Cargando…

CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study

We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28)...

Descripción completa

Detalles Bibliográficos
Autores principales:	Li, Yajun, Lu, Lin, Xiao, Manjun, Dercle, Laurent, Huang, Yue, Zhang, Zishu, Schwartz, Lawrence H., Li, Daiqiang, Zhao, Binsheng
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2018
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297245/ https://www.ncbi.nlm.nih.gov/pubmed/30559455 http://dx.doi.org/10.1038/s41598-018-36421-0

Ejemplares similares

Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies
por: Yoon, Jin H., et al.
Publicado: (2021)

Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule
por: He, Lan, et al.
Publicado: (2016)

Toward radiomics for assessment of response to systemic therapies in lung cancer
por: Sun, Shawn, et al.
Publicado: (2020)

Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
por: Lu, Lin, et al.
Publicado: (2021)

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy
por: Dercle, Laurent, et al.
Publicado: (2022)

Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle
por: Escudero Sanchez, Lorena, et al.
Publicado: (2021)

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings
por: Lu, Lin, et al.
Publicado: (2016)

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer
por: Park, Sohee, et al.
Publicado: (2019)

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma
por: Wang, Yang, et al.
Publicado: (2022)

Radiomics Prediction of EGFR Status in Lung Cancer—Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data
por: Lu, Lin, et al.
Publicado: (2020)

Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom
por: Lu, Lin, et al.
Publicado: (2019)

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes
por: Zhao, Wei, et al.
Publicado: (2019)

Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours
por: Tan, Yongqiang, et al.
Publicado: (2012)

Reproducibility of radiomics for deciphering tumor phenotype with imaging
por: Zhao, Binsheng, et al.
Publicado: (2016)

Optimal slice thickness for cone-beam CT with on-board imager
por: Seet, KYT, et al.
Publicado: (2010)

Evaluation of the linear interpolation method in correcting the influence of slice thicknesses on radiomic feature values in solid pulmonary nodules: a prospective patient study
por: Yang, Shouxin, et al.
Publicado: (2021)

Understanding Sources of Variation to Improve the Reproducibility of Radiomics
por: Zhao, Binsheng
Publicado: (2021)

CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features
por: Refaee, Turkey, et al.
Publicado: (2022)

LKC-Net: large kernel convolution object detection network
por: Wang, Weina, et al.
Publicado: (2023)

Impact of different CBCT imaging monitor units, reconstruction slice thicknesses, and planning CT slice thicknesses on the positioning accuracy of a MV‐CBCT system in head‐and‐neck patients
por: Jia, Ming X., et al.
Publicado: (2012)

The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
por: Ibrahim, Abdalla, et al.
Publicado: (2022)

Assessing immune response upon systemic RNA vaccination on [18F]-FDG PET/CT for COVID-19 vaccine and then for immuno-oncology?
por: Seban, Romain-David, et al.
Publicado: (2021)

PCA Based Kernel Initialization for Convolutional Neural Networks
por: Wang, Yifeng, et al.
Publicado: (2020)

High Rate of False Negative Diagnosis of Silent Patent Ductus Arteriosus on the Chest CT with 3 mm Slice-Thickness, Suggesting the Need for Analysis with Thinner Slice Thickness
por: Lee, Dongjun, et al.
Publicado: (2021)

Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions
por: Lu, Lin, et al.
Publicado: (2021)

A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution
por: Yu, Hongxia, et al.
Publicado: (2023)

Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
por: Aerts, Hugo J. W. L., et al.
Publicado: (2016)

Image quality of mixed convolution kernel in thoracic computed tomography
por: Neubauer, Jakob, et al.
Publicado: (2016)

Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy
por: McGale, Jeremy, et al.
Publicado: (2023)

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso‐center, and reconstruction filter
por: Lasiyah, Nani, et al.
Publicado: (2021)

Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture
por: Lee, Kanggeun, et al.
Publicado: (2022)

Corrigendum: Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
por: Aerts, Hugo J. W. L., et al.
Publicado: (2017)

Impact of CT slice thickness on volume and dose evaluation during thoracic cancer radiotherapy
por: Luo, Huanli, et al.
Publicado: (2018)

Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms
por: Huang, Kai, et al.
Publicado: (2021)

Imaging-guided precision medicine in glioblastoma patients treated with immune checkpoint modulators: research trend and future directions in the field of imaging biomarkers and artificial intelligence
por: Sinigaglia, Mathieu, et al.
Publicado: (2019)

The Effect of Slice Thickness on Contours of Brain Metastases for Stereotactic Radiosurgery
por: Thrower, Sara L., et al.
Publicado: (2021)

Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel – A Prospective Cross-Sectional Study with 256-Slice CT
por: Tan, Stéphanie, et al.
Publicado: (2016)

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?
por: Digumarthy, Subba R., et al.
Publicado: (2019)

Optoacoustic inversion via convolution kernel reconstruction in the paraxial approximation and beyond
por: Melchert, O., et al.
Publicado: (2018)

Recurrent convolutional neural kernel model for stock price movement prediction
por: Liu, Suhui, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_rinoo4hmkgd0urn5opek9a89ai