非小细胞肺癌化疗反应与结局的放射组学预测：一项系统综述与荟萃分析

By extracting and analyzing quantitative features from medical images, radiomics can objectively classify tumor types, assess treatment efficacy, and predict prognostic outcomes. Radiomics-based predictive models for treatment response provide critical support for personalized clinical decision-making. Non-small cell lung cancer(NSCLC) is a major subtype of lung cancer, and chemotherapy remains a core component of its multidisciplinary treatment. However, substantial interpatient heterogeneity in chemotherapy response has been observed. This marked individual variability highlights the urgent need for more precise predictive tools to guide treatment decisions. This study aims to systematically summarize the current landscape of radiomics research on predicting chemotherapy response and survival outcomes in NSCLC, comprehensively assess the scientific value and reporting quality of relevant studies, and ultimately provide highlevel evidence-based evidence to support precision chemotherapy in clinical practice for NSCLC.