Yadigar Ordu, Sinan Aydoğan, Nurcan Çalışkan
The effect of interactive learning method on nursing students' learning of movement requirement: A randomized controlled study
Nurse education today
developing deep learning radiomics model longitudinal breast ultrasound sonographers axillary ultrasound diagnosis predicting axillary lymph node aln response neoadjuvant chemotherapy nac breast cancer breast cancer patients undergoing nac followed surgery recruited three centers november 2016 december 2022 collected ultrasound images extracting tumorderived radiomics deep learning features selecting quantitative features various methods two machine learning models based random forest developed using prenac postnac features support vector machine integrated data fusion model evaluated via area curve auc decision curve analysis calibration curves compared fusion models performance sonographers diagnosis prenac postnac axillary ultrasonography referencing histological outcomes sentinel lymph node biopsy axillary lymph node dissection validation cohort fusion model outperformed prenac auc 0899 vs 0786 p 0001 postnac models auc 0899 vs 0853 p 0014 well sonographers diagnosis aln status prenac postnac axillary ultrasonography auc 0899 vs 0719 p 0001 decision curve analysis revealed patient benefits fusion model across threshold probabilities 002 098 model also enhanced sonographers diagnostic ability increasing accuracy 719 792 deep learning radiomics model accurately predicted aln response nac breast cancer furthermore model assist sonographers improve diagnostic ability aln status surgery ai model based pre postneoadjuvant chemotherapy ultrasound accurately predict axillary lymph node metastasis assist sonographers axillary diagnosis axillary lymph node metastasis status affects choice surgical treatment currently relies subjective ultrasound ai model outperformed sonographers visual diagnosis axillary ultrasound deep learning radiomics model improve sonographers diagnosis might assist surgical decisionmaking