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Deep Fusion Models of Multi-phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

Weibin Wang, Qingqing Chen, Yutaro Iwamoto, Panyanat Aonpong, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yen-Wei Chen
2020 IEEE Access  
INDEX TERMS Hepatocellular carcinoma, early recurrence, deep learning, multi-phase CT images, clinical data, fusion model.  ...  In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical  ...  ACKNOWLEDGMENT (Weibin Wang and Qingqing Chen are co-first authors.)  ... 
doi:10.1109/access.2020.3011145 fatcat:vvw4rpo5ljagze4iyczvth6eda

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Anna Castaldo, Davide Raffaele De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, Renato Cuocolo
2021 Diagnostics  
While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy.  ...  Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes.  ...  A multi-institutional study [66] tested a radiomics model based on CECT analysis to predict recurrence of HCC after liver resection.  ... 
doi:10.3390/diagnostics11071194 fatcat:zmwt7urwinac7bshxcxm6q7doe

Radiomics for liver tumours

Constantin Dreher, Philipp Linde, Judit Boda-Heggemann, Bettina Baessler
2020 Strahlentherapie und Onkologie (Print)  
The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome  ...  This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice.  ...  [66] To predict recurrence of HCC (hepatocellular carcinoma) after curative treatment CECT 156 T: 109 V: 47 A radiomics model effectively pre- dicts early recurrence (ER) of HCC and is more  ... 
doi:10.1007/s00066-020-01615-x pmid:32296901 pmcid:PMC7498486 fatcat:urkoadbedrhqnj5tv4yhxfyyyq

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study

Gu-Wei Ji, Fei-Peng Zhu, Qing Xu, Ke Wang, Ming-Yu Wu, Wei-Wei Tang, Xiang-Cheng Li, Xue-Hao Wang
2019 EBioMedicine  
We explored the potential of radiomics coupled with machine-learning algorithms to improve the predictive accuracy for HCC recurrence.  ...  Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is  ...  Declaration of Competing Interest The authors declare no potential conflicts of interest.  ... 
doi:10.1016/j.ebiom.2019.10.057 pmid:31735556 pmcid:PMC6923482 fatcat:4vutbmk3wzd37bpkdrq53rdote

Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Zhi-Min Zou, De-Hua Chang, Hui Liu, Yu-Dong Xiao
2021 Insights into Imaging  
With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC  ...  By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence  ...  [79] used a CNN-based segmentation protocol to extract a large number of shape and texture features from portal venous phase CT images.  ... 
doi:10.1186/s13244-021-00977-9 pmid:33675433 fatcat:kzm3plvuejhdddon32beoh2tlq

Radiomics and Deep Learning: Hepatic Applications

Hyo Jung Park, Bumwoo Park, Seung Soo Lee
2020 Korean Journal of Radiology  
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases.  ...  In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.  ...  This suggested the potential of deep learning-based image reconstruction combined with data under-sampling for fast MRI.  ... 
doi:10.3348/kjr.2019.0752 pmid:32193887 pmcid:PMC7082656 fatcat:kncq2om26rg5hf4quusf6z5wk4

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Zhenyu Liu, Shuo Wang, Di Dong, Jingwei Wei, Cheng Fang, Xuezhi Zhou, Kai Sun, Longfei Li, Bo Li, Meiyun Wang, Jie Tian
2019 Theranostics  
Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events  ...  Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology  ...  Shen et al. proposed a deep learning model based on CT images and achieved better prediction results for malignant lung nodule compared with previous methods [133] .  ... 
doi:10.7150/thno.30309 pmid:30867832 pmcid:PMC6401507 fatcat:zlupwckeajdfzgknpqexuuscce

Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning

Qiu-Ping Liu, Xun Xu, Fei-Peng Zhu, Yu-Dong Zhang, Xi-Sheng Liu
2020 EClinicalMedicine  
Meanwhile, we developed a deep learning (DL)-score for disease-specific survival by training CT imaging using DL networks in a cohort of 243 HCCs with TACE.  ...  Our study offers a DL-based, noninvasive imaging hallmark to predict outcome of HCCs with TACE.  ...  We attempted to build two new imaging hallmarks based on histologic-imaging correlation, using machine learning of volumetric CT radiomics features for predicting MVI and Edmondson-Steiner grade of HCC  ... 
doi:10.1016/j.eclinm.2020.100379 pmid:32548574 pmcid:PMC7284069 fatcat:woxcwy44qjbrxgnypeo7uz4pey

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review

Michal Kawka, Aleksander Dawidziuk, Long R. Jiao, Tamara M. H. Gall
2021 Translational Gastroenterology and Hepatology  
These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice.  ...  Hepatocellular carcinoma (HCC) is a significant cause of morbidity and mortality worldwide. Despite significant advancements in detection and treatment of HCC, its management remains a challenge.  ...  Wang and colleagues employed multi-phase CT radiomics features together with clinical models to yield a combined model with AUC of 0.82.  ... 
doi:10.21037/tgh-20-242 fatcat:csuy7bzr3nelxduakk4znm4cd4

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03,  ...  The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  10575 3Q Temporal assessment of radiomic features on clinical mammography in a high-risk population [10575-134] 10575 3R Deep radiomic prediction with clinical predictors of the survival in patients with  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research  ...  Herein, radiomics and deep learning-based radiomics were reviewed, focusing on the types of characteristics, approaches for extraction and selection, statistical analysis, predictive models, and depth  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study

Jingwei Wei, Hanyu Jiang, Mengsu Zeng, Meiyun Wang, Meng Niu, Dongsheng Gu, Huanhuan Chong, Yanyan Zhang, Fangfang Fu, Mu Zhou, Jie Chen, Fudong Lyv (+5 others)
2021 Cancers  
In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities—contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced  ...  Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management.  ...  Data Availability Statement: The dataset is available for sharing upon request from Jie Tian. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers13102368 pmid:34068972 fatcat:hwxuckjiwbdu5nzu5ouuhq3oxy

Radiogenomics: bridging imaging and genomics

Zuhir Bodalal, Stefano Trebeschi, Thi Dan Linh Nguyen-Kim, Winnie Schats, Regina Beets-Tan
2019 Abdominal Radiology  
From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed.  ...  The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed.  ...  Deep learning radiomics Newer deep learning radiomic workflows (Fig. 1b ) can now process the image, automatically extract features, and perform classification without the need for a detailed delineation  ... 
doi:10.1007/s00261-019-02028-w pmid:31049614 fatcat:34rbtjd6ljh3rm2svqhv2hjqoe

Advances in liver US, CT, and MRI: moving toward the future

Federica Vernuccio, Roberto Cannella, Tommaso Vincenzo Bartolotta, Massimo Galia, An Tang, Giuseppe Brancatelli
2021 European Radiology Experimental  
Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing  ...  This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years  ...  Marco Dioguardi Burgio, and Dr. Jean-Charles Bijot for their support in selecting Figs. 3, 5, 6, 7 , and 9 for this review.  ... 
doi:10.1186/s41747-021-00250-0 pmid:34873633 pmcid:PMC8648935 fatcat:5to5wgkys5cfviz73nyr7r5gee

Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study

Xiaofeng Chen, Zhiqi Yang, Jiada Yang, Yuting Liao, Peipei Pang, Weixiong Fan, Xiangguang Chen
2020 Cancer Imaging  
Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images.  ...  To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients.  ...  ; GC: Gastric cancer; Radscore: Radiomics scores; A: The predicted model based on arterial phase images; V: The predicted model based on venous phase images; A + V: The predicted model based on a the combination  ... 
doi:10.1186/s40644-020-00302-5 pmid:32248822 pmcid:PMC7132895 fatcat:tgygzgm7bre67dc4f7esfb77r4
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