本研究旨在建立基于术前增强CT的肿瘤放射组学质量和数量模型（RQQM）来预测结直肠肝转移癌根治性手术（CRLM）后的早期复发。对来自3个中心的282例病例进行回顾性分析。使用单变量和多变量逻辑回归（LR）检查临床危险因素以构建临床模型。使用最小绝对收缩和选择算子（LASSO）提取放射组学特征以进行降维。采用LR学习算法构建放射组学模型、RQQM（放射组学-TBS）、组合模型（放射组学-临床）、临床风险评分（CRS）模型和肿瘤负荷评分（TBS）模型。使用曲线下面积（AUC）、决策曲线分析（DCA）和校准曲线进行模型间比较。对数秩检验评估了无病生存 (DFS) 和总生存 (OS) 的差异。临床特征筛查将 CRS、KRAS/NRAS/BRAF 和肝叶分布确定为危险因素。放射组学模型 RQQM 组合模型在训练、内部和外部验证队列中表现出比 CRS 和 TBS 模型更高的 AUC 值（Delong 检验 P < 0.05）。 RQQM 优于放射组学模型，但略逊于组合模型。生存曲线显示 RQQM 的 1 年 DFS 和 3 年 OS 存在统计学显着性差异 (P< 0.001)。RQQM 整合了“质量”（放射组学）和“数量”（TBS）。放射组学模型优于TBS模型，对患者预后影响更大。在缺乏临床数据的情况下，仅依靠影像数据的 RQQM 在预测 CRLM 根治性手术后的早期复发方面显示出优势。© 2024。作者获得 Federación de Sociedades Españolas de Oncología (FESEO) 的独家许可。

This study aimed to develop a tumor radiomics quality and quantity model (RQQM) based on preoperative enhanced CT to predict early recurrence after radical surgery for colorectal liver metastases (CRLM).A retrospective analysis was conducted on 282 cases from 3 centers. Clinical risk factors were examined using univariate and multivariate logistic regression (LR) to construct the clinical model. Radiomics features were extracted using the least absolute shrinkage and selection operator (LASSO) for dimensionality reduction. The LR learning algorithm was employed to construct the radiomics model, RQQM (radiomics-TBS), combined model (radiomics-clinical), clinical risk score (CRS) model and tumor burden score (TBS) model. Inter-model comparisons were made using area under the curve (AUC), decision curve analysis (DCA) and calibration curve. Log-rank tests assessed differences in disease-free survival (DFS) and overall survival (OS).Clinical features screening identified CRS, KRAS/NRAS/BRAF and liver lobe distribution as risk factors. Radiomics model, RQQM, combined model demonstrated higher AUC values compared to CRS and TBS model in training, internal and external validation cohorts (Delong-test P < 0.05). RQQM outperformed the radiomics model, but was slightly inferior to the combined model. Survival curves revealed statistically significant differences in 1-year DFS and 3-year OS for the RQQM (P < 0.001).RQQM integrates both "quality" (radiomics) and "quantity" (TBS). The radiomics model is superior to the TBS model and has a greater impact on patient prognosis. In the absence of clinical data, RQQM, relying solely on imaging data, shows an advantage in predicting early recurrence after radical surgery for CRLM.© 2024. The Author(s), under exclusive licence to Federación de Sociedades Españolas de Oncología (FESEO).