Citations

Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Background/Objectives: Proton therapy delivers highly conformal doses to the target area without producing an exit dose, minimizing cumulative doses to healthy liver tissue. This study aims to evaluate current practices, challenges, and variations in the implementation of proton stereotactic body radiation therapy (SBRT) and hypofractionated therapy for liver malignancies, with the goal of providing a technical assessment to promote broader adoption and support future clinical trials. Methods and Materials: An extensive survey was conducted by NRG Oncology across North American proton treatment centers to assess the current practices of proton liver SBRT and hypofractionated therapy. The survey focused on key aspects, including patient selection, prescription and normal tissue constraints, simulation and motion management, treatment planning, quality assurance (QA), treatment delivery, and the use of image-guided radiation therapy (IGRT). Results: This survey captures the current practice patterns and status of proton SBRT and hypofractionated therapy in liver cancer treatment. Proton therapy is increasingly preferred for treating inoperable liver malignancies due to its ability to minimize healthy tissue exposure. However, the precision required for proton therapy presents challenges, particularly in managing uncertainties and target motion during high-dose fractions and short treatment courses. Survey findings revealed significant variability in clinical practices across centers, highlighting differences in motion management, dose fractionation schedules, and QA protocols. Conclusion: Proton SBRT and hypofractionated therapy offer significant potential for treating liver malignancies. A comprehensive approach involving precise patient selection, treatment planning, and QA is essential for ensuring safety and effectiveness. This survey provides valuable insights into current practices and challenges, offering a foundation for technical recommendations to optimize the use of proton therapy and guide future clinical trials.

Abstract Background The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose This study aims to investigate whether fine‐tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD‐10 code prediction in radiation oncology. Methods Data for 15 724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD‐10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD‐10 code, respectively) for the supervised fine‐tuning of these three tasks. Open source LLaMA2‐7B and Mistral‐7B models were utilized for the fine‐tuning with the Low‐Rank Approximations method. Accuracy and ROUGE‐1 score were reported for the fine‐tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F‐1 score were evaluated for Task (2) and (3). One‐sided Wilcoxon signed‐rank tests were used to statistically analyze the results. Results Fine‐tuned LLMs outperformed original LLMs across all tasks with p value ≤ 0.001. Clinical evaluation demonstrated that over 60% of the fine‐tuned LLMs‐generated treatment regimens were clinically acceptable. Precision, recall, and F1‐score showed improved performance of fine‐tuned LLMs. Conclusion Fine‐tuned LLMs demonstrated statistically significant improvements over original LLMs upon three clinically important tasks in radiation oncology. This study explored the feasibility of applying fine‐tuned LLMs in radiation oncology, inspiring further development of utilizing LLMs to assist with radiation oncology tasks.

Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice. Materials and Methods The patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD). Results The proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics. Conclusion Our proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.

Objectives/Goals: Our study’s objective is to evaluate RadOnc-GPT, a GPT-4o powered LLM, in generating responses to in-basket messages related to prostate cancer treatment in the Radiation Oncology department. By integrating it with electronic health record (EHR) systems, the goal is to assess its impact on clinician workload, response quality, and efficiency in healthcare communication. Methods/Study Population: RadOnc-GPT was integrated with patient EHRs from both hospital-wide and radiation-oncology-specific databases. The study examined 158 pre-recorded in-basket message interactions from 90 non-metastatic prostate cancer patients. Quantitative natural language processing analysis and two randomized single-blinded grading studies, involving four clinicians and four nurses, were conducted to evaluate RadOnc-GPT’s response quality in completeness, correctness, clarity, empathy, and estimated editing time. Response times were measured to estimate the time saved for clinicians and nurses. The study population included patient messages across all phases of care (pre-, during, and post-treatment) for those undergoing radiotherapy. Results/Anticipated Results: In the single-blinded grader study, clinician graders evaluated 316 responses (158 from human care teams and 158 from RadOnc-GPT). Results showed RadOnc-GPT outperformed human responses in empathy and clarity, while humans excelled in completeness and correctness. Sentiment analyses using TextBlob and VADER revealed RadOnc-GPT responses had a positive mean score of 0.25, whereas human responses clustered around neutral. VADER analysis indicated a high median score for RadOnc-GPT, nearing 1.0, reflecting predominantly positive sentiment, while human responses displayed a broader sentiment range, indicating sensitivity to context. Clinicians averaged 3.60 minutes (SD 1.44) to respond, compared to 6.39 minutes (SD 4.05) for nurses, highlighting RadOnc-GPT’s efficiency in generating timely responses. Discussion/Significance of Impact: RadOnc-GPT effectively generated responses to individualized patient in-basket messages, comparable to those from radiation oncologists and nurses. While human oversight is still necessary to avoid errors, RadOnc-GPT can speed up response times and reduce pressure on care teams, shifting their role from drafting to reviewing responses.

