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.

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models. MATERIALS AND METHODS: Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson's correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning. RESULTS: Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD. CONCLUSION: The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer.

Machine learning algorithms were used to analyze the odds and predictors of complications of thyroid damage after radiation therapy in patients with head and neck cancer. This study used decision tree (DT), random forest (RF), and support vector machine (SVM) algorithms to evaluate predictors for the data of 137 head and neck cancer patients. Candidate factors included gender, age, thyroid volume, minimum dose, average dose, maximum dose, number of treatments, and relative volume of the organ receiving X dose (X: 10, 20, 30, 40, 50, 60 Gy). The algorithm was optimized according to these factors and tenfold cross-validation to analyze the state of thyroid damage and select the predictors of thyroid dysfunction. The importance of the predictors identified by the three machine learning algorithms was ranked: the top five predictors were age, thyroid volume, average dose, V50 and V60. Of these, age and volume were negatively correlated with thyroid damage, indicating that the greater the age and thyroid volume, the lower the risk of thyroid damage; the average dose, V50 and V60 were positively correlated with thyroid damage, indicating that the larger the average dose, V50 and V60, the higher the risk of thyroid damage. The RF algorithm was most accurate in predicting the probability of thyroid damage among the three algorithms optimized using the above factors. The Area under the receiver operating characteristic curve (AUC) was 0.827 and the accuracy (ACC) was 0.824. This study found that five predictors (age, thyroid volume, mean dose, V50 and V60) are important factors affecting the chance that patients with head and neck cancer who received radiation therapy will develop hypothyroidism. Using these factors as the prediction basis of the algorithm and using RF to predict the occurrence of hypothyroidism had the highest ACC, which was 82.4%. This algorithm is quite helpful in predicting the probability of radiotherapy complications. It also provides references for assisting medical decision-making in the future.

Preparation and Application of In-Situ Loaded Silver Nanoparticles Antibacterial Fresh-Keeping Composite Paper.

The freshness and safety of fruits and vegetables affect our daily life. Paper products are often used in the packaging and transportation of fruits and vegetables, and these can provide other functions besides packaging after certain modifications and additions. In this study, the AgNPs/1-MCP antibacterial fresh-keeping composite paper was prepared by in-situ loaded silver nanoparticles and spraying 1-MCP solution. Moreover, the prepared paper was used to preserve sweet cherries. It was found that the prepared AgNPs/1-MCP antibacterial fresh-keeping composite paper could effectively inhibit E. coli and S. aureus. When the addition of 1-MCP in the paper was 0.05 g, the fresh-keeping effect on cherries was the best. Under this optimal condition, the weight loss ratio of the cherries was reduced by 1.93%, the firmness was increased by 27.7%, and the soluble solid content was increased by 25%. The preservation time was extended from 4 days to 12 days, three times that of the untreated ones. The prepared fresh-keeping material is environmentally friendly, non-toxic and harmless, simple to prepare and convenient to use, and is expected to become one of the important fresh-keeping methods for fruits.

Clickable and smart drug delivery vehicles accelerate the healing of infected diabetic wounds.

In this study, an adipic acid dihydrazide (ADH)/ tannic acid (TA)-grafted hyaluronic acid (HA)-based multifunctional hydrogel was synthesized through a spontaneous amino-yne click reaction and used to promote the improved healing of infected diabetic wounds. This hydrogel exhibited a range of beneficial properties such as tunable gelation time, adjustable mechanical properties, pH-sensitive response characteristics, excellent injectability, the ability to readily adhere to tissue, and ultra-intimate contact capabilities. Following the encapsulation of ultrasmall Ag nanoclusters (AgNCs) and deferoxamine loaded polydopamine/ hollow mesoporous manganese dioxide (PHMD, PDA/H-mMnO2@DFO) nanoparticles, the prepared hydrogel presented with robust antibacterial, anti-inflammatory, and pro-angiogenic properties and a desirable smart drug release profile. In this fabricated platform, PHMD was able to effectively alleviate localized oxidative stress and prolonged oxygen deprivation via the decomposition of endogenous H2O2 to produce O2. Further in vivo assays revealed that this hydrogel was capable of facilitating the healing of infected wounds through the sequential engagement of antibacterial, anti-inflammatory, and pro-angiogenic activities. Together, this synthesized clickable environmentally-responsive hydrogel offers great promise as a tool that can be applied to aid in the healing of chronically infected diabetic wounds and other inflammatory conditions.