-and-neck, and liver. RESULTS: Across the liver and head-and-neck cases, all LLM-based configurations significantly outperformed the random baseline ( p < 0.05 ). In the prostate scenario, most strategies demonstrated statistically significant improvements, except for the Multi-View configurations with extended exploration phases (10 and 15 iterations). Rewards consistently increased during the exploitation phase, and the resulting dose-volume histograms and dose distributions exhibited improved conformity to target volumes with enhanced sparing of OARs. Notably, plans of clinically plausible quality were obtained within 20 iterative refinement steps in this proof-of-concept setting. CONCLUSIONS: This study demonstrates that general-purpose LLMs, operating without specialized model training or fine-tuning, can effectively serve as intelligent agents for automated radiotherapy TP, specifically addressing the BAO problem. This flexible and scalable framework has the potential to enhance clinical decision-making workflows in radiotherapy. Future research directions include exploring more comprehensive and clinically nuanced reward functions and extending the methodology to other components of radiotherapy TP.

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   PublisherPubMedScopus
2. Towards Intelligent Agents for Radiotherapy: Integrating Exploration-Exploitation with Foundation Models.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
   This study proposes an automated approach to radiotherapy treatment planning by integrating a reinforcement-learning-style iterative framework with a multimodal Large Language Model (LLM). We specifically investigate the problem of Beam Angle Optimization, a high-dimensional and non-convex subproblem of Treatment Planning. Our system employs GPT-4V to select candidate beam angles and analyze three-dimensional dose distributions generated by Monte Carlo simulations within the MatRAD environment. Iterative plan refinement is guided by a reward function that encourages target dose conformity and penalizes excessive dose to organs at risk. We incorporate exploration-exploitation principles to strike a balance between investigating diverse action proposals and refining promising solutions. Experimental results on prostate cancer cases demonstrate that our LLM-based framework offers superior performance compared to random beam selection and can outperform the quality of deep reinforcement learning baselines, indicating the potential for LLMs to assist in complex radiotherapy treatment planning tasks.Clinical relevance-This approach is designed to alleviate the significant effort of manual treatment planning by assisting medical physicists in exploring beam configurations and systematically refining plans to improve dose coverage and protect healthy tissues.

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   PublisherPubMed