Literature DB >> 30189236

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.

Hans Jonas Meyer1, Leonard Leifels2, Gordian Hamerla3, Anne Kathrin Höhn4, Alexey Surov5.   

Abstract

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.
Copyright © 2018. Published by Elsevier Inc.

Entities:  

Keywords:  ADC; DWI; Head and neck cancer

Mesh:

Substances:

Year:  2018        PMID: 30189236     DOI: 10.1016/j.mri.2018.07.013

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  17 in total

1.  Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression.

Authors:  Feng Wang; Yuxiang Wang; Yan Zhou; Congrong Liu; Dong Liang; Lizhi Xie; Zhihang Yao; Jianyu Liu
Journal:  Mol Imaging Biol       Date:  2019-08       Impact factor: 3.488

2.  Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis.

Authors:  Hans-Jonas Meyer; Andreas Wienke; Alexey Surov
Journal:  Breast Care (Basel)       Date:  2021-02-23       Impact factor: 2.860

3.  Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis.

Authors:  Alexey Surov; Maciej Pech; Maciej Powerski; Katja Woidacki; Andreas Wienke
Journal:  Dig Dis       Date:  2021-03-04       Impact factor: 2.404

4.  Predictive Value of Early Post-Treatment Diffusion-Weighted MRI for Recurrence or Tumor Progression of Head and Neck Squamous Cell Carcinoma Treated with Chemo-Radiotherapy.

Authors:  Esteban Brenet; Coralie Barbe; Christine Hoeffel; Xavier Dubernard; Jean-Claude Merol; Léa Fath; Stéphanie Servagi-Vernat; Marc Labrousse
Journal:  Cancers (Basel)       Date:  2020-05-14       Impact factor: 6.639

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

Authors:  Hans Jonas Meyer; Leonard Leifels; Gordian Hamerla; Anne Kathrin Höhn; Alexey Surov
Journal:  Contrast Media Mol Imaging       Date:  2019-01-02       Impact factor: 3.161

6.  Associations between FDG-PET and Ki 67-index in head and neck cancer: A meta-analysis.

Authors:  Hans-Jonas Meyer; Peter Gundermann; Alexey Surov
Journal:  Medicine (Baltimore)       Date:  2019-10       Impact factor: 1.889

7.  Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study.

Authors:  Noriyuki Fujima; Yukie Shimizu; Daisuke Yoshida; Satoshi Kano; Takatsugu Mizumachi; Akihiro Homma; Koichi Yasuda; Rikiya Onimaru; Osamu Sakai; Kohsuke Kudo; Hiroki Shirato
Journal:  Cancers (Basel)       Date:  2019-06-10       Impact factor: 6.639

8.  Apparent diffusion coefficient-based histogram analysis differentiates histological subtypes of periampullary adenocarcinoma.

Authors:  Jing-Yu Lu; Hao Yu; Xian-Lun Zou; Zhen Li; Xue-Mei Hu; Ya-Qi Shen; Dao-Yu Hu
Journal:  World J Gastroenterol       Date:  2019-10-28       Impact factor: 5.742

9.  Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis.

Authors:  Alexey Surov; Yun-Woo Chang; Lihua Li; Laura Martincich; Savannah C Partridge; Jin You Kim; Andreas Wienke
Journal:  BMC Cancer       Date:  2019-11-05       Impact factor: 4.430

10.  Apparent Diffusion Coefficient for Distinguishing Between Malignant and Benign Lesions in the Head and Neck Region: A Systematic Review and Meta-Analysis.

Authors:  Alexey Surov; Hans Jonas Meyer; Andreas Wienke
Journal:  Front Oncol       Date:  2020-01-08       Impact factor: 6.244

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