Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
about
Radiogenomic Analysis of Oncological Data: A Technical Survey.Radiomics of pulmonary nodules and lung cancerPrediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners.Development and clinical application of radiomics in lung cancer.Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer.Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.Predicting survival time of lung cancer patients using radiomic analysis.Radiomics in precision medicine for lung cancer.Towards precision medicine: from quantitative imaging to radiomics.Clinical applications of textural analysis in non-small cell lung cancer.Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma.Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation.Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status.Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response.Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers.Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancerData Analysis Strategies in Medical ImagingIdentifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings
P2860
Q33624662-ADD8079E-7BB8-403C-B433-C960DBF5BA03Q37691872-80439CD9-556E-4282-9F53-F0D71BF3CBD0Q38748715-259A78FB-22FD-4A9A-8563-E914790FD961Q41717198-54CEC920-3394-4CF9-9289-D97FAF0940F0Q45946420-9033D4FE-1EFC-4E7A-95ED-94AA8CC5598AQ47111617-3476B887-E978-4F62-871C-C64F9C1CE9B4Q47120003-485A7162-35FE-48E9-8930-A78A653377DFQ47155121-D9545EB2-96F5-4F0C-8C23-7A8573B095F0Q47195161-70281625-6985-4626-95A2-13B2D2D25A76Q47966288-DD0AA228-0AE5-463C-BC00-0C384EEE3DA1Q48033863-25006033-E42F-4D12-9C1A-8F83C036CBADQ50041531-1A333E7B-7504-4E4F-B582-0DF2EC6EA046Q52446289-A8A373B9-AF1C-490D-A42F-074E24423AC5Q52629217-DDF6ABFB-A220-4976-9903-219E99CB89ABQ53073424-68EDCB48-18E7-40DE-90BF-EADC869EA606Q53808415-3DD21B51-C712-4E6B-B7B4-08DBCB94301DQ55058049-981B730A-6DA1-4669-B90E-F2308389A0C7Q57172944-B402DD21-1B74-48FB-9533-2BBE5498E7AFQ57663144-DD3D148B-C105-4E96-B85B-97875FDAD2BEQ58782540-92D82539-8028-4E3C-A5AB-5E2DFD6715C4
P2860
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@ast
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@en
type
label
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@ast
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@en
prefLabel
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@ast
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@en
P2093
P2860
P50
P356
P1476
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
@en
P2093
Hugo J W L Aerts
Patrick Grossmann
Philippe Lambin
Raymond Mak
Weimiao Wu
P2860
P356
10.3389/FONC.2016.00071
P577
2016-03-30T00:00:00Z