scholarly journals Development of CT texture analysis in COPD and association with visual scoring and DLCO

2021 ◽  
Author(s):  
V. Lui ◽  
W. C. Tan ◽  
J. C. Hogg ◽  
H. O. Coxson ◽  
M. Kirby
Keyword(s):  
Lung Function ◽  
Healthy Volunteers ◽  
Texture Analysis ◽  
Texture Features ◽  
Visual Assessment ◽  
Quantitative Ct ◽  
Visual Scoring ◽  
Ct Texture Analysis

Objectives: • To determine if CT texture features, such as GLCM and FD, can differentiate patients with COPD from healthy volunteers, and are related to lung function • To determine if CT texture features are association with qualitative visual scoring • To determine if CT texture features are significantly associated with COPD outcomes, independent of qualitative scoring and standard quantitative CT emphysema measurements Hypothesis: • CT texture features can be developed to objectively aid in quantifying the severity of emphysema, and may provide information complementary to qualitative visual assessment

scholarly journals Development of CT texture analysis in COPD and association with visual scoring and DLCO

2021 ◽  
Author(s):  
V. Lui ◽  
W. C. Tan ◽  
J. C. Hogg ◽  
H. O. Coxson ◽  
M. Kirby
Keyword(s):  
Lung Function ◽  
Healthy Volunteers ◽  
Texture Analysis ◽  
Texture Features ◽  
Visual Assessment ◽  
Quantitative Ct ◽  
Visual Scoring ◽  
Ct Texture Analysis

Objectives: • To determine if CT texture features, such as GLCM and FD, can differentiate patients with COPD from healthy volunteers, and are related to lung function • To determine if CT texture features are association with qualitative visual scoring • To determine if CT texture features are significantly associated with COPD outcomes, independent of qualitative scoring and standard quantitative CT emphysema measurements Hypothesis: • CT texture features can be developed to objectively aid in quantifying the severity of emphysema, and may provide information complementary to qualitative visual assessment

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Radiology ◽  
2015 ◽  
Vol 276 (3) ◽  
pp. 787-796 ◽  
Author(s):  
Taryn Hodgdon ◽  
Matthew D. F. McInnes ◽  
Nicola Schieda ◽  
Trevor A. Flood ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Ct Images ◽  
Renal Angiomyolipoma ◽  
Quantitative Ct ◽  
Unenhanced Ct ◽  
Ct Texture Analysis

scholarly journals Quantitative CT texture analysis for diagnosing systemic sclerosis

Medicine ◽  
2019 ◽  
Vol 98 (29) ◽  
pp. e16423 ◽  
Author(s):  
Gianluca Milanese ◽  
Manoj Mannil ◽  
Katharina Martini ◽  
Britta Maurer ◽  
Hatem Alkadhi ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Texture Analysis ◽  
Quantitative Ct ◽  
Ct Texture Analysis

Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis

2019 ◽  
Vol 61 (5) ◽  
pp. 595-604 ◽  
Author(s):  
Zhonglan Wang ◽  
Xiao Chen ◽  
Jianhua Wang ◽  
Wenjing Cui ◽  
Shuai Ren ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Texture Analysis ◽  
Neuroendocrine Tumors ◽  
Texture Features ◽  
Ct Images ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Neuroendocrine Tumors ◽  
Portal Phase ◽  
Contrast Enhanced ◽  
Ct Texture Analysis

Background Hypovascular pancreatic neuroendocrine tumor is usually misdiagnosed as pancreatic ductal adenocarcinoma. Purpose To investigate the value of texture analysis in differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma on contrast-enhanced computed tomography (CT) images. Material and Methods Twenty-one patients with hypovascular pancreatic neuroendocrine tumors and 63 patients with pancreatic ductal adenocarcinomas were included in this study. All patients underwent preoperative unenhanced and dynamic contrast-enhanced CT examinations. Two radiologists independently and manually contoured the region of interest of each lesion using texture analysis software on pancreatic parenchymal and portal phase CT images. Multivariate logistic regression analysis was performed to identify significant features to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Receiver operating characteristic curve analysis was performed to ascertain diagnostic ability. Results The following CT texture features were obtained to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: RMS (root mean square) (odds ratio [OR] = 0.50, P<0.001), Quantile50 (OR = 1.83, P<0.001), and sumAverage (OR = 0.92, P=0.007) in parenchymal images and “contrast” in portal phase images (OR = 6.08, P<0.001). The areas under the curves were 0.76 for RMS (sensitivity = 0.75, specificity = 0.67), 0.73 for Quantile50 (sensitivity = 0.60, specificity = 0.77), 0.70 for sumAverage (sensitivity = 0.65, specificity = 0.82), 0.85 for the combined texture features (sensitivity = 0.77, specificity = 0.85). Conclusion CT texture analysis may be helpful to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. The three combined texture features showed acceptable diagnostic performance.

