Abstract：
OBJECTIVE: Histopathological growth patterns (HGPs) of colorectal liver metastases (CRLMs) have prognostic value. However, the differentiation of HGPs relies on postoperative pathology. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomic model to predict HGP pre-operatively, following the latest guidelines.
METHODS: This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and a partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (RTumor), a 2-mm outer ring (RT+2), a 2-mm inner ring (RT-2), and a combined ring (R2+2) on late arterial phase MRI images. Analysis of variance method (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model construction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. DeLong tests were used to compare different models.
RESULTS: Twenty-nine desmoplastic and sixty-four non-desmoplastic CRLMs were included. The radiomic models achieved area under the curve (AUC) values of 0.736, 0.906, 0.804, and 0.794 for RTumor, RT-2, RT+2, and R2+2, respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for RTumor, RT-2, RT+2, and R2+2, respectively, in the validation cohort. RT-2 exhibited the best performance.
CONCLUSIONS: The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively.
METHODS: This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and a partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (RTumor), a 2-mm outer ring (RT+2), a 2-mm inner ring (RT-2), and a combined ring (R2+2) on late arterial phase MRI images. Analysis of variance method (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model construction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. DeLong tests were used to compare different models.
RESULTS: Twenty-nine desmoplastic and sixty-four non-desmoplastic CRLMs were included. The radiomic models achieved area under the curve (AUC) values of 0.736, 0.906, 0.804, and 0.794 for RTumor, RT-2, RT+2, and R2+2, respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for RTumor, RT-2, RT+2, and R2+2, respectively, in the validation cohort. RT-2 exhibited the best performance.
CONCLUSIONS: The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively.
摘要：
目的：结直肠癌肝转移（CRLM）的组织病理学生长模式（HGPs）具有预后价值。然而,HGPs的分化依赖于术后病理。这项研究旨在开发一种基于磁共振成像（MRI）的放射学模型来预测HGP术前，遵循最新的指导方针。
方法：这项回顾性研究包括2014年至2022年期间接受了对比增强肝MRI和部分肝切除术的93例CRLM初治化疗患者。从肿瘤区（RTumor）提取放射学特征，2毫米外环(RT+2),2毫米内圈（RT-2），和动脉晚期MRI图像上的组合环（R22）。使用方差分析方法（ANOVA）和最小绝对收缩和选择算子（LASSO）算法进行特征选择。采用五折交叉验证的Logistic回归模型构建。接收机工作特性曲线,校准曲线,和决策曲线分析用于评估模型性能。使用DeLong测试来比较不同的模型。
结果：纳入了29个去纤维增生性和64个非去纤维增生性CRLM。对于RTumor，影像组学模型的曲线下面积（AUC）值为0.736、0.906、0.804和0.794，分别为RT-2、RT+2和R2+2，在训练队列中。RTumor的AUC值分别为0.713、0.876、0.785和0.777，分别为RT-2、RT+2和R2+2，在验证队列中。RT-2表现出最佳性能。
结论：基于MRI的影像组学模型可以在术前预测CRLM中的HGPs。
方法：这项回顾性研究包括2014年至2022年期间接受了对比增强肝MRI和部分肝切除术的93例CRLM初治化疗患者。从肿瘤区（RTumor）提取放射学特征，2毫米外环(RT+2),2毫米内圈（RT-2），和动脉晚期MRI图像上的组合环（R22）。使用方差分析方法（ANOVA）和最小绝对收缩和选择算子（LASSO）算法进行特征选择。采用五折交叉验证的Logistic回归模型构建。接收机工作特性曲线,校准曲线,和决策曲线分析用于评估模型性能。使用DeLong测试来比较不同的模型。
结果：纳入了29个去纤维增生性和64个非去纤维增生性CRLM。对于RTumor，影像组学模型的曲线下面积（AUC）值为0.736、0.906、0.804和0.794，分别为RT-2、RT+2和R2+2，在训练队列中。RTumor的AUC值分别为0.713、0.876、0.785和0.777，分别为RT-2、RT+2和R2+2，在验证队列中。RT-2表现出最佳性能。
结论：基于MRI的影像组学模型可以在术前预测CRLM中的HGPs。
