Public articles linked to the same research event.
Physics in Medicine and Biology In a training cohort (n = 186), a validation cohort (n = 116), and a prospective multicenter validation cohort (n = 269), this study extracted radiomic and supervised deep learning features from pretreatment T2-weighted MRI and compared manually developed with GPT-assisted modeling workflows, finding that fused features achieved the highest AUC for both manually developed and GPT-assisted logistic regression models (0.759 versus 0.763 ± 0.003 in the training cohort, 0.714 versus 0.741 ± 0.008 in the validation cohort, and 0.700 versus 0.706 ± 0.
In a training cohort (n = 186), a validation cohort (n = 116), and a prospective multicenter validation cohort (n = 269), this study extracted radiomic and supervised deep learning features from pretreatment T2-weighted MRI and compared manually developed with GPT-assisted modeling workflows, finding that fused features achieved the highest AUC for both manually developed and GPT-assisted logistic regression models (0.759 versus 0.763 ± 0.003 in the training cohort, 0.714 versus 0.741 ± 0.008 in the validation cohort, and 0.700 versus 0.706 ± 0.
In a training cohort (n = 186), a validation cohort (n = 116), and a prospective multicenter validation cohort (n = 269), this study extracted radiomic and supervised deep learning features from pretreatment T2-weighted MRI and compared manually developed with GPT-assisted modeling workflows, finding that fused features achieved the highest AUC for both manually developed and GPT-assisted logistic regression models (0.759 versus 0.763 ± 0.003 in the training cohort, 0.714 versus 0.741 ± 0.008 in the validation cohort, and 0.700 versus 0.706 ± 0.
In a training cohort (n = 186), a validation cohort (n = 116), and a prospective multicenter validation cohort (n = 269), this study extracted radiomic and supervised deep learning features from pretreatment T2-weighted MRI and compared manually developed with GPT-assisted modeling workflows, finding that fused features achieved the highest AUC for both manually developed and GPT-assisted logistic regression models (0.759 versus 0.763 ± 0.003 in the training cohort, 0.714 versus 0.741 ± 0.008 in the validation cohort, and 0.700 versus 0.706 ± 0.