Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics
Mengjie Fang, Jie Tian, Di Dong
Abstract
Gastric cancer is one of the most common malignancies, ranking fifth in incidence and third in mortality worldwide.1 The high mortality rate is mainly caused by delayed early diagnosis and inappropriate choice of treatment. Neoadjuvant chemotherapy (NACT) combined with surgery is recommended as one of the routine treatment options for locally advanced gastric cancer (LAGC).2 However, clinical practice has found that NACT is not effective for all patients, and there are significant individual differences in its therapeutic effect among patients.
Topics & Concepts
MedicineRadiomicsCancerChemotherapyNeoadjuvant therapyOncologyInternal medicineRadiologyBreast cancerGastric Cancer Management and OutcomesRadiomics and Machine Learning in Medical ImagingColorectal Cancer Screening and Detection