Distinguishing fibrosis/necrosis from teratoma or viable disease in the retroperitoneum in post-chemotherapy, nonseminomatous testicular germ cell tumor using quantitative CT texture analysis.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 563-563
Author(s):  
Kevin George King ◽  
Sumeet Bhanvadia ◽  
Saum Ghodoussipour ◽  
Darryl Hwang ◽  
Bino Varghese ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Germ Cell ◽  
Texture Analysis ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Gray Level ◽  
Square Root ◽  
Quantitative Ct ◽  
Testicular Germ Cell ◽  
Ct Texture Analysis

563 Background: In metastatic nonseminomatous testicular germ cell tumor (NSGCT), post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) is indicated for residual masses > 1 cm because of these 45% will be fibrosis/necrosis, 45% will be teratoma and 15% will be viable malignancy. There is no imaging test that reliably distinguishes lymph nodes (LNs) with tumor (teratoma or malignancy) from LNs with fibrosis/necrosis. We evaluated whether quantitative CT texture analysis (TA) could make this differentiation. Methods: Pre- and post-chemotherapy CTs (all same phase and slice thickness) were reviewed in 22 NSGCT patients with RP LNs > 1 cm post chemotherapy. After manual segmentation of RP LNs on a 3D workstation, 187 TA metrics were derived, using 2D/3D gray-level co-occurrence matrix (GLCM), 2D/3D gray-level difference matrix (GLDM), and spectral analysis. Metrics were derived 2 ways: from post-chemotherapy CTs alone, and also as a difference between pre- and post-chemotherapy CTs, resulting in 374 metrics. PC-RPLND pathology was correlated with CT data at 88 LN stations in these 22 patients. Results: 15 imaging metrics showed a significant difference (p ≤ 0.05) between LN stations with only fibrosis/necrosis and those with teratoma or viable tumor. Seven were derived from the difference between pre- and post-chemotherapy CTs: 4 using a 2D GLCM (coronal standard deviation, coronal square root of variance, coronal mean, and coronal sum of average), and 3 using a 2D GLDM (axial variance, axial square root of variance, and coronal variance). The other 8 were derived from post-chemotherapy CTs alone: 7 using a 2D GLCM (sagittal square root of variance, sagittal standard deviation, coronal square root of variance, coronal mean, coronal standard deviation, coronal sum of average, and coronal entropy) and 1 using a 2D GLDM (sagittal sum entropy). Conclusions: CT TA shows promise in differentiating necrosis from teratoma or viable tumor in RP LNs in post-chemotherapy NSGCT. A larger study is needed to further test this method, towards a long-term goal of potentially allowing some patients to avoid PC-RPLND.

Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma

2016 ◽  
Vol 42 (2) ◽  
pp. 561-568 ◽  
Author(s):  
Gu-Mu-Yang Zhang ◽  
Hao Sun ◽  
Bing Shi ◽  
Zheng-Yu Jin ◽  
Hua-Dan Xue
Keyword(s):  
Urothelial Carcinoma ◽  
Texture Analysis ◽  
Histologic Grade ◽  
Quantitative Ct ◽  
Ct Texture Analysis

scholarly journals Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients

2021 ◽  
Author(s):  
Stefano Bracci ◽  
Miriam Dolciami ◽  
Claudio Trobiani ◽  
Antonella Izzo ◽  
Angelina Pernazza ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Advanced Nsclc ◽  
Validation Cohort ◽  
Locally Advanced ◽  
Area Under The Curve ◽  
Gray Level ◽  
Quantitative Ct ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Ct Texture Analysis

Abstract Purpose The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC. Methods By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort. Results The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively. Conclusion Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

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