Beatrice Magistro, Cecilia Abramson, Daniel Ebanks, Ramit Debnath, R Michael Alvarez
Identifying American climate change free riders and motivating sustainable behavior
Scientific reports
developed validated deep learning radiomics nomogram using multiphase contrastenhanced computed tomography cect images predict neoadjuvant chemotherapy nac response locally advanced gastric cancer lagc patients multicenter study retrospectively included 322 patients diagnosed gastric cancer january 2013 june 2023 two hospitals handcrafted radiomics technique efficientnet v2 neural network applied arterial portal venous delayed phase ct images extract twodimensional handcrafted deep learning features nomogram model built integrating handcrafted signature deep learning signature clinical features discriminative ability assessed using receiver operating characteristics roc curve precisionrecall pr curve model fitting evaluated using calibration curves clinical utility assessed decision curve analysis dca nomogram exhibited excellent performance area roc curve auc 0848 95 confidence interval ci 07930893 0802 95 ci 06880889 0751 95 ci 06520833 training internal validation external validation sets respectively aucs pr curves 0838 95 ci 07560895 0541 95 ci 03290740 0556 95 ci 03760722 corresponding sets nomogram outperformed clinical model handcrafted signature across sets p 005 nomogram model demonstrated good calibration provided greater net benefit within relevant threshold range compared models study created deep learning nomogram using cect images clinical data predict nac response lagc patients undergoing surgical resection offering personalized treatment insights