Seo Hee Choi

Sangjoon Park, Chan Woo Wee, Seo Hee Choi et al.

Accurate survival prediction in radiotherapy (RT) is critical for optimizing treatment decisions. This study developed and validated the RT-Surv framework, which integrates general-domain, open-source large language models (LLMs) to structure unstructured electronic health records alongside structured clinical data. Using data from 34,276 patients and an external cohort of 852, the framework successfully transformed unstructured clinical information into structured formats. Incorporating LLM-structured clinical features improved the concordance index from 0.779 to 0.842 during external validation, demonstrating a significant performance enhancement. Key LLM-structured features, such as disease extent, general condition, and RT purpose, showed high predictive importance and aligned closely with statistically significant predictors identified through conventional statistical analyses, thereby improving model interpretability. Furthermore, the framework enhanced risk stratification, enabling more distinct differentiation among low-, intermediate-, and high-risk groups (p < 0.001) using LLM-structured clinical features. These findings highlight the potential of LLMs to convert unstructured data into actionable insights, improving predictive modeling and patient outcomes in clinics.

Sangjoon Park, Yong Bae Kim, Jee Suk Chang et al.

As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life. However, evaluating breast cosmesis presents challenges due to the inherently subjective nature of expert labeling. In this study, we present a novel automated approach, Attention-Guided Denoising Diffusion Anomaly Detection (AG-DDAD), designed to assess breast cosmesis following surgery, addressing the limitations of conventional supervised learning and existing anomaly detection models. Our approach leverages the attention mechanism of the distillation with no label (DINO) self-supervised Vision Transformer (ViT) in combination with a diffusion model to achieve high-quality image reconstruction and precise transformation of discriminative regions. By training the diffusion model on unlabeled data predominantly with normal cosmesis, we adopt an unsupervised anomaly detection perspective to automatically score the cosmesis. Real-world data experiments demonstrate the effectiveness of our method, providing visually appealing representations and quantifiable scores for cosmesis evaluation. Compared to commonly used rule-based programs, our fully automated approach eliminates the need for manual annotations and offers objective evaluation. Moreover, our anomaly detection model exhibits state-of-the-art performance, surpassing existing models in accuracy. Going beyond the scope of breast cosmesis, our research represents a significant advancement in unsupervised anomaly detection within the medical domain, thereby paving the way for future investigations.