Xinye Ni

MDA-TransUNet: A Deep Learning-Based Automatic Segmentation Method for Cervical Cancer Brachytherapy

Introduction Accurate delineation of the high-risk clinical target volume (HR-CTV) and organs at risk (OARs) is critical for cervical cancer brachytherapy. However, treatment planning is time-consuming, and prolonged waiting can lead to organ displacement and patient discomfort. Additionally, the steep dose gradients around HR-CTV amplify segmentation errors in HR-CTV and OARs. Therefore, achieving rapid and precise delineation of HR-CTV and OARs remains challenging. This study proposes a novel network model, MDA-TransUNet, for fast segmentation of HR-CTV and OARs in cervical cancer. Methods We applied MDA-TransUnet, a CNN-Transformer hybrid model, to segment the bladder, colon, rectum, small bowel, and HR-CTV on cervical cancer CT images. 122 cervical cancer brachytherapy patients’ CT images from three clinical centers were utilized for training and testing, with 80 cases allocated to training, 22 to testing, and 20 to external validation. Segmentation accuracy was quantified using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95), and Average Surface Distance (ASD). Dosimetric differences were analyzed via paired t-tests. Results Compared to other methods, MDA-TransUnet achieved superior segmentation performance on the test dataset. The DSCs for the bladder, colon, rectum, small bowel, and HR-CTV were 94.54%, 79.27%, 79.27%, 88.90%, and 82.35%, respectively. Paired t-tests on five dosimetric metrics (D5cc, D2cc, D0.1cc, D90%, and Dmean) showed no significant differences. For OARs, the average difference in D2cc was less than 12%. For HR-CTV, the average difference in Dmean was less than 8%, and D90% was less than 11%. Conclusion This work demonstrates the superiority of MDA-TransUnet in segmenting OARs and HR-CTV for cervical cancer brachytherapy, with robust performance across multi-center datasets.

A feasibility study of automating radiotherapy planning with large language model agents

Abstract Objective. Radiotherapy planning requires significant expertise to balance tumor control and organ-at-risk (OAR) sparing. Automated planning can improve both efficiency and quality. This study introduces GPT-Plan, a novel multi-agent system powered by the GPT-4 family of large language models (LLMs), for automating the iterative radiotherapy plan optimization. Approach. GPT-Plan uses LLM-driven agents, mimicking the collaborative clinical workflow of a dosimetrist and physicist, to iteratively generate and evaluate text-based radiotherapy plans based on predefined criteria. Supporting tools assist the agents by leveraging historical plans, mitigating LLM hallucinations, and balancing exploration and exploitation. Performance was evaluated on 12 lung (IMRT) and 5 cervical (VMAT) cancer cases, benchmarked against the ECHO auto-planning method and manual plans. The impact of historical plan retrieval on efficiency was also assessed. Results. For IMRT lung cancer cases, GPT-Plan generated high-quality plans, demonstrating superior target coverage and homogeneity compared to ECHO while maintaining comparable or better OAR sparing. For VMAT cervical cancer cases, plan quality was comparable to a senior physicist and consistently superior to a junior physicist, particularly for OAR sparing. Retrieving historical plans significantly reduced the number of required optimization iterations for lung cases (p < 0.01) and yielded iteration counts comparable to those of the senior physicist for cervical cases (p = 0.313). Occasional LLM hallucinations have been mitigated by self-reflection mechanisms. One limitation was the inaccuracy of vision-based LLMs in interpreting dose images. Significance. This pioneering study demonstrates the feasibility of automating radiotherapy planning using LLM-powered agents for complex treatment decision-making tasks. While challenges remain in addressing LLM limitations, ongoing advancements hold potential for further refining and expanding GPT-Plan’s capabilities.