用腹部CT的肝脏影像特征预测结直肠肿瘤，无需额外检查。
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
- 从常规CT中提取肝脏影像特征，结合机器学习建模。
- 模型在测试集上准确率（AUROC）达0.810，显著优于临床模型。
- 适合希望提升肠镜筛查率的医疗机构参考使用。
非侵入性结直肠癌（CRC）筛查是提高肠镜参与率、降低死亡率的关键机会。本研究探索通过肝-肠轴，利用常规腹部CT中肝脏的影像组学特征预测结直肠病变，作为新型机会性筛查方法。回顾性分析1,997名接受肠镜和腹部CT的患者，其中无病变者1,189人，有病变者共808人（腺瘤423例，癌症385例）。采用Radiomics Processing ToolKit（RPTK）对三维肝脏分割区域提取影像组学特征，并进行筛选与分类。数据分为训练集（n=1,397）和测试集（n=600）。基于前20个最具信息量的特征，训练五种机器学习模型，通过5折交叉验证选择最佳模型集成。最优的基于影像组学的XGBoost模型在测试集上达到AUROC 0.810，明显优于仅使用临床数据的最佳模型（测试集AUROC: 0.457）。区分癌症与腺瘤的子分类任务表现较差（测试集AUROC: 0.674）。研究证实，从常规腹部CT中提取的肝脏影像组学特征可有效预测结直肠病变。该方法具有普适性和广泛可及性，为结直肠癌筛查提供新工具，同时揭示肝-肠轴作为新型生物标志物来源的潜力，为后续转化研究提供机制假说。
原文摘要 · Abstract (English)
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential of the gut-liver axis for predicting colorectal neoplasia through liver-derived radiomic features extracted from routine CT images as a novel opportunistic screening approach. In this retrospective study, we analyzed data from 1,997 patients who underwent colonoscopy and abdominal CT. Patients either had no colorectal neoplasia (n=1,189) or colorectal neoplasia (n_total=808; adenomas n=423, CRC n=385). Radiomics features were extracted from 3D liver segmentations using the Radiomics Processing ToolKit (RPTK), which performed feature extraction, filtering, and classification. The dataset was split into training (n=1,397) and test (n=600) cohorts. Five machine learning models were trained with 5-fold cross-validation on the 20 most informative features, and the best model ensemble was selected based on the validation AUROC. The best radiomics-based XGBoost model achieved a test AUROC of 0.810, clearly outperforming the best clinical-only model (test AUROC: 0.457). Subclassification between colorectal cancer and adenoma showed lower accuracy (test AUROC: 0.674). Our findings establish proof-of-concept that liver-derived radiomics from routine abdominal CT can predict colorectal neoplasia. Beyond offering a pragmatic, widely accessible adjunct to CRC screening, this approach highlights the gut-liver axis as a novel biomarker source for opportunistic screening and sparks new mechanistic hypotheses for future translational research.
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