Yan Zhang, Mu Li, Hong Jiang, Huijing Shi, Biao Xu, Salla Atkins, Xu Qian
Development and validation of a Chinese parental health literacy questionnaire for caregivers of children 0 to 3 years old
BMC pediatrics
accurate rapid discrimination benign malignant ovarian masses crucial optimal patient management study aimed establish ultrasound imagebased nomogram combining clinical radiomics deep transfer learning features automatically classify ovarian masses low risk intermediatehigh risk malignancy lesions according ovarian adnexal reporting data system orads ultrasound images 1080 patients 1080 ovarian masses included training cohort consisting 683 patients collected south china hospital shenzhen university test cohort consisting 397 patients collected shenzhen university general hospital workflow included image segmentation feature extraction feature selection model construction pretrained resnet101 model achieved best performance among different monomodal features fusion feature models nomogram achieved highest level diagnostic performance auc 0930 accuracy 849 sensitivity 935 specificity 817 ppv 654 npv 971 precision 654 diagnostic indices nomogram higher junior radiologists diagnostic indices junior radiologists significantly improved assistance model calibration curves showed good agreement prediction nomogram actual classification ovarian masses decision curve analysis showed nomogram clinically useful model exhibited satisfactory diagnostic performance compared junior radiologists potential improve level expertise junior radiologists provide fast effective method ovarian cancer screening