CT-Image analyzing using MC simulations to diagnose lung cancer at early stages

2016 ◽  
Keyword(s):  
Lung Cancer ◽  
Ct Image ◽  
Early Stages ◽  
Diagnose Lung Cancer

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients

2015 ◽  
Vol 54 (06) ◽  
pp. 247-254 ◽  
Author(s):  
A. Kapfhammer ◽  
T. Winkens ◽  
T. Lesser ◽  
A. Reissig ◽  
M. Steinert ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Chest Wall ◽  
Fdg Pet ◽  
Ct Image ◽  
Lung Cancers ◽  
Pet Ct ◽  
Peripheral Lung ◽  
Fdg Pet Ct ◽  
Tumour Infiltration

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Heterogeneity in Meta-analysis of FDG-PET Studies to Diagnose Lung Cancer

JAMA ◽  
2015 ◽  
Vol 313 (4) ◽  
pp. 419 ◽  
Author(s):  
Edward J. Mills ◽  
Jeroen P. Jansen ◽  
Steve Kanters
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Meta Analysis ◽  
Diagnose Lung Cancer

scholarly journals Non-small cell lung cancer: early stages

2006 ◽  
Vol 17 ◽  
pp. ii17-ii21
Author(s):  
E. Bria ◽  
A. Ceribelli ◽  
M.G. Trovò ◽  
A. Gelibter ◽  
M. Gigante ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Early Stages

scholarly journals A Comparative Study of Auto-contouring Softwares in Delineation of Organs at Risk in Lung Cancer and Rectal Cancer

2021 ◽  
Author(s):  
weijun chen ◽  
Cheng Wang ◽  
Wenming Zhan ◽  
Yongshi Jia ◽  
Fangfang Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
Rectal Cancer ◽  
Organs At Risk ◽  
Similarity Index ◽  
Slight Modification ◽  
Ct Images ◽  
Target Area ◽  
Ct Image ◽  
Significant Difference

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

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