CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

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Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

Contrast Media & Molecular Imaging ◽

10.1155/2021/8873065 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Cuiping Li ◽

Mingxue Zheng ◽

Xiaomin Zheng ◽

Xin Fang ◽

Jiangning Dong ◽

...

Keyword(s):

Logistic Regression ◽

Texture Analysis ◽

Volume Fraction ◽

Texture Features ◽

Maximum Level ◽

Cervical Squamous Cell Carcinoma ◽

Proliferation Index ◽

Imaging Biomarkers ◽

Ki 67 ◽

D Value

Purpose. This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods. A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D ∗ )) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results. The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group ( P < 0.05 ). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions. Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.

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

Acta Radiologica ◽

10.1177/0284185119875023 ◽

2019 ◽

Vol 61 (5) ◽

pp. 595-604 ◽

Cited By ~ 1

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.

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Chromogranin A, Ki-67 index and IGF-related genes in patients with neuroendocrine tumors

Endocrine Connections ◽

10.1530/ec-13-0052 ◽

2013 ◽

Vol 2 (4) ◽

pp. 172-177 ◽

Cited By ~ 6

Author(s):

R C S van Adrichem ◽

L J Hofland ◽

R A Feelders ◽

M C De Martino ◽

P M van Koetsveld ◽

...

Keyword(s):

Mrna Expression ◽

Neuroendocrine Tumors ◽

Chromogranin A ◽

Insulin Receptors ◽

Proliferation Index ◽

Gastroenteropancreatic Neuroendocrine Tumors ◽

Ki 67 ◽

Igf Binding Proteins ◽

Igf Receptors ◽

Igf Binding

Chromogranin A (CgA) and the Ki-67 proliferation index are considered as important biochemical and pathological markers for clinical behaviour of gastroenteropancreatic neuroendocrine tumors (GEP NETs), respectively. The IGF system has been suggested as an important regulator of GEP NET proliferation and differentiation. A possible relationship between serum CgA (sCgA), Ki-67 proliferation index, and expression of IGF-related genes in patients with GEP NETs has not been demonstrated yet. This study investigates the relationship between sCgA, the Ki-67 proliferation index, and the expression of IGF-related genes in GEP NET tissues and their relation with 5-year survival. Tumor and blood samples from 22 GEP NET patients were studied. Tumoral mRNA expression of IGF-related genes (IGFs: IGF1, IGF2; IGF receptors: IGF1R, IGF2R; insulin receptors: subtype A (IR-A) and B (IR-B); IGF-binding proteins (IGFBPs): IGFBP1, IGFBP2, IGFBP3, and IGFBP6) was measured using quantitative RT-PCR. Ki-67 proliferation index was determined using immunohistochemistry. sCgA was measured with ELISA. Five-year survival in patients with nonelevated sCgA (n=11) was 91 vs 46% in patients with elevated sCgA (n=11) (P=0.006). IR-A mRNA expression was significantly higher in tumors obtained from patients with elevated sCgA than in those from patients with nonelevated sCgA (6.42±2.08 vs 2.60±0.40; P=0.04). This data suggests that sCgA correlates well with 5-year survival of GEP NET patients, and that IR-A mRNA expression correlates well with tumor mass in GEP NET patients.

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Canine Neuroendocrine Tumors of the Pancreas: A Study Using Image Analysis Techniques for the Discrimination of Metastatic Versus Nonmetastatic Tumors

Veterinary Pathology ◽

10.1177/030098589703400206 ◽

1997 ◽

Vol 34 (2) ◽

pp. 138-145 ◽

Cited By ~ 11

Author(s):

G. Minkus ◽

U. Jütting ◽

M. Aubele ◽

K. Rodenacker ◽

P. Gais ◽

...

Keyword(s):

Image Analysis ◽

Neuroendocrine Tumors ◽

Nucleolar Organizer ◽

Biological Behavior ◽

Proliferation Index ◽

Nucleolar Organizer Regions ◽

Ki 67 ◽

Analysis Techniques ◽

Significant Difference ◽

Image Analysis Techniques

Canine pancreatic neuroendocrine tumors were studied using different image analysis techniques (nuclear image histometry, analysis of argyrophilic proteins of nucleolar organizer regions, determination of the mouse anti-Ki 67 antigen proliferation index, and DNA densitometry) to correlate their biological behavior with objective phenotypic markers. The methods were compared to determine the best method for distinguishing between metastatic and nonmetastatic tumors. Discrimination between the two types of tumor was possible using nuclear image histometry in combination with morphometric analysis of argyrophilic proteins of nucleolar organizer regions. In contrast, the mouse anti-Ki 67 antigen proliferation index, DNA measurement, and immunohistochemical parameters revealed no significant difference between the two types of tumors.

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Albumin to alkaline phosphatase ratio: does it predict survival in grade 1 and grade 2 neuroendocrine tumors?

