Radiotherapy treatment planning is a complex and time-intensive process, often impacted by inter-planner variability and subjective decision-making. To address these challenges, we introduce Dose Optimization Language Agent (DOLA), an autonomous large language model (LLM)-based agent designed for optimizing radiotherapy treatment plans while rigorously protecting patient privacy. DOLA integrates the LLaMa3.1 LLM directly with a commercial treatment planning system, utilizing chain-of-thought prompting, retrieval-augmented generation (RAG), and reinforcement learning (RL). Operating entirely within secure local infrastructure, this agent eliminates external data sharing. We evaluated DOLA using a retrospective cohort of 18 prostate cancer patients prescribed 60 Gy in 20 fractions, comparing model sizes (8 billion vs. 70 billion parameters) and optimization strategies (No-RAG, RAG, and RAG+RL) over 10 planning iterations. The 70B model demonstrated significantly improved performance, achieving approximately 16.4% higher final scores than the 8B model. The RAG approach outperformed the No-RAG baseline by 19.8%, and incorporating RL accelerated convergence, highlighting the synergy of retrieval-based memory and reinforcement learning. Optimal temperature hyperparameter analysis identified 0.4 as providing the best balance between exploration and exploitation. This proof of concept study represents the first successful deployment of locally hosted LLM agents for autonomous optimization of treatment plans within a commercial radiotherapy planning system. By extending human-machine interaction through interpretable natural language reasoning, DOLA offers a scalable and privacy-conscious framework, with significant potential for clinical implementation and workflow improvement.

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or post-operative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk post-operative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For pre-operative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for post-operative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using the lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes.

Google and Mayo Clinic have joined hands to develop an AI-based system that can support physicians, help reduce treatment planning time and improve the efficiency of radiotherapy. The partnership will help develop an algorithm to assist oncologist in contouring, a labour-intensive step involving segmenting areas of cancer and nearby healthy tissues, and conduct research to better understand how AI could be deployed effectively in clinical practice.

In a bid to fight cancer, Google has collaborated with Mayo Clinic on research to develop an Artificial Intelligence (AI)-based system that can support physicians, help reduce treatment planning time and improve the efficiency of radiotherapy. In this research partnership, Mayo Clinic and Google Health will work to develop an algorithm to assist clinicians in contouring healthy tissue and organs from tumours, and conduct research to better understand how this technology could be deployed effectively in clinical practice. Mayo Clinic is an international centre of excellence for cancer treatment with world-renowned radiation oncologists. "Google researchers have studied how AI can potentially be used to augment other areas of healthcare -- from mammographies to the early deployment of an AI system that detects diabetic retinopathy using eye scans," said Dr Cian Hughes, Informatics Lead, Google Health. More than 18 million new cancer cases are diagnosed globally each year, and radiotherapy is one of the most common cancer treatments.

More than 18 million new cancer cases are diagnosed globally each year, and radiotherapy is one of the most common cancer treatments--used to treat over half of cancers in the United States. But planning for a course of radiotherapy treatment is often a time-consuming and manual process for clinicians. The most labor-intensive step in planning is a technique called "contouring" which involves segmenting both the areas of cancer and nearby healthy tissues that are susceptible to radiation damage during treatment. Clinicians have to painstakingly draw lines around sensitive organs on scans--a time-intensive process that can take up to seven hours for a single patient. Technology has the potential to augment the work of doctors and other care providers, like the specialists who plan radiotherapy treatment.

We're excited to announce a new research partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust, which provides world-leading cancer treatment. Head and neck cancer in general affects over 11,000 patients in the UK alone each year. Advances in treatment such as radiotherapy have improved survival rates, but because of the high number of delicate structures concentrated in this area of the body, clinicians have to plan treatment extremely carefully to ensure none of the vital nerves or organs are damaged. That makes a cancer at the back of the mouth or in the sinuses, for example, particularly hard to treat with radiotherapy. So with clinicians in UCLH's world-leading radiotherapy team we are exploring whether machine learning methods could reduce the amount of time it takes to plan radiotherapy treatment for such cancers.

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