CT Texture Analysis—Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas

Introduction: Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC).Materials and Methods: Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests.Results: Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (ρ = −0.60 and ρ = −0.50, respectively).Conclusions: CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity

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

Associations between dynamic-contrast enhanced MRI and tumor infiltrating lymphocytes and tumor-stroma ratio in head and neck squamous cell cancer

Objectives!#!The present study used dynamic-contrast enhanced MRI (DCE-MRI) to elucidate possible associations with tumor-infiltrating lymphocytes (TIL), stroma ratio and vimentin expression in head and neck squamous cell cancer (HNSCC).!##!Methods!#!Overall, 26 patients with primary HNSCC of different localizations were involved in the study. DCE-MRI was obtained on a 3 T MRI and analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on bioptic samples before any form of treatment. P16 staining was used to define HPV-status.!##!Results!#!Tumor-stroma ratio correlated with entropy derived from K!##!Conclusions!#!DCE-MRI might be able to reflect tumor compartments and TIL expression in HNSCC. The most promising parameters were values derived from

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer – A First Preliminary Study

OBJECT: Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer. METHODS: The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated. RESULTS: Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r = 0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r = −0.69, −0.66, −0.69 and −0.63, respectively) was also identified. For T2-weighted images, Variance with r = 0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r = −0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r = −0.80, S(0;5)SumVarnc was positively with r = 0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r = −0.77). S(1;0)Correlat was with r = 0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r = 0.65 with p53 count. For T2-weighted images S(1;−1)SumEntrp was the inverse correlation with r = −0.72, whereas S(0;4)AngScMom correlated positively with r = 0.63. CONCLUSIONS: MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology

Associations between Histogram Analysis Parameters Derived from DCE-MRI and Histopathological Features including Expression of EGFR, p16, VEGF, Hif1-alpha, and p53 in HNSCC

Background. Our purpose was to elucidate possible correlations between histogram parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) with several histopathological features in head and neck squamous cell carcinomas (HNSCC). Methods. Thirty patients with primary HNSCC were prospectively acquired. Histogram analysis was derived from the DCE-MRI parameters: Ktrans, Kep, and Ve. Additionally, in all cases, expression of human papilloma virus (p16) hypoxia-inducible factor-1-alpha (Hif1-alpha), vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), and tumor suppressor protein p53 were estimated. Results. Kep kurtosis was significantly higher in p16 tumors, and Ve min was significantly lower in p16 tumors compared to the p16 negative tumors. In the overall sample, Kep entropy correlated well with EGFR expression (p=0.38, P=0.04). In p16 positive carcinomas, Ktrans max correlated with VEGF expression (p=0.46, P=0.04), Ktrans kurtosis correlated with Hif1-alpha expression (p=0.46, P=0.04), and Ktrans entropy correlated with EGFR expression (p=0.50, P=0.03). Regarding Kep parameters, mode correlated with VEGF expression (p=0.51, P=0.02), and entropy correlated with Hif1-alpha expression (p=0.47, P=0.04). In p16 negative carcinomas, Kep mode correlated with Her2 expression (p=−0.72, P=0.03), Ve max correlated with p53 expression (p=−0.80, P=0.009), and Ve p10 correlated with EGFR expression (p=0.68, P=0.04). Conclusion. DCE-MRI can reflect several histopathological features in HNSCC. Associations between DCE-MRI and histopathology in HNSCC depend on p16 status. Kep kurtosis and Ve min can differentiate p16 positive and p16 negative carcinomas

Combined Metabolo-Volumetric Parameters of 18F-FDG-PET and MRI Can Predict Tumor Cellularity, Ki67 Level and Expression of HIF 1alpha in Head and Neck Squamous Cell Carcinoma: A Pilot Study

BACKGROUND: Our purpose was to evaluate associations of combined 18F-FDG-PET and MRI parameters with histopathological features in head and neck squamous cell carcinoma (HNSCC). METHODS: Overall, 22 patients with HNSCC were acquired (10 with G1/2 tumors and 12 with G3 tumors).18F-FDG-PET/CT and MRI was performed and maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG) and metabolic tumor volume (MTV) were estimated. Neck MRI was obtained on a 3 T scanner. Diffusion weighted imaging was performed with estimation of apparent diffusion coefficient (ADC). Perfusion parameters Ktrans, Ve, and Kep were derived from dynamic contrast-enhanced (DCE) imaging. Different combined PET/MRI parameters were calculated as ratios: PET parameters divided by ADC or DCE MRI parameters. The following histopathological features were estimated: Ki 67, EGFR, VEGF, p53, hypoxia-inducible factor (HIF)-1α, and cell count. Spearman's correlation coefficient (p) was used for correlation analysis. P < .05 was taken to indicate statistical significance. RESULTS: In overall sample, cellularity correlated with SUVmax/ADCmin (P = .558, P = .007), TLG/ADCmin (P = .546, P = .009), and MTV/ADCmin (P = .468, P = .028). MTV/Kep correlated with expression of HIF-1α (P = .450, P = 0,047). In G1/2 tumors, SUVmax/ADCmin correlated with HIF-1α (P = −.648, P = .043); MTV/Kep (P = −.669, P = .034) and TLG/Kep (P = −.644, P = .044) with Ki67. In G3 tumors, cellularity correlated with SUVmax/ADCmin (P = .832, P = .001), SUVmax/ADCmean (P = .741, P = .006), and TLG/ADCmin (P = .678, P = .015). MTV/ADCmin and TLG/ADCmin tended to correlate with HIF-1α. CONCLUSION: Combined parameters of 18F-FDG-PET and MRI can reflect Ki 67, tumor cellularity and expression of HIF-1α in HNSCC. Associations between parameters of 18F-FDG-PET and MRI and histopathology depend on tumor grading

