2021
DOI: 10.1007/s00259-021-05242-1
|View full text |Cite
|
Sign up to set email alerts
|

Structural and functional radiomics for lung cancer

Abstract: Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to includ… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
62
0
1

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 83 publications
(63 citation statements)
references
References 120 publications
(123 reference statements)
0
62
0
1
Order By: Relevance
“…Much effort has been made by researchers to optimize the ability of radiomics to predict gene mutations. For example, in a study by Zhang et al on the prediction of EGFR mutations in lung cancer using radiomics, a complex model combining clinical information such as smoking and gender with a radiomics model resulted in better predictive performance than the radiomics model alone [22]. Although several studies concluded that non-smoking women are more likely to have EGFR mutations [38][39][40], there is still no direct correlation between this clinical information and gene mutation status.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Much effort has been made by researchers to optimize the ability of radiomics to predict gene mutations. For example, in a study by Zhang et al on the prediction of EGFR mutations in lung cancer using radiomics, a complex model combining clinical information such as smoking and gender with a radiomics model resulted in better predictive performance than the radiomics model alone [22]. Although several studies concluded that non-smoking women are more likely to have EGFR mutations [38][39][40], there is still no direct correlation between this clinical information and gene mutation status.…”
Section: Discussionmentioning
confidence: 99%
“…Tumor segmentation, feature extraction, and selection Following previously published methods [19,22,25,[32][33][34], all regions of interest (ROI) were de ned for PET and CT images by two experienced nuclear medicine physicians using MITK (Medical Imaging interaction Tool Kit v2018.04.2) software. CT images were delineated manually on the lung window (WL=-500HU, WW=1500HU), PET images were delineated by segmentation threshold and region-growing 3D segmentation and then manually adjusted.…”
Section: Patientsmentioning
confidence: 99%
See 1 more Smart Citation
“…Texture analysis is an emergent imaging analysis that quantifies radiological images and thereby can provide novel imaging biomarkers [ 1 , 2 , 3 ]. Every radiological modality is principally applicable for this analysis.…”
Section: Introductionmentioning
confidence: 99%
“…Every radiological modality is principally applicable for this analysis. Images derived from computed tomography (CT) are readily analyzed because of its robust imaging acquisition and high availability [ 1 , 2 , 3 ].…”
Section: Introductionmentioning
confidence: 99%