Literature DB >> 28315203

ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study.

Stefan Schob1, Hans Jonas Meyer2, Nikolaos Pazaitis3, Dominik Schramm4, Kristina Bremicker2, Marc Exner2, Anne Kathrin Höhn5, Nikita Garnov2, Alexey Surov2.   

Abstract

PURPOSE: Apparent diffusion coefficient (ADC) histogram analysis has been used to some extent in cervical cancer (CC) to distinguish between low-grade and high-grade tumors. Although this differentiation is undoubtedly helpful, it would be even more crucial in the presurgical setting to determine whether a tumor already gained the potential to metastasize via the lymphatic system. So far, no studies investigated the potential of 3T ADC histogram analysis in CC to differentiate between nodal-positive and nodal-negative entities. Therefore, the principal aim of our study was to investigate the potential of 3T ADC histogram analysis to differentiate between CC with and without lymph node metastasis. The second aim was to elucidate possible differences in ADC histogram parameters between CC with limited vs. advanced tumor stages and well-differentiated vs. undifferentiated lesions. Finally, correlations of p53 expression and Ki-67 index with ADC parameters were analyzed. PROCEDURES: Eighteen female patients (mean age 55.4 years, range 32-79 years) with histopathologically confirmed cervical squamous cell carcinoma of the uterine cervix were prospectively enrolled. Tumor stages, tumor grading, status of metastatic dissemination, Ki67-index, and p53 expression were assessed in these patients. Diffusion weighted imaging (DWI) was obtained in a 3T scanner using the following b values: b0 and b1000 s/mm2.
RESULTS: Group comparisons using Mann-Whitney U test revealed the following findings: nodal-positive CC had statistically significant lower ADC parameters (ADCmin, ADCmean, median ADC, Mode, p10, p25, p75, and p90) in comparison to nodal-negative CC (all p < 0.05). ADCentropy was significantly elevated (p = 0.046) in tumors with advanced T stages (T3/4) compared to tumors with limited T stage (T2). ADCmin values were different in a statistically significant manner comparing G1/G2 and G3 tumors (40.45 ± 18.63 vs. 65.0 ± 23.63 × 10-5 mm2 s-1, p = 0.035). Furthermore, Spearman Rho calculation identified an inverse correlation between ADCentropy and p53 expression (r = -0.472, p = 0.048).
CONCLUSION: The main finding of our study is the discriminability of nodal-positive from nodal-negative CC using ADC histogram analysis in 3T DWI. This information is crucial for the gynecological surgeon to identify the optimal treatment strategy for patients suffering from CC. Furthermore, ADCentropy was identified as a potential imaging biomarker for tumor heterogeneity and might be able to indicate further molecular changes like loss of p53 expression, which is associated with EMT and consequentially indicates a poor prognosis in CC. Finally, our study confirmed the findings of previous works, which indicated that histogram analysis of ADC maps can distinguish between low-grade and high-grade CC. In conclusion, it can be stated that ADC histogram analysis provides additional, prognostically important information on tumor biology in CC.

Entities:  

Keywords:  ADC; Cervical cancer; DWI; Histogram analysis; Ki67

Mesh:

Substances:

Year:  2017        PMID: 28315203     DOI: 10.1007/s11307-017-1073-y

Source DB:  PubMed          Journal:  Mol Imaging Biol        ISSN: 1536-1632            Impact factor:   3.488


  29 in total

1.  Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: effects of lesion type and size.

Authors:  Savannah C Partridge; Christiane D Mullins; Brenda F Kurland; Michael D Allain; Wendy B DeMartini; Peter R Eby; Constance D Lehman
Journal:  AJR Am J Roentgenol       Date:  2010-06       Impact factor: 3.959

2.  High-risk group in node-positive patients with stage IB, IIA, and IIB cervical carcinoma after radical hysterectomy and postoperative pelvic irradiation.

Authors:  Y Aoki; M Sasaki; M Watanabe; T Sato; I Tsuneki; H Aida; K Tanaka
Journal:  Gynecol Oncol       Date:  2000-05       Impact factor: 5.482

3.  Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis.

Authors:  Alexey Surov; Daniel T Ginat; Eser Sanverdi; C C Tchoyoson Lim; Bahattin Hakyemez; Akira Yogi; Teresa Cabada; Andreas Wienke
Journal:  World Neurosurg       Date:  2015-10-31       Impact factor: 2.104

4.  Pre-treatment diffusion-weighted MR imaging for predicting tumor recurrence in uterine cervical cancer treated with concurrent chemoradiation: value of histogram analysis of apparent diffusion coefficients.

Authors:  Suk Hee Heo; Sang Soo Shin; Jin Woong Kim; Hyo Soon Lim; Yong Yeon Jeong; Woo Dae Kang; Seok Mo Kim; Heoung Keun Kang
Journal:  Korean J Radiol       Date:  2013-07-17       Impact factor: 3.500

Review 5.  Clinical significance of epithelial-mesenchymal transition.

