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本文引用的文献

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A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.一种使用深度学习进行多器官分割的新型放射组学方法，用于预测肝细胞癌患者 TACE 治疗的疗效。
Sci Rep. 2024 Jun 26;14(1):14779. doi: 10.1038/s41598-024-65630-z.
2
TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.全段分割器：CT图像中104种解剖结构的稳健分割
Radiol Artif Intell. 2023 Jul 5;5(5):e230024. doi: 10.1148/ryai.230024. eCollection 2023 Sep.
3
Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation.多模态注释的肝细胞癌数据集，包括 TACE 前后的影像分割。
Sci Data. 2023 Jan 18;10(1):33. doi: 10.1038/s41597-023-01928-3.
4
CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients.CT 放射组学和临床风险评分用于 TACE HCC 患者的反应和总生存预后预测。
Sci Rep. 2023 Jan 11;13(1):533. doi: 10.1038/s41598-023-27714-0.
5
Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging.人工智能：肝细胞癌成像当前应用综述
Diagn Interv Imaging. 2023 Jan;104(1):24-36. doi: 10.1016/j.diii.2022.10.001. Epub 2022 Oct 19.
6
Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.人工智能在肝细胞癌的预防和临床管理中的应用。
J Hepatol. 2022 Jun;76(6):1348-1361. doi: 10.1016/j.jhep.2022.01.014.
7
Predicting cancer outcomes with radiomics and artificial intelligence in radiology.利用放射组学和人工智能技术预测癌症预后。
Nat Rev Clin Oncol. 2022 Feb;19(2):132-146. doi: 10.1038/s41571-021-00560-7. Epub 2021 Oct 18.
8
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.nnU-Net：一种基于深度学习的生物医学图像分割的自配置方法。
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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.影像生物标志物标准化倡议：高通量基于影像表型的标准化定量放射组学。
Radiology. 2020 May;295(2):328-338. doi: 10.1148/radiol.2020191145. Epub 2020 Mar 10.
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A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.一种预测肝细胞癌对经动脉化疗栓塞反应的机器学习模型。
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WAW-TACE：一个包含分割、影像组学特征和临床数据的肝细胞癌多期CT数据集。

WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.

作者信息

Bartnik Krzysztof, Bartczak Tomasz, Krzyziński Mateusz, Korzeniowski Krzysztof, Lamparski Krzysztof, Węgrzyn Piotr, Lam Eric, Bartkowiak Mateusz, Wróblewski Tadeusz, Mech Katarzyna, Januszewicz Magdalena, Biecek Przemysław

机构信息

From the Second Department of Radiology (K.B., K.K., K.L., P.W., M.J.), Department of General, Transplant and Liver Surgery (M.B., T.W.), and Department of General, Gastroenterological and Oncological Surgery (K.M.), Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland (T.B., M.K., P.B.); and Ottawa Hospital Research Institute, Ottawa, Canada (E.L.).

出版信息

Radiol Artif Intell. 2024 Nov;6(6):e240296. doi: 10.1148/ryai.240296.

DOI:10.1148/ryai.240296
PMID:39441110
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11605144/
Abstract

The WAW-TACE dataset contains baseline multiphase abdominal CT images from 233 treatment-naive patients with hepatocellular carcinoma treated with transarterial chemoembolization and includes 377 handcrafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data. The dataset can be accessed at .

摘要

WAW-TACE数据集包含233例未经治疗的肝细胞癌患者经动脉化疗栓塞治疗前的腹部多期CT图像，包括377个手工绘制的肝肿瘤掩码、多个内部器官的自动分割、提取的影像组学特征以及相应的广泛临床数据。该数据集可在……获取。