Abstract Background In breast cancer patients treated with pencil‐beam scanning proton therapy (PBS), the increased linear energy transfer (LET) near the end of the proton range can affect nearby ribs. This may associate with a higher risk of rib fractures. Purpose To study the effect of LET on rib fracture in breast cancer patients treated with PBS using a novel tool of dose‐LET volume histogram (DLVH). Methods From a prospective registry of patients treated with post‐mastectomy proton therapy to the chest wall and regional lymph nodes for breast cancer between 2015 and 2020, we retrospectively identified rib fracture cases detected after completing treatment. Contemporaneously treated control patients who did not develop rib fracture were matched to patients 2:1 considering prescription dose, boost location, reconstruction status, laterality, chest wall thickness, and treatment year. The DLVH index, V (d, l), defined as volume( V ) of the structure with at least dose(d) and dose‐averaged LET (l) (LETd), was calculated. DLVH plots between the fracture and control group were compared. Conditional logistic regression (CLR) model was used to establish the relation of V (d, l) and the observed fracture at each combination of d and l . The p ‐value derived from CLR model shows the statistical difference between fracture patients and the matched control group. Using the 2D p ‐value map derived from CLR model, the DLVH features associated with the patient outcomes were extracted. Results Seven rib fracture patients were identified, and fourteen matched patients were selected for the control group. The median time from the completion of proton therapy to rib fracture diagnosis was 12 months (range 5–14 months). Two patients had grade 2 symptomatic rib fracture while the remaining 5 were grade 1 incidentally detected on imaging. The derived p ‐value map demonstrated larger V (0–36 Gy[RBE], 4.0–5.0 keV/µm) in patients experiencing fracture ( p < 0.1). For example, the p ‐value for V (30 Gy[RBE], 4.0 keV/um) was 0.069. Conclusion In breast cancer patients receiving PBS, a larger volume of chest wall receiving moderate dose and high LETd may result in an increased risk of rib fracture.

425 Background: Prostate cancer (PC) ranks among the most prevalent cancers in men, with incidence rising due to factors like aging populations and enhanced screening techniques. Effective data extraction from clinical records is crucial for advancing research and improving patient care; however, traditional manual methods can be time-consuming and susceptible to mistakes. This study seeks to evaluate RadOnc-GPT against human annotation for extracting essential clinical information from prostate cancer clinical records. Methods: RadOnc-GPT is a retrieval augmented generation (RAG)-based chatbot with access to a variety of patient data including treatment details, radiology reports, and clinical notes. RadOnc-GPT uses Open AI’s gpt-4o model. Patients who received curative intent radiotherapy as initial treatment for PC in our institution between 2014 and 2023 were randomly selected for evaluation. Relevant Clinical factors were extracted by a human expert annotator on our on-site RedCap database. The extraction of the same variables was performed using RadOnc-GPT. A comparison between the ground truth (human annotation) and RadOnc-GPT, to evaluate the performance of the AI system, was carried out including accuracy, recall and F1 score. Results: A total of 382 PC patients were included in the cohort for data extraction. The clinical variables extracted were the treatment intent, NCCN risk category, T and N stage, Gleason score, radiation volume, and Androgen Deprivation therapy (ADT) use. The overall performance of RadOnc-GPT was excellent, with the highest accuracy achieved for treatment intent (99.7%), Gleason score (98.4%), and Treatment Volume (90.1-99.5%). Conclusions: RadOnc-GPT performed excellent at extracting relevant clinical details from the electronic medical record and radiation oncology information systems with dramatic reduction in time and cost compared with manual review. LLMs could be highly useful for large-scale data extraction in PC research, offering a promising solution for automating the accurate data extraction process. Correct (n) Total (n) Accuracy Recall F1 score RT with curative intent 381 382 99.7 99.9 99.9 Prostate Cancer Risk Category 345 382 90.3 90.4 95.1 Primary tumor staging (T) at diagnosis 368 382 96.3 96.3 97.9 Regional nodal status (N) at diagnosis 352 382 92.1 92.2 95.9 Gleason Score Primary Secondary Total 376372376 382382382 98.497.498.4 98.597.498.5 99.298.799.2 Treated Areas Prostate Pelvic Nodes Para-Aortic Nodes Distant Site 380344378372 382382382382 99.590.19997.4 99.490.199.197.4 99.79599.598.7 ADT Use with RT 365 382 95.5 95.7 97.8