Authors:  Konrad Steinestel; Stefan Eder; Andres Jan Schrader; Julie Steinestel
Journal:  Clin Transl Med       Date:  2014-07-02

Review 6.  Improving tumour heterogeneity MRI assessment with histograms.

Authors:  N Just
Journal:  Br J Cancer       Date:  2014-09-30       Impact factor: 7.640

7.  Epithelial-mesenchymal transition, proliferation, and angiogenesis in locally advanced cervical cancer treated with chemoradiotherapy.

Authors:  Leonardo Rojas-Puentes; Andrés F Cardona; Hernán Carranza; Carlos Vargas; Luis F Jaramillo; Delma Zea; Lucely Cetina; Beatriz Wills; Erika Ruiz-Garcia; Oscar Arrieta
Journal:  Cancer Med       Date:  2016-05-27       Impact factor: 4.452

Review 8.  Different imaging techniques for the detection of pelvic lymph nodes metastasis from gynecological malignancies: a systematic review and meta-analysis.

Authors:  Yi Gong; Qingming Wang; Li Dong; Yiping Jia; Chengge Hua; Fanglin Mi; Chunjie Li
Journal:  Oncotarget       Date:  2017-02-21

9.  WNT2 Promotes Cervical Carcinoma Metastasis and Induction of Epithelial-Mesenchymal Transition.

Authors:  Yun Zhou; Yongwen Huang; Xinping Cao; Jing Xu; Lan Zhang; Jianhua Wang; Long Huang; Shuting Huang; Linjing Yuan; Weihua Jia; Xingjuan Yu; Rongzhen Luo; Min Zheng
Journal:  PLoS One       Date:  2016-08-11       Impact factor: 3.240

10.  Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images.

Authors:  Kate Downey; Sophie F Riches; Veronica A Morgan; Sharon L Giles; Ayoma D Attygalle; Tom E Ind; Desmond P J Barton; John H Shepherd; Nandita M deSouza
Journal:  AJR Am J Roentgenol       Date:  2013-02       Impact factor: 3.959

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  27 in total

1.  Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

Authors:  He Zhang; Yunfei Mao; Xiaojun Chen; Guoqing Wu; Xuefen Liu; Peng Zhang; Yu Bai; Pengcong Lu; Weigen Yao; Yuanyuan Wang; Jinhua Yu; Guofu Zhang
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

2.  Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma.

Authors:  Stefan Schob; Anne Beeskow; Julia Dieckow; Hans-Jonas Meyer; Matthias Krause; Clara Frydrychowicz; Franz-Wolfgang Hirsch; Alexey Surov
Journal:  Childs Nerv Syst       Date:  2018-05-31       Impact factor: 1.475

3.  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

4.  Histogram analysis derived from apparent diffusion coefficient (ADC) is more sensitive to reflect serological parameters in myositis than conventional ADC analysis.

Authors:  Hans Jonas Meyer; Alexander Emmer; Malte Kornhuber; Alexey Surov
Journal:  Br J Radiol       Date:  2018-02-20       Impact factor: 3.039

5.  Histogram Analysis of T1-Weighted, T2-Weighted, and Postcontrast T1-Weighted Images in Primary CNS Lymphoma: Correlations with Histopathological Findings-a Preliminary Study.

Authors:  Hans-Jonas Meyer; Stefan Schob; Benno Münch; Clara Frydrychowicz; Nikita Garnov; Ulf Quäschling; Karl-Titus Hoffmann; Alexey Surov
Journal:  Mol Imaging Biol       Date:  2018-04       Impact factor: 3.488

6.  Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status.

Authors:  Georg Alexander Gihr; Diana Horvath-Rizea; Nikita Garnov; Patricia Kohlhof-Meinecke; Oliver Ganslandt; Hans Henkes; Hans Jonas Meyer; Karl-Titus Hoffmann; Alexey Surov; Stefan Schob
Journal:  Mol Imaging Biol       Date:  2018-08       Impact factor: 3.488

7.  Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer.

Authors:  Jiyeong Lee; Chan Kyo Kim; Sung Yoon Park
Journal:  MAGMA       Date:  2019-09-23       Impact factor: 2.310

8.  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.

Authors:  Hans Jonas Meyer; Leonard Leifels; Gordian Hamerla; Anne Kathrin Höhn; Alexey Surov
Journal:  Mol Imaging Biol       Date:  2019-08       Impact factor: 3.488

9.  Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

Authors:  Yu-Chun Lin; Chia-Hung Lin; Hsin-Ying Lu; Hsin-Ju Chiang; Ho-Kai Wang; Yu-Ting Huang; Shu-Hang Ng; Ji-Hong Hong; Tzu-Chen Yen; Chyong-Huey Lai; Gigin Lin
Journal:  Eur Radiol       Date:  2019-11-11       Impact factor: 5.315

10.  Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging.

Authors:  Yang Peng; Hao Tang; Xiaoyan Meng; Yaqi Shen; Daoyu Hu; Ihab Kamel; Zhen Li
Journal:  Quant Imaging Med Surg       Date:  2020-01
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