Anne Kathrin Höhn

Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer

Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid carcinomas which had already gained the capacity to metastasize lymphatically from those not yet being able to spread via the lymphatic system. The secondary aim was to reflect prognostically important tumor-biological features like cellularity and proliferative activity with ADC histogram analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status, extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm2. Whole tumor ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed significant correlations with immunohistopathological parameters. Most importantly, ADC histogram skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal positive thyroid carcinoma. Conclusions: histogram analysis of whole ADC tumor volumes has the potential to provide valuable information on tumor biology in thyroid carcinoma. However, further studies are warranted.

Monoclonal origin of peritoneal implants and lymph node deposits in serous borderline ovarian tumors (s-BOT) with high intratumoral homogeneity.

Molecular studies have shown that the most prevalent mutations in serous ovarian borderline tumors (s-BOT) are BRAF and/or KRAS alterations. About one third of s-BOT represent peritoneal implants and/or lymph node involvement. These extraovarian deposits may be monoclonal or polyclonal in origin. To test both the hypotheses, mutational analyses using pyrosequencing for BRAF codon 600 and KRAS codon 12/13 and 61 of microdissected tissue was performed in 15 s-BOT and their invasive and noninvasive peritoneal implants. Two to 6 implants from different peritoneal sites were examined in 13 cases. Lymph node deposits were available for the analysis in 3 cases. Six s-BOT showed mutation in exon 2 codon 12 of the KRAS proto-oncogen. Five additional cases showed BRAF p.V600E mutation representing an overall mutation rate of 73.3%. Multiple (2–6) peritoneal implants were analyzed after microdissection in 13 of 15 cases. All showed identical mutational results when compared with the ovarian site of the disease. All lymph node deposits, including those with multiple deposits in different nodes, showed identical results, suggesting high intratumoral mutational homogeneity. The evidence presented in this study and the majority of data reported in the literature support the hypothesis that s-BOT with their peritoneal implants and lymph node deposits show identical mutational status of BRAF and KRAS suggesting a monoclonal rather than a polyclonal disease regarding these both tested genetic loci. In addition, a high intratumoral genetic homogeneity can be suggested. In conclusion, the results of the present study support the monoclonal origin of s-BOT and their peritoneal implants and lymph node deposits.

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.

Global Increase of p16INK4a in APC-Deficient Mouse Liver Drives Clonal Growth of p16INK4a-Negative Tumors

Reduction of β-catenin (CTNNB1) destroying complex components, for example, adenomatous polyposis coli (APC), induces β-catenin signaling and subsequently triggers activation of genes involved in proliferation and tumorigenesis. Though diminished expression of APC has organ-specific and threshold-dependent influence on the development of liver tumors in mice, the molecular basis is poorly understood. Therefore, a detailed investigation was conducted to determine the underlying mechanism in the development of liver tumors under reduced APC levels. Mouse liver at different developmental stages was analyzed in terms of β-catenin target genes including Cyp2e1, Glul, and Ihh using real-time RT-PCR, reporter gene assays, and immunohistologic methods with consideration of liver zonation. Data from human livers with mutations in APC derived from patients with familial adenomatous polyposis (FAP) were also included. Hepatocyte senescence was investigated by determining p16INK4a expression level, presence of senescence-associated β-galactosidase activity, and assessing ploidy. A β-catenin activation of hepatocytes does not always result in β-catenin positive but unexpectedly also in mixed and β-catenin–negative tumors. In summary, a senescence-inducing program was found in hepatocytes with increased β-catenin levels and a positive selection of hepatocytes lacking p16INK4a, by epigenetic silencing, drives the development of liver tumors in mice with reduced APC expression (Apc580S mice). The lack of p16INK4a was also detected in liver tumors of mice with triggers other than APC reduction. Implications: Epigenetic silencing of p16Ink4a in selected liver cells bypassing senescence is a general principle for development of liver tumors with β-catenin involvement in mice independent of the initial stimulus. Mol Cancer Res; 13(2); 239–49. ©2014 AACR.

