Zhou, Mi

Abstract Objective . Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery. Approach . Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use. Main Results . The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels. Significance . DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.

357 Background: Prostate cancer (PCa) is the most incident cancer in adult males in the United States, and the second highest in the world. CTCAE is the standardized method to grade the severity of treatment-related adverse events (AEs), but are tedious to collect and subject to inter-observer variability. This study aimed to evaluate the performance of LLMs in automatically extracting and grading CTCAE toxicities from clinical notes and patient-reported outcomes of PCa patients from a clinical trial. Methods: A total of 55 patients from NCT02874014 trial, undergoing proton therapy (PT) for localized PCa, were included. The ground truth of CTCAE graded toxicities was obtained from the trial’s results. Clinical notes within 3 days, and the most recent patient questionnaires within 90 days of toxicity assessment were retrieved from the electronic health record. A comprehensive prompt was developed to extract and CTCAE grade toxicities from the retrieved records. LLMs used for this analysis were OpenAI GPT-4o and Gemini 2.0 Flash. Phase 1 evaluated both LLMs' performance against the ground truth. To ensure the metrics reflected the models’ performance based only on the information available in the text provided, cases where both LLMs contradicted the ground truth were manually reviewed. Phase 2 recalculated LLMs' performance metrics using this adapted ground truth. A conservative stepwise ensemble model was developed to leverage the complementary strengths of both LLMs. Results: In the conservative stepwise ensemble model, Gemini 2.0 Flash served as the initial screening platform to identify AEs in step 1. All cases with identified toxicity events were then reviewed and confirmed using OpenAI GPT-4o in step 2. This effectively reduced false positive rates while preserving high sensitivity. The ensemble model achieved strong performance metrics: binary accuracy (0.954), sensitivity (0.961), specificity (0.953), F1 score (0.836), and grade accuracy (0.932). Conclusions: This study demonstrates the feasibility of the use of LLMs for extraction and CTCAE toxicity grading in PCa patients treated with RT. The ensemble model achieved strong performance metrics. Further validation is needed for other cancer diagnosis and treatment modalities. Phase Model Binary Accuracy Precision Sensitivity Specificity F1 Score Grade Accuracy 1 Gemini 0.946 0.672 1.000 0.939 0.804 0.919 1 OpenAI 0.940 0.785 0.637 0.978 0.703 0.895 2 Gemini 0.956 0.735 1.000 0.950 0.848 0.927 2 OpenAI 0.954 0.917 0.679 0.991 0.780 0.903

Abstract Branched-chain amino acid transaminase 1 (BCAT1) is upregulated selectively in human isocitrate dehydrogenase (IDH) wildtype (WT) but not mutant glioblastoma multiforme (GBM) and promotes IDHWT GBM growth. Through a metabolic synthetic lethal screen, we report here that α-ketoglutarate (AKG) kills IDHWT GBM cells when BCAT1 protein is lost, which is reversed by reexpression of BCAT1 or supplementation with branched-chain α-ketoacids (BCKA), downstream metabolic products of BCAT1. In patient-derived IDHWT GBM tumors in vitro and in vivo, cotreatment of BCAT1 inhibitor gabapentin and AKG resulted in synthetic lethality. However, AKG failed to evoke a synthetic lethal effect with loss of BCAT2, BCKDHA, or GPT2 in IDHWT GBM cells. Mechanistically, loss of BCAT1 increased the NAD+/NADH ratio but impaired oxidative phosphorylation, mTORC1 activity, and nucleotide biosynthesis. These metabolic alterations were synergistically augmented by AKG treatment, thereby causing mitochondrial dysfunction and depletion of cellular building blocks, including ATP, nucleotides, and proteins. Partial restoration of ATP, nucleotides, proteins, and mTORC1 activity by BCKA supplementation prevented IDHWT GBM cell death conferred by the combination of BCAT1 loss and AKG. These findings define a targetable metabolic vulnerability in the most common subset of GBM that is currently incurable. Significance: Metabolic synthetic lethal screening in IDHWT glioblastoma defines a vulnerability to ΑΚG following BCAT1 loss, uncovering a therapeutic strategy to improve glioblastoma treatment. See related commentary by Meurs and Nagrath, p. 2354