Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis

2014 ◽  
Author(s):  
Maxine Tan ◽  
Nastaran Emaminejad ◽  
Wei Qian ◽  
Shenshen Sun ◽  
Yan Kang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Expression ◽  
Image Features ◽  
Cancer Prognosis ◽  
Ct Image

Exploring new quantitative CT image features to improve assessment of lung cancer prognosis

2015 ◽  
Author(s):  
Nastaran Emaminejad ◽  
Wei Qian ◽  
Yan Kang ◽  
Yubao Guan ◽  
Fleming Lure ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Cancer Prognosis ◽  
Ct Image ◽  
Quantitative Ct

scholarly journals Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

2016 ◽  
Vol 43 (8) ◽  
pp. 1477-1485 ◽  
Author(s):  
Marie-Charlotte Desseroit ◽  
Dimitris Visvikis ◽  
Florent Tixier ◽  
Mohamed Majdoub ◽  
Rémy Perdrisot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Clinical Staging ◽  
Cancer Stage ◽  
Ct Image ◽  
Small Cell Lung ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 1418-1426 ◽  
Author(s):  
Samuel H. Hawkins ◽  
John N. Korecki ◽  
Yoganand Balagurunathan ◽  
Yuhua Gu ◽  
Virendra Kumar ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lung Cancer ◽  
Nonsmall Cell Lung Cancer ◽  
Image Features ◽  
Ct Image

SU-DD-A4-04: Predicting Tumor Local Control in Lung Cancer From Pre-Treatment PET/CT Image Features

2009 ◽  
Vol 36 (6Part2) ◽  
pp. 2426-2427
Author(s):  
M Vaidya ◽  
J Bradley ◽  
A Apte ◽  
D Yang ◽  
I El Naqa
Keyword(s):  
Lung Cancer ◽  
Local Control ◽  
Image Features ◽  
Ct Image ◽  
Pet Ct ◽  
Pre Treatment

scholarly journals Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 400-404
Author(s):  
Weipeng Zhang
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Image Features ◽  
Ct Image ◽  
Lung Cancers ◽  
Diagnosis Rate ◽  
Malignant Lesions ◽  
The Relationship

Abstract Background The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. Methods This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. Results the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. Conclusions the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

Computer-aided CT image features improving the malignant risk prediction in pulmonary nodules suspicious for lung cancer

2019 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Masahiko Kusumoto ◽  
Hironobu Ohmatsu ◽  
Keiju Aokage ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Prediction ◽  
Pulmonary Nodules ◽  
Image Features ◽  
Ct Image ◽  
Computer Aided

Study on Prognosis Factors of Non-Small Cell Lung Cancer Based on CT Image Features

2019 ◽  
Vol 9 (6) ◽  
pp. 1131-1137
Author(s):  
Xiaoteng Lu ◽  
Jing Gong ◽  
Shengdong Nie
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Ct Image ◽  
Small Cell Lung ◽  
Prognosis Factors ◽  
Second Moment

This study aims to investigate the prognosis factors of non-small cell lung cancer (NSCLC) based on CT image features and develop a new quantitative image feature prognosis approach using CT images. Firstly, lung tumors were segmented and images features were extracted. Secondly, the Kaplan-Meier method was used to have a univariate survival analysis. A multiple survival analysis was carried out with the method of COX regression model. Thirdly, SMOTE algorithm was took to make the feature data balanced. Finally, classifiers based on WEKA were established to test the prognosis ability of independent prognosis factors. Univariate analysis results reflected that six features had significant influence on patients' prognosis. After multivariate analysis, angular second moment, srhge and volume were significantly related to the survival situation of NSCLC patients (P < 0.05). According to the results of classifiers, these three features could make a well prognosis on the NSCLC. The best classification accuracy was 78.4%. The results of our study suggested that angular second moment, srhge and volume were high potential independent prognosis factors of NSCLC.

scholarly journals Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Kun-Hsing Yu ◽  
Ce Zhang ◽  
Gerald J. Berry ◽  
Russ B. Altman ◽  
Christopher Ré ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Cancer Prognosis ◽  
Small Cell Lung ◽  
Microscopic Pathology ◽  
Pathology Image

scholarly journals Erratum to: Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

2016 ◽  
Vol 43 (10) ◽  
pp. 1933-1933 ◽  
Author(s):  
Marie-Charlotte Desseroit ◽  
Dimitris Visvikis ◽  
Florent Tixier ◽  
Mohamed Majdoub ◽  
Rémy Perdrisot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Clinical Staging ◽  
Cancer Stage ◽  
Ct Image ◽  
Small Cell Lung ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

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.

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