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. Spearmans 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.

Processing and Histopathological Workup of Hysterectomy Specimens

The challenges of histopathology have been changed dramatically during the last years. The pathologist nowadays is a diagnostic oncologist. The histopathologic report is one cornerstone and within the majority of cases the basement of quality control and decision making for patients treatment. The final pathology report bases on a very accurate macroscopic description and cutting of the specimens. The quality of the histopathological report is mainly influenced by the available clinical information. So, it is necessary that the pathologist and the clinician speak the same language. This chapter describes the requirements for good clinical practice in handling and reporting of different kinds of hysterectomy specimens and gives an overview about the needs as well as for the clinicians but also for gynaecologic pathologists. Special focus was made on the requirements for optimal handlings influenced by the clinicians. The procedure colloquially known, as frozen section is one of the most important and potentially stressful tasks that the pathologist performs in practice. The advances and limitations of that technique is discussed. Detailed descriptions are given for the parameters included in the histopathology report for benign and malignant disease within hysterectomies, lymphonodectomy and omentectomy specimens.

Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma

PurposeTo analyze associations between histogram analysis parameters derived from conventional magnetic resonance imaging (MRI) and different histopathological features in head and neck squamous cell carcinoma (HNSCC).ProceduresThirty-four patients with histologically proven primary HNSCC were prospectively acquired. Histogram analysis was derived from pre-contrast T1-weighted (T1w) and T2-weighted (T2w) images. In all cases, expression of HIF-1α, VEGF, EGFR, p53, Ki67, and p16 as well as tumor cell count was analyzed.ResultsIn the overall sample, inverse correlation between entropy derived from T1w images and p53 expression (p = − 0.458, P = 0.01) was found. Furthermore, p10 derived from T1w images correlated with VEGF expression (p = 0.371, P = 0.04). In the p16-positive tumors, VEGF expression correlated with several parameters derived from T1w images: mean (p = 0.481, P = 0.032), p10 (p = 0.489, P = 0.029), p25 (p = 0.475, P = 0.034), median (p = 0.468, P = 0.037), and mode (p = 0.492, P = 0.028). Several T2w parameters were associated with p53 expression: mean (p = 0.569, P = 0.007), p25 (p = 0.508, P = 0.019), p75 (p = 0.479, P = 0.028), median (p = 0.555, P = 0.009), and mode (p = 0.468, P = 0.033). Kurtosis derived from T2w images correlated with cell count (p = 0.534, P = 0.013). In p16-negative carcinomas, T2w parameters correlated with p53 expression: max (p = 0.736, P = 0.015), p90 (p = 0.687, P = 0.028), and standard deviation (p = 0.760, P = 0.011). T2w p10 (p = − 0.709, P = 0.022) and T2w p25 (p = − 0.733, P = 0.016) correlated also with HIF-1α expression.ConclusionsMultiple associations between histogram parameters derived from T1w and T2w images and clinically relevant histopathological features were found in HNSCC. Therefore, imaging parameters can be also used as surrogate markers for tumor cellularity, proliferation, and vascularization in HNSCC. The identified correlations differed significantly between p16-positive and p16-negative cancers.

Associations between whole tumor histogram analysis parameters derived from ADC maps and expression of EGFR, VEGF, Hif 1-alpha Her-2 and Histone 3 in uterine cervical cancer

