Cargando…

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors

OBJECTIVE: To investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis. MATERIALS AND METHODS: From 94 pathologically confir...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yang, Yin, Chen, Fei, Liang, Hongmei, Bai, Yun, Wang, Zhen, Zhao, Lei, Ma, Sai, Niu, Qinghua, Li, Fan, Xie, Tianwu, Cai, Yingyu
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Oncology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272725/ https://www.ncbi.nlm.nih.gov/pubmed/37333811 http://dx.doi.org/10.3389/fonc.2023.1166988

Ejemplares similares

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer
por: Jin, Juebin, et al.
Publicado: (2021)

CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy
por: Chen, Xinyuan, et al.
Publicado: (2020)

Exploring the Value of Radiomics Features Based on B-Mode and Contrast-Enhanced Ultrasound in Discriminating the Nature of Thyroid Nodules
por: Guo, Shi Yan, et al.
Publicado: (2021)

Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis
por: Gong, Xuantong, et al.
Publicado: (2023)

The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics
por: Jaudet, Cyril, et al.
Publicado: (2021)

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma
por: Zhang, Baihua, et al.
Publicado: (2021)

An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics
por: Xu, Chenan, et al.
Publicado: (2022)

Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics
por: Zhang, Hui, et al.
Publicado: (2022)

Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma
por: Teng, Xinzhi, et al.
Publicado: (2022)

Diagnostic value of radiomics model based on gray-scale and contrast-enhanced ultrasound for inflammatory mass stage periductal mastitis/duct ectasia
por: Zheng, Yan, et al.
Publicado: (2022)

Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
por: Zhang, Di, et al.
Publicado: (2021)

3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor
por: Li, Haimei, et al.
Publicado: (2021)

Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT
por: He, Kan, et al.
Publicado: (2021)

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis
por: Luo, Jieli, et al.
Publicado: (2023)

MRI Radiomics in Prostate Cancer: A Reliability Study
por: Urraro, Fabrizio, et al.
Publicado: (2021)

Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction
por: Müller-Franzes, Gustav, et al.
Publicado: (2022)

Ultrasound Radiomics Effective for Preoperative Identification of True and Pseudo Gallbladder Polyps Based on Spatial and Morphological Features
por: Yuan, Hai-xia, et al.
Publicado: (2020)

Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer
por: Lu, Wen-Jie, et al.
Publicado: (2023)

Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma
por: Zhou, Yue, et al.
Publicado: (2023)

Automatic Detection of Gastric Wall Structure Based on Oral Contrast-Enhanced Ultrasound and Its Application on Tumor Screening
por: Sui, An, et al.
Publicado: (2021)

Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading
por: Chen, Wen, et al.
Publicado: (2021)

Auto-Segmentation Ultrasound-Based Radiomics Technology to Stratify Patient With Diabetic Kidney Disease: A Multi-Center Retrospective Study
por: Chen, Jifan, et al.
Publicado: (2022)

Combination of clinical, radiomic, and “delta” radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma
por: Krishna, Satheesh, et al.
Publicado: (2023)

Reproducibility and Repeatability of CBCT-Derived Radiomics Features
por: Wang, Hao, et al.
Publicado: (2021)

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy
por: Li, Jing, et al.
Publicado: (2023)

A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma
por: Wang, Yating, et al.
Publicado: (2023)

Deep-learning-based automatic segmentation and classification for craniopharyngiomas
por: Yan, Xiaorong, et al.
Publicado: (2023)

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma
por: Wu, Jiangfeng, et al.
Publicado: (2022)

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis
por: Dai, Quan, et al.
Publicado: (2023)

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features
por: Wang, Jing, et al.
Publicado: (2023)

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma
por: Wang, Jifei, et al.
Publicado: (2022)

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features
por: Liu, Dongming, et al.
Publicado: (2022)

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images
por: Zhao, Chen, et al.
Publicado: (2021)

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics
por: Chen, Wei, et al.
Publicado: (2018)

Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video)
por: Jin, Jing, et al.
Publicado: (2022)

Radiomic analysis of contrast-enhanced ultrasound data
por: Theek, Benjamin, et al.
Publicado: (2018)

Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression
por: Peng, Tao, et al.
Publicado: (2022)

Automatic Detection and Segmentation for Group-Housed Pigs Based on PigMS R-CNN †
por: Tu, Shuqin, et al.
Publicado: (2021)

Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions
por: Wang, Simin, et al.
Publicado: (2021)

Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics
por: Li, Yunfei, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_81cbrp76th3qn6d5722lu49qq7