10.21203/rs.2.17508/v1 ◽

2019 ◽

Author(s):

Yusuf Acikgoz ◽

Öznur Bal ◽

Mutlu Doğan

Keyword(s):

Alkaline Phosphatase ◽

Neuroendocrine Tumors ◽

Prognostic Value ◽

Proliferation Index ◽

Rank Test ◽

P Value ◽

Albumin Concentration ◽

Ki 67 ◽

Registration Data ◽

Well Differentiated

Abstract BACKGROUND: Neuroendocrine tumors (NETs) are very heterogeneous tumors. Although it is classified according to Ki-67 proliferation index and mitotic count, their behavior may greatly vary even in the same group. Therefore, more accurate prognostic markers are required to predict prognosis in patients with well differentiated NETs. This study is aimed to evaluate prognostic value of albumin to alkaline phosphatase ratio (AAPR) in patients with well differentiated neuroendocrine tumors. PATIENTS AND METHODS: A total of 110 patients included in this study. Patients' data were obtained from registration data-base of the hospital and reviewed retrospectively. AAPR was calculated by dividing albumin concentration (g/dl) to alkaline phosphatase level (U/L). Cut off value for AAPR was determined by Receiver Operating Characteristic (ROC) analysis. Survival analysis was performed by Kaplan-Meier method with the Long-rank test. We reported two-sided p value and p<0.05 was considered statistically significant.RESULT: The calculated optimum cut-off value for AAPR was 0.028. Patients were divided into two groups as patients with AAPR ≤0.028 (n:22, 20%) and, with AAPR >0.028 (n:88, 80%). Patients with AAPR >0.028 had statistically longer overall survival (OS) compared with patients with ≤0.028 ( NR vs 96,8 months, p=0.001). Additionally, AAPR has been shown to be an independent prognostic factor for OS in in multivariate analysis (HR=4.942, 95% CI=1.693-14.420, p=0.003).CONCLUSION: Patients with higher AAPR had more favourable prognosis compared to patients with lower AAPR. We demonstrated that AAPR can be of prognostic value in well-differentiated NETs.

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Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas

Frontiers in Oncology ◽

10.3389/fonc.2021.678441 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jia You ◽

Jiandong Yin

Keyword(s):

Texture Analysis ◽

Multivariate Logistic Regression Analysis ◽

Texture Features ◽

Roc Curves ◽

Support Vector ◽

Classification Models ◽

Entire Volume ◽

First Order ◽

T Stage ◽

Combined Application

ObjectiveTo determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1–2 versus T3–4) in rectal carcinomas.Materials and MethodsOne hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman’s correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves.ResultsThe wavelet HHH NGTDM strength (RS = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (RS = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (RS = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (RS = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively.ConclusionsTexture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.

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Comparison of Manual and Digital Assessment of KI-67 in Neuroendocrine Tumors

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqaa161.274 ◽

2020 ◽

Vol 154 (Supplement_1) ◽

pp. S125-S125

Author(s):

B S Raju ◽

M Quinton ◽

L Hassell

Keyword(s):

Image Analysis ◽

Neuroendocrine Tumors ◽

Digital Image ◽

Digital Image Analysis ◽

Hot Spot ◽

World Health ◽

Proliferation Index ◽

Digital Analysis ◽

Ki 67 ◽

Digital Assessment

Abstract Introduction/Objective Proliferative activity is an essential prognostic and treatment indicator for neuroendocrine tumors (NET). Ki-67 proliferation index, if reported by unaided microscopic estimation on hot-spot locations could lead to variability and inconsistencies. This study aims to compare the Ki-67 assessment of NETs by visual estimation versus automated digital image analysis (Roche iCoreo/Virtuoso). Methods 212 patients with Ki-67-graded GI NETs (117 G1; 61 G2; 34 G3) from 2010 to 2019 were reassessed using digital image analysis quantification of hot spot areas of at least 500 cells (average 800 cells). Revised tumor grades were assigned according to the European Neuroendocrine Tumor Society guidelines and the 2010 World Health Organization classification and compared to initially reported grade. Results We found 75% concordance for G1, with 22% of cases upgraded to G2 and 3% of cases upgraded to G3. For G2, there was 70.5% agreement, with 13.1% of cases downgraded to G1 and 16.4% upgraded to G3. For G3, there was 100% agreement, (kappa=0.64, overall). Retrospective review of discordant G3 cases revealed cases with known metastasis, small fragments of tissue, or polyps. Scanning and scoring required approximately 10 minutes per case. Conclusion Our data shows the time/effort difference of visually estimating versus automated digital analysis may lead to significant classification errors in these tumors. Although digital analysis has limitations, including tumor heterogeneity, misidentification of tumor cells, and poor immunostaining which could require manual counting by a pathologist, this rigor should be reinforced and explicitly stated to increase accuracy and reproducibility of grading.

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