Ömer Özer, Burak Köksal, Ahmet Altinok
Understanding university students' attitudes and preferences for internet-based mental health interventions
Internet interventions
present many limitations evaluation lymph node metastasis lung adenocarcinoma currently demand safe accurate method predict lymph node metastasis lung cancer study radiomics used accurately predict lymph node status lung adenocarcinoma patients based contrastenhanced ct total 503 cases fulfilled analysis requirements gathered two distinct hospitals among 287 patients exhibited lymph node metastasis lnm 216 patients confirmed without lymph node metastasis lnm using traditional deep learning methods 22318 features extracted segmented images patients enhanced ct spearman test least absolute shrinkage selection operator used effectively reduce dimension feature data enabling us focus pertinent features enhance overall analysis finally classification model lung adenocarcinoma lymph node metastasis constructed machine learning algorithm accuracy auc specificity precision recall f1 used evaluate efficiency model incorporating comprehensively selected set features extreme gradient boosting method xgboost effectively distinguished status lymph nodes patients lung adenocarcinoma accuracy auc specificity precision recall f1 prediction model performance external test set 0765 0845 0705 0784 0811 0797 respectively moreover decision curve analysis calibration curve confusion matrix model external test set indicated stability accuracy model leveraging enhanced ct images study introduces noninvasive classification prediction model based extreme gradient boosting method approach exhibits remarkable precision identifying lymph node status lung adenocarcinoma patients offering safe accurate alternative invasive procedures providing clinicians reliable tool diagnosing assessing disease progression method holds potential significantly improve patient outcomes enhance overall quality clinical practice