Multi-Parameter Analysis of Disseminated Tumor Cells (DTCs) in Early Breast Cancer Patients with Hormone-Receptor-Positive Tumors

Background: Patients with hormone-receptor-positive (HR+) breast cancer are at increased risk for late recurrence. One reason might be disseminated tumor cells (DTCs), which split off in the early stages of the disease and metastasize into the bone marrow (BM). Methods: We developed a novel multi-parameter immunofluorescence staining protocol using releasable and bleachable antibody&ndash;fluorochrome-conjugates. This sequential procedure enabled us to analyze six distinct phenotypical and therapy-related markers on the same DTC. We characterized BM aspirates from 29 patients with a HR+ tumor and a known positive DTC status&mdash;based on the standardized detection of epithelial cells in BM. Results: Using the immunofluorescence staining, a total of 153 DTCs were detected. Luminal A patients revealed a higher DTC count compared with luminal B. The majority of the detected DTCs were CK-positive (128/153). However, in 16 of 17 luminal A patients we found HER2-positive DTCs. We detected CK-negative DTCs (25/153) in 12 of 29 patients. Of those cells, 76% were Ki67-positive and 68% were HER2-positive. Moreover, we detected DTC clusters consisting of mixed characteristics in 6 of 29 patients. Conclusions: Using sequential multi-parameter imaging made it possible to identify distinct DTC profiles not solely based on epithelial features. Our findings indicate that characterization rather than quantification of DTCs might be relevant for treatment decisions

Multi-Parameter Analysis of Disseminated Tumor Cells (DTCs) in Early Breast Cancer Patients with Hormone-Receptor-Positive Tumors

Background: Patients with hormone-receptor-positive (HR+) breast cancer are at increased risk for late recurrence. One reason might be disseminated tumor cells (DTCs), which split off in the early stages of the disease and metastasize into the bone marrow (BM). Methods: We developed a novel multi-parameter immunofluorescence staining protocol using releasable and bleachable antibody–fluorochrome-conjugates. This sequential procedure enabled us to analyze six distinct phenotypical and therapy-related markers on the same DTC. We characterized BM aspirates from 29 patients with a HR+ tumor and a known positive DTC status—based on the standardized detection of epithelial cells in BM. Results: Using the immunofluorescence staining, a total of 153 DTCs were detected. Luminal A patients revealed a higher DTC count compared with luminal B. The majority of the detected DTCs were CK-positive (128/153). However, in 16 of 17 luminal A patients we found HER2-positive DTCs. We detected CK-negative DTCs (25/153) in 12 of 29 patients. Of those cells, 76% were Ki67-positive and 68% were HER2-positive. Moreover, we detected DTC clusters consisting of mixed characteristics in 6 of 29 patients. Conclusions: Using sequential multi-parameter imaging made it possible to identify distinct DTC profiles not solely based on epithelial features. Our findings indicate that characterization rather than quantification of DTCs might be relevant for treatment decisions

Correlations Between DCE MRI and Histopathological Parameters in Head and Neck Squamous Cell Carcinoma

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) can characterize perfusion and vascularization of tissues. DCE MRI parameters can differentiate between malignant and benign lesions and predict tumor grading. The purpose of this study was to correlate DCE MRI findings and various histopathological parameters in head and neck squamous cell carcinoma (HNSCC). PATIENTS AND METHODS: Sixteen patients with histologically proven HNSCC (11 cases primary tumors and in 5 patients with local tumor recurrence) were included in the study. DCE imaging was performed in all cases and the following parameters were estimated: Ktrans, Ve, Kep, and iAUC. The tumor proliferation index was estimated on Ki 67 antigen stained specimens. Microvessel density parameters (stained vessel area, total vessel area, number of vessels, and mean vessel diameter) were estimated on CD31 antigen stained specimens. Spearman's non-parametric rank sum correlation coefficients were calculated between DCE and different histopathological parameters. RESULTS: The mean values of DCE perfusion parameters were as follows: Ktrans 0.189 ± 0.056 min−1, Kep 0.390 ± 0.160 min−1, Ve 0.548 ± 0.119%, and iAUC 22.40 ± 12.57. Significant correlations were observed between Kep and stained vessel areas (r = 0.51, P = .041) and total vessel areas (r = 0.5118, P = .043); between Ve and mean vessel diameter (r = −0.59, P = .017). Cell count had a tendency to correlate with Ve (r = −0.48, P = .058). In an analysis of the primary HNSCC only, a significant inverse correlation between Ktrans and KI 67 was identified (r = −0.62, P = .041). Our analysis showed significant correlations between DCE parameters and histopathological findings in HNSCC

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