Gu Mengting, Zou Wenjie, Chen Huilin, He Ruilin, Zhao Xingyu, Jia Ningyang, Liu Wanmin, Wang Peijun
Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Cancer Imaging. 2025 Jul 7;25(1):87. doi: 10.1186/s40644-025-00895-9.
The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative gadobenate-enhanced (Gd-BOPTA) magnetic resonance imaging (MRI) using multilayer perceptron (MLP) deep learning to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients.
A total of 230 patients with histopathologically confirmed HCC who underwent preoperative Gd-BOPTA MRI before hepatectomy were retrospectively enrolled from three hospitals (144, 54, and 32 in training, test, and validation set, respectively). Univariate and multivariate logistic regression analyses were used to determine independent clinicoradiological predictors significantly associated with VETC, which then constituted the clinicoradiological model. Regions of interest (ROIs) included four modes, intratumoral (Tumor), peritumoral area ≤ 2 mm (Peri2mm), intratumoral + peritumoral area ≤ 2 mm (Tumor + Peri2mm) and intratumoral integrated with peritumoral ≤ 2 mm as a whole (TumorPeri2mm). A total of 7322 radiomics features were extracted respectively for ROI(Tumor), ROI(Peri2mm), ROI(TumorPeri2mm) and 14644 radiomics features for ROI(Tumor + Peri2mm). Least absolute shrinkage and selection operator (LASSO) and univariate logistic regression analysis were used to select the important features. Seven different machine learning classifiers respectively combined the radiomics signatures selected from four ROIs to constitute different models, and compare the performance between them in three sets and then select the optimal combination to become the radiomics model we need. Then a radiomics score (rad-score) was generated, which combined significant clinicoradiological predictors to constituted the fusion model through multivariate logistic regression analysis. After comparing the performance of the three models using area under receiver operating characteristic curve (AUC), integrated discrimination index (IDI) and net reclassification index (NRI), choose the optimal predictive model for VETC prediction.
Arterial peritumoral enhancement and peritumoral hypointensity on hepatobiliary phase (HBP) were independent risk factors for VETC, and constituted the Radiology model, without any clinical variables. Arterial peritumoral enhancement defined as the enhancement outside the tumor boundary in the late stage of arterial phase or early stage of portal phase, extensive contact with the tumor edge, which becomes isointense during the DP. MLP deep learning algorithm integrated radiomics features selected from ROI TumorPeri2mm was the best combination, which constituted the radiomics model (MLP model). A MLP score (MLP_score) was calculated then, which combining the two radiology features composed the fusion model (Radiology MLP model), with AUCs of 0.871, 0.894, 0.918 in the training, test and validation sets. Compared with the two models aforementioned, the Radiology MLP model demonstrated a 33.4%-131.3% improvement in NRI and a 9.3%-50% improvement in IDI, showing better discrimination, calibration and clinical usefulness in three sets, which was selected as the optimal predictive model.
We mainly developed a fusion model (Radiology MLP model) that integrated radiology and radiomics features using MLP deep learning algorithm to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients, which yield an incremental value over the radiology and the MLP model.
本研究的主要目的是基于术前钆贝葡胺增强（Gd - BOPTA）磁共振成像（MRI）的临床放射学和影像组学特征，使用多层感知器（MLP）深度学习开发一种预测模型，以预测肝细胞癌（HCC）患者中包裹肿瘤簇的血管（VETC）。
从三家医院回顾性纳入230例经组织病理学确诊的HCC患者，这些患者在肝切除术前接受了术前Gd - BOPTA MRI检查（训练集、测试集和验证集分别为144例、54例和32例）。采用单因素和多因素逻辑回归分析来确定与VETC显著相关的独立临床放射学预测因素，进而构建临床放射学模型。感兴趣区域（ROI）包括四种模式，瘤内（肿瘤）、瘤周区域≤2mm（瘤周2mm）、瘤内+瘤周区域≤2mm（肿瘤+瘤周2mm）以及瘤内与瘤周整体≤2mm（肿瘤瘤周2mm）。分别为ROI（肿瘤）、ROI（瘤周2mm）、ROI（肿瘤瘤周2mm）提取了7322个影像组学特征，为ROI（肿瘤+瘤周2mm）提取了14644个影像组学特征。采用最小绝对收缩和选择算子（LASSO）和单因素逻辑回归分析来选择重要特征。七种不同的机器学习分类器分别将从四个ROI中选择的影像组学特征组合起来构成不同的模型，并在三个数据集中比较它们的性能，然后选择最佳组合成为我们需要的影像组学模型。然后生成一个影像组学评分（rad - score），通过多因素逻辑回归分析将显著的临床放射学预测因素组合起来构成融合模型。使用受试者操作特征曲线下面积（AUC）、综合判别指数（IDI）和净重新分类指数（NRI）比较这三个模型的性能，选择用于VETC预测的最佳预测模型。
动脉期瘤周强化和肝胆期（HBP）瘤周低信号是VETC的独立危险因素，并构成了放射学模型，不包含任何临床变量。动脉期瘤周强化定义为动脉晚期或门静脉期早期肿瘤边界外的强化，与肿瘤边缘广泛接触，在延迟期变为等信号。MLP深度学习算法整合从ROI肿瘤瘤周2mm中选择的影像组学特征是最佳组合，构成了影像组学模型（MLP模型）。然后计算了一个MLP评分（MLP_score），将这两个放射学特征组合起来构成融合模型（放射学MLP模型），在训练集、测试集和验证集中的AUC分别为0.871、0.894、0.918。与上述两个模型相比，放射学MLP模型在NRI上提高了33.4% - 131.3%，在IDI上提高了9.3% - 50%，在三个数据集中显示出更好的区分度、校准度和临床实用性，被选为最佳预测模型。
我们主要开发了一种融合模型（放射学MLP模型），该模型使用MLP深度学习算法整合放射学和影像组学特征，以预测肝细胞癌（HCC）患者中包裹肿瘤簇的血管（VETC），与放射学模型和MLP模型相比具有增量价值。
