Improving Prediction of Complications Post-Proton Therapy in Lung Cancer Using Large Language Models and Meta-Analysis.

PURPOSE: This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment. MATERIALS AND METHODS: We integrated systematic reviews with LLM evaluations, sourcing studies from Web of Science, PubMed, and Scopus, managed via EndNote X20. Inclusion and exclusion criteria ensured literature relevance. Techniques included meta-analysis, heterogeneity assessment using Cochran's Q test and I2 statistics, and subgroup analyses for different complications. Quality and bias risk were assessed using the PROBAST tool and further analyzed with models such as ChatGPT-4, Llama2-13b, and Llama3-8b. Evaluation metrics included AUC, accuracy, precision, recall, F1 score, and time efficiency (WPM). RESULTS: The meta-analysis revealed an overall effect size of 0.78 for model predictions, with high heterogeneity observed (I2 = 72.88%, P < 0.001). Subgroup analysis for radiation-induced esophagitis and pneumonitis revealed predictive effect sizes of 0.79 and 0.77, respectively, with a heterogeneity index (I2) of 0%, indicating that there were no significant differences among the models in predicting these specific complications. A literature assessment using LLMs demonstrated that ChatGPT-4 achieved the highest accuracy at 90%, significantly outperforming the Llama3 and Llama2 models, which had accuracies ranging from 44% to 62%. Additionally, LLM evaluations were conducted 3229 times faster than manual assessments were, markedly enhancing both efficiency and accuracy. The risk assessment results identified nine studies as high risk, three as low risk, and one as unknown, confirming the robustness of the ChatGPT-4 across various evaluation metrics. CONCLUSION: This study demonstrated that the integration of large language models with meta-analysis techniques can significantly increase the efficiency of literature evaluations and reduce the time required for assessments, confirming that there are no significant differences among models in predicting post proton therapy complications in lung cancer patients.

Using Advanced AI to Improve Predictions of Treatment Side Effects in Lung Cancer: This research uses cutting-edge artificial intelligence (AI) techniques, including large language models like ChatGPT-4, to better predict potential side effects in lung cancer patients undergoing proton therapy. By analyzing extensive scientific literature quickly and accurately, this approach has proven to enhance the evaluation process, making it faster and more reliable in foreseeing complications from treatments.

Characterization of particulate-phase polycyclic aromatic hydrocarbons emitted from incense burning and their bioreactivity in RAW264.7 macrophage.

This study investigated the effects of particle-bound polycyclic aromatic hydrocarbons (PAHs) produced from burning three incense types on and their bioreactivity in the RAW 264.7 murine macrophage cell line. Gas chromatography/mass spectrometry was used to determine the levels of 16 identified PAHs. Macrophages were exposed to incense particle extracts at concentrations of 0, 3.125, 6.25, 12.5, 25, 50, and 100 µg/mL for 24 h. After exposure, cell viability and nitric oxide (NO) and inflammatory mediator [tumor necrosis factor (TNF)-α] production of the cells were examined. The mean atomic hydrogen (H) to carbon (C) ratios in the environmentally friendly, binchotan charcoal, and lao shan incenses were 0.69, 1.13, and 1.71, respectively. PAH and total toxic equivalent (TEQ) mass fraction in the incenses ranged from 137.84 to 231.00 and 6.73-26.30 pg/µg, respectively. The exposure of RAW 264.7 macrophages to incense particles significantly increased TNF-α and NO production and reduced cell viability. The cells treated with particles collected from smoldering the environmentally friendly incense produced more NO and TNF-α compared to other incenses. Additionally, the TEQ of fluoranthene (FL), pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (INP), dibenz[a,h]anthracene (DBA), and benzo[g,h,i]perylene [B(ghi)P] had a significant correlation (R2 = 0.64-0.98, P < 0.05) with NO and TNF-α production. The current findings indicate that incense particle-bound PAHs are biologically active and that burning an incense with a lower H/C ratio caused higher bioreactivity. The stimulatory effect of PAH-containing particles on molecular mechanisms of inflammation are critical for future study.