Publications by authors named "Sangwoon Jeong"
Purpose: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer.
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- Patients getting radiation therapy often feel anxious, which can cause their muscles to tense up.
- The study used smartwatches to measure heart rate and stress levels in patients during their treatment.
- Results showed that all patients had higher heart rates and stress during treatment, but these levels went down as they kept getting treated.
Article Synopsis
- - The study utilized artificial intelligence to predict stress in patients undergoing radiation therapy by analyzing biological signals, such as heart-rate variability, and calculated stress scores from these features.
- - Various AI models, including both non-pretrained and pretrained (like ChatGPT), were evaluated for their performance in predicting stress and their accuracy in distinguishing different stress levels.
- - Results indicated that over 90% of patients experienced stress during treatment, with significant correlations found between stress scores and respiratory irregularities, highlighting the potential of AI to enhance patient care by identifying those in need of psychological support.
For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated.
View Article and Find Full Text PDFBackground: A decline in serum carbohydrate antigen 19-9 (CA19-9) levels during systemic chemotherapy is considered as a prognostic marker for patients with advanced pancreatic cancer. Neutrophil-to-lymphocyte ratio (NLR) has been extensively studied as a simple and useful indicator of prognosis in various cancers including pancreatic cancer.
Aim: To assess the prognostic significance of NLR and CA19-9 in patients with advanced pancreatic adenocarcinoma received first-line chemotherapy according to CA19-9 positivity.