Deep Learning Radiopathomics for Predicting Tumor Vasculature and Prognosis in Hepatocellular Carcinoma.
作者信息
Mirza-Aghazadeh-Attari Mohammad
机构信息
Division of Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21205.
出版信息
Radiol Imaging Cancer. 2025 May;7(3):e250141. doi: 10.1148/rycan.250141.
Abstract
摘要
相似文献
1
Deep Learning Radiopathomics for Predicting Tumor Vasculature and Prognosis in Hepatocellular Carcinoma.用于预测肝细胞癌肿瘤血管生成和预后的深度学习放射组学
Radiol Imaging Cancer. 2025 May;7(3):e250141. doi: 10.1148/rycan.250141.
2
Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.基于对比增强磁共振成像和病理成像的深度学习放射组学模型预测肝细胞癌中包裹肿瘤团块的血管及预后
Radiol Imaging Cancer. 2025 Mar;7(2):e240213. doi: 10.1148/rycan.240213.
3
Deep learning Radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma.基于预处理磁共振成像和全切片图像的深度学习影像组学预测局部晚期鼻咽癌总生存期
Radiother Oncol. 2025 Aug;209:110949. doi: 10.1016/j.radonc.2025.110949. Epub 2025 May 21.
4
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.整合放射组学模型预测非转移性鼻咽癌患者的无进展生存期。
J Cancer Res Clin Oncol. 2024 Sep 9;150(9):415. doi: 10.1007/s00432-024-05930-z.
5
Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature.通过联合MRI影像组学和病理组学特征预测肝细胞癌患者的总生存期
Transl Oncol. 2025 Jan;51:102174. doi: 10.1016/j.tranon.2024.102174. Epub 2024 Nov 2.
6
Multimodal radiopathological integration for prognosis and prediction of adjuvant chemotherapy benefit in resectable lung adenocarcinoma: A multicentre study.多模态放射病理学整合用于可切除肺腺癌的预后及辅助化疗获益预测：一项多中心研究
Cancer Lett. 2025 Apr 28;616:217557. doi: 10.1016/j.canlet.2025.217557. Epub 2025 Feb 13.
7
A radiopathomics model for predicting large-number cervical lymph node metastasis in clinical N0 papillary thyroid carcinoma.一种用于预测临床N0期乳头状甲状腺癌大量颈部淋巴结转移的放射组学模型。
Eur Radiol. 2025 Jan 29. doi: 10.1007/s00330-025-11377-8.
8
Machine Learning Model for Risk Stratification of Papillary Thyroid Carcinoma Based on Radiopathomics.基于放射组学的甲状腺乳头状癌风险分层机器学习模型
Acad Radiol. 2025 May;32(5):2545-2553. doi: 10.1016/j.acra.2024.12.062. Epub 2025 Jan 26.
9
A novel deep learning radiopathomics model for predicting carcinogenesis promotor cyclooxygenase-2 expression in common bile duct in children with pancreaticobiliary maljunction: a multicenter study.一种用于预测胰胆管合流异常患儿胆总管中致癌促进因子环氧化酶-2表达的新型深度学习放射组学模型：一项多中心研究。
Insights Imaging. 2025 Mar 27;16(1):74. doi: 10.1186/s13244-025-01951-5.
10
An Integrated Radiopathomics Machine Learning Model to Predict Pathological Response to Preoperative Chemotherapy in Gastric Cancer.一种用于预测胃癌术前化疗病理反应的综合放射组学机器学习模型。
Acad Radiol. 2025 Jan;32(1):134-145. doi: 10.1016/j.acra.2024.08.014. Epub 2024 Aug 30.
本文引用的文献
1
Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.基于对比增强磁共振成像和病理成像的深度学习放射组学模型预测肝细胞癌中包裹肿瘤团块的血管及预后
Radiol Imaging Cancer. 2025 Mar;7(2):e240213. doi: 10.1148/rycan.240213.
2
Radiomics analysis of gadoxetic acid-enhanced MRI for evaluating vessels encapsulating tumour clusters in hepatocellular carcinoma.钆塞酸二钠增强MRI的影像组学分析用于评估肝细胞癌中包裹肿瘤结节的血管情况
Front Oncol. 2024 Aug 13;14:1422119. doi: 10.3389/fonc.2024.1422119. eCollection 2024.
3
Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging.基于增强磁共振成像的≤3cm小肝细胞癌中肿瘤簇包绕血管的列线图预测
World J Gastrointest Oncol. 2024 May 15;16(5):1808-1820. doi: 10.4251/wjgo.v16.i5.1808.
4
Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment.术前钆塞酸增强 MRI 特征评估肝癌肿瘤簇包绕血管和微血管侵犯：创建风险评估列线图。
J Magn Reson Imaging. 2024 Sep;60(3):1094-1110. doi: 10.1002/jmri.29187. Epub 2023 Dec 20.
5
Editorial for "Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma".《用于评估肝细胞癌中包裹肿瘤团块的血管及预后的动态对比增强磁共振成像深度学习放射组学模型》社论
J Magn Reson Imaging. 2024 Jan;59(1):120-121. doi: 10.1002/jmri.28775. Epub 2023 May 11.
6
Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma.用于表征肝细胞癌中包裹肿瘤结节的血管及微血管侵犯的术前MRI特征
Abdom Radiol (NY). 2023 Feb;48(2):554-566. doi: 10.1007/s00261-022-03740-w. Epub 2022 Nov 16.
7
ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data.ContransGAN：基于卷积神经网络与全局 Swin-Transformer 网络耦合的无配对数据高分辨率定量相位成像方法
Cells. 2022 Aug 3;11(15):2394. doi: 10.3390/cells11152394.
8
Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma.钆塞酸二钠增强磁共振成像的影像组学用于预测肝细胞癌中肿瘤巢包绕血管（VETC）及患者预后
Eur Radiol. 2022 Feb;32(2):959-970. doi: 10.1007/s00330-021-08250-9. Epub 2021 Sep 4.
9
Vessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma.血管包裹肿瘤簇（VETC）是预测侵袭性肝细胞癌的有力指标。
Hepatology. 2020 Jan;71(1):183-195. doi: 10.1002/hep.30814. Epub 2019 Aug 9.
Zotero 插件