Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer

ConclusionThe Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy.Critical relevance statementThe deep learning model using baseline and early follow-up CT images aims to predict OS in patients with stage IV gastric cancer receiving anti-HER2 targeted therapy. This model highlights the spatiotemporal heterogeneity of stage IV GC, assisting clinicians in the early evaluation of the efficacy of anti-HER2 therapy.Key points• Multi-lesion and time series model revealed the spatiotemporal heterogeneity in anti-HER2 therapy.• The Nomo-LDLM-2F score was a valuable prognostic marker for anti-HER2 therapy.• CT-based deep learning model incorporating time-series tumor markers improved performance.Graphical Abstract
Source: Insights into Imaging - Category: Radiology Source Type: research