OBJECTIVE Diffusion weighted imaging (DWI) can be quantified by apparent diffusion coefficient (ADC) and can predict tissue microstructure. The aim of the present study was to analyze possible associations between ADC histogram based parameters with different histopathological parameters in cervical squamous cell carcinoma. MATERIALS AND METHODS 18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled. In all cases, pelvic MRI was performed with a DWI (b-values 0 and 1000 s/mm2). Histogram analysis was performed as a whole lesion measurement. Histopathological parameters included expression of EGFR, VEGF, Hif1-alpha, Her2 and Histone 3. Spearmans correlation coefficient was used to analyze associations between investigated parameters. RESULTS Analyze of the investigated ADC histogram parameters showed a good interreader variability, ranging from 0.705 for entropy to 0.959 for ADCmedian. EGFR expression correlated statistically significant with several histogram parameters. The highest correlation was observed for p75 (p = -0.562, P = 0.015). There were several correlations with histone 3, the highest with p25 (p = -0.610, P = 0.007). None of the ADC related parameters correlated statistically significant with expression of VEGF, Hif1-alpha and Her2. CONCLUSION Histogram analysis showed a good interreader agreement. ADC histogram parameters might be able to reflect expression of EGFR and histone 3 in cervical squamous cell carcinomas, but not expression of VEGF, Hif1-alpha and Her2.

ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1α, Her 2 and p53. A preliminary study

OBJECTIVE Apparent diffusion coefficient (ADC) values derived from Diffusion-weighted images are able to reflect tumor microstructure, such as cellularity, extracellular matrix or proliferation potential. This present study sought to correlate prognostic relevant histopathologic parameters with ADC values derived from a whole lesion measurement in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS Thirty-four patients with histological proven primary HNSCC were prospectively acquired. Histogram analysis was derived from ADC maps. In all cases, expression of Hif1-alpha, VEGF, EGFR, p53, p16, Her 2 were analyzed. RESULTS In the overall patient sample, ADCmax correlated with p53 expression (p = -0.446, p = 0.009) and ADCmode correlated with Her2-expression (p = -0.354, p = 0.047). In the p16 positive group there were several correlations. P25, P90 and entropy correlated with Hif1-alpha (p = -0.423, p = 0.05, p = -0.494, p = 0.019, p = 0.479, p = 0.024, respectively). Kurtosis correlated with P53 expression (p = -0.466, p = 0.029). For p16 negative carcinomas the following associations could be identified. Mode correlated with VEGF-expression (p = -0.657, p = 0.039). ADCmax, P75, P90, and Std correlated with p53-expression (p = -0.827, p = 0.002, p = -0.736, p = 0.01, p = -0.836, p = 0.001 and p = -0.70, p = 0.016, respectively). There were no statistically significant differences of ADC histogram parameters between p16 positive and p16 negative carcinomas. CONCLUSION ADC histogram values can reflect different histopathological features in HNSCC. Associations between ADC histogram analysis parameters and histopathology depend on p16 status.

Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer

RATIONALE AND OBJECTIVES Histogram analysis is an imaging analysis in which a whole tumor can be assessed, and every voxel of a radiological image is issued into a histogram. Thereby, statistically information about tumor can be obtained. The purpose of the study was to analyze possible relationships between histogram parameters derived from conventional MRI sequences and several histopathological features in cervical squamous cell carcinomas. METHODS A total of 18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled into the study. In all cases, pelvic MRI with a clinically protocol was performed. Histogram analysis was performed as a whole lesion measurement, calculating several percentils, minimum, mean, median, mode, maximum, kurtosis, skewness, and entropy. Histopathological parameters included expression of epidermal-growth factor (EGFR), vascular endothelial growth factor, hypoxia-inducible factor 1-alpha, Her2, and Histone 3. Spearmans correlation coefficient was used to analyze associations between investigated parameters. RESULTS Several pre- and postcontrast derived T1-weighted parameters correlated inversely with EGFR expression. For precontrast T1-weighted images, the strongest correlation was found for p90 (ρ = -0.77, p = 0.004). For postcontrast T1-weighted images, the strongest correlation was observed for minimum (ρ = -0.64, p = 0.021). Several parameters derived from T2-weighted images were statistically significant different between Her2-positive and Her2 negative tumors. Skewness had the best p-value ( p = 0.004). CONCLUSIONS Histogram analysis parameters of T1-weighted and T2-weighted images reflect HER2 status and EGFR expression in cervical cancer. Histogram parameters cannot predict cell count, proliferation index, or angiogenesis related histopathological features.