Cargando…

Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy

OBJECTIVES: To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC). METHODS: We collected 292 AGC patients who received neoa...

Descripción completa

Detalles Bibliográficos
Autores principales:	Xu, Qinmei, Sun, Zeyu, Li, Xiuli, Ye, Chen, Zhou, Changsheng, Zhang, Longjiang, Lu, Guangming
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2021
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523390/ https://www.ncbi.nlm.nih.gov/pubmed/33909133 http://dx.doi.org/10.1007/s00330-021-07962-2

Ejemplares similares

Radiomics analysis based on lumbar spine CT to detect osteoporosis
por: Jiang, Yan-Wei, et al.
Publicado: (2022)

[(18)F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas
por: Noortman, Wyanne A., et al.
Publicado: (2022)

A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer
por: Xie, Yunming, et al.
Publicado: (2021)

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia
por: Li, Liang, et al.
Publicado: (2021)

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy
por: Chen, Xiaojun, et al.
Publicado: (2022)

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage
por: Pszczolkowski, Stefan, et al.
Publicado: (2021)

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images
por: Cai, Shengping, et al.
Publicado: (2022)

Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey
por: Volpe, Stefania, et al.
Publicado: (2023)

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept
por: Skarping, I., et al.
Publicado: (2021)

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features
por: Fujita, Shohei, et al.
Publicado: (2022)

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative
por: Spadarella, Gaia, et al.
Publicado: (2022)

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study
por: Eifer, Michal, et al.
Publicado: (2022)

Prediction of incident cardiovascular events using machine learning and CMR radiomics
por: Pujadas, Esmeralda Ruiz, et al.
Publicado: (2022)

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis
por: Xue, Li-Yun, et al.
Publicado: (2020)

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility
por: Schurink, Niels W., et al.
Publicado: (2021)

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
por: Fornacon-Wood, Isabella, et al.
Publicado: (2020)

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2
por: Fang, Xu, et al.
Publicado: (2020)

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning
por: Lin, Yu-Chun, et al.
Publicado: (2023)

Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis
por: Ursprung, Stephan, et al.
Publicado: (2020)

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer
por: Bleker, Jeroen, et al.
Publicado: (2019)

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
por: Hou, Min, et al.
Publicado: (2022)

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
por: Fournier, Laure, et al.
Publicado: (2021)

Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI
por: Zhang, Yang, et al.
Publicado: (2023)

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study
por: Meng, Fanyang, et al.
Publicado: (2022)

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm
por: Schuessler, Maximilian, et al.
Publicado: (2022)

Automated quantification of COVID-19 severity and progression using chest CT images
por: Pu, Jiantao, et al.
Publicado: (2020)

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps
por: Wesp, Philipp, et al.
Publicado: (2022)

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
por: Wang, Shuai, et al.
Publicado: (2021)

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?
por: Jungmann, Florian, et al.
Publicado: (2021)

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT
por: Nowak, Sebastian, et al.
Publicado: (2021)

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients
por: Gang, Yadong, et al.
Publicado: (2021)

Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA
por: Löffler, Maximilian T., et al.
Publicado: (2021)

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
por: Schmitt, N., et al.
Publicado: (2021)

Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma
por: Frood, Russell, et al.
Publicado: (2022)

Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation
por: Weikert, T., et al.
Publicado: (2023)

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks
por: Koitka, Sven, et al.
Publicado: (2020)

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19
por: Feng, Zhichao, et al.
Publicado: (2021)

Deep learning assisted contrast-enhanced CT–based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases
por: Xu, Xiaoshuai, et al.
Publicado: (2022)

An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma
por: Yang, Huancheng, et al.
Publicado: (2023)

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study
por: Xie, Qiuchen, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_ub88g8mi2v6ni64h8b9paaq0q6