首页|Data on Radiation Pneumonitis Reported by Lixia Xu and Colleagues (Predicting radiation pneumonitis in lung cancer: a EUDbased machine learning approach for volumetric modulated arc therapy patients)
Data on Radiation Pneumonitis Reported by Lixia Xu and Colleagues (Predicting radiation pneumonitis in lung cancer: a EUDbased machine learning approach for volumetric modulated arc therapy patients)
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New research on Lung Diseases and Conditions - Radiation Pneumonitis is the subject of a report. According to news reporting from Hangzhou, People's Republic of China, by NewsRx journalists, research stated, "This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT." Financial support for this research came from Natural Science Foundation of Zhejiang Province. The news correspondents obtained a quote from the research, "Patients were categorized based on the onset of grade Ⅱ or higher radiation pneumonitis (RP 2_+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter a, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2_+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman- Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. The optimal a-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2_+ and non-RP 2_+ groups. A strong correlation coefficient of 0.929 (P ＜0.01) was observed between lung EUD (a = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUDbased predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. The likelihood of developing RP 2_+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-offreedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the a-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2_+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients."
HangzhouPeople's Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesHealth and MedicineInterstitial Lung Diseases and ConditionsLung CancerLung Diseases and ConditionsLung InjuryLung NeoplasmsMachine LearningOncologyRadiation PneumonitisTherapy
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Frontiers Media Sa
