用CT自动分析身体成分，提升结肠切除术后风险预测准确率
Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
- 从CT扫描自动提取300+个体成分特征，融合临床数据建模
- 预测1年全因死亡率的C-index达0.78，优于传统NSQIP评分
- 适合外科风险评估、精准医疗及医学影像智能分析研究者
目的：评估术前从CT扫描中自动提取的身体成分指标能否独立或联合临床变量/现有风险预测工具，预测结肠切除术后结局。主要终点为术后1年全因死亡率，采用1年随访的Cox比例风险模型，以一致性指数（C-index）和综合贝叶斯得分（IBS）评估预测性能。次要终点包括术后并发症、非计划再入院、输血及严重感染，通过逻辑回归的AUC与贝叶斯得分评估。个体CT衍生身体成分指标与结局的关联性以比值比（OR）表示。在多个椎体水平上从术前CT提取超过300个特征，涵盖骨骼肌面积、密度、脂肪区域及组织间指标。2012年后所有手术均具备NSQIP评分。
原文摘要 · Abstract (English)
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combined with clinical variables or existing risk predictors. Main outcomes and measures: The primary outcome was the predictive performance for 1-year all-cause mortality following colectomy. A Cox proportional hazards model with 1-year follow-up was used, and performance was evaluated using the concordance index (C-index) and Integrated Brier Score (IBS). Secondary outcomes included postoperative complications, unplanned readmission, blood transfusion, and severe infection, assessed using AUC and Brier Score from logistic regression. Odds ratios (OR) described associations between individual CT-derived body composition metrics and outcomes. Over 300 features were extracted from preoperative CTs across multiple vertebral levels, including skeletal muscle area, density, fat areas, and inter-tissue metrics. NSQIP scores were available for all surgeries after 2012.
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