Literature DB >> 23345351

Histogram-based apparent diffusion coefficient analysis: an emerging tool for cervical cancer characterization?

Andrew B Rosenkrantz1.   

Abstract

OBJECTIVE: Numerous prior studies have shown the utility of apparent diffusion coefficient values in cervical cancer assessment, particularly in differentiation from benign cervix. This article will discuss histogram-based analysis for detection of adverse histologic features of cervical cancer.
CONCLUSION: The study by Downey et al. in this issue investigates histogram-based analysis for detection of adverse histologic features of cervical cancer, including subtype and grade. This approach offers a more complete assessment of tumor texture and heterogeneity. Given the potential utility suggested by the results of this study, additional larger studies are warranted.

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Year:  2013        PMID: 23345351     DOI: 10.2214/AJR.12.9926

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  13 in total

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

Authors:  Stefan Schob; Hans Jonas Meyer; Nikolaos Pazaitis; Dominik Schramm; Kristina Bremicker; Marc Exner; Anne Kathrin Höhn; Nikita Garnov; Alexey Surov
Journal:  Mol Imaging Biol       Date:  2017-12       Impact factor: 3.488

2.  Comparison of FDG PET metabolic tumour volume versus ADC histogram: prognostic value of tumour treatment response and survival in patients with locally advanced uterine cervical cancer.

Authors:  Yoshiko Ueno; Robert Lisbona; Tsutomu Tamada; Amer Alaref; Kazuro Sugimura; Caroline Reinhold
Journal:  Br J Radiol       Date:  2017-06-16       Impact factor: 3.039

3.  Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

Authors:  Riccardo De Robertis; Bogdan Maris; Nicolò Cardobi; Paolo Tinazzi Martini; Stefano Gobbo; Paola Capelli; Silvia Ortolani; Sara Cingarlini; Salvatore Paiella; Luca Landoni; Giovanni Butturini; Paolo Regi; Aldo Scarpa; Giampaolo Tortora; Mirko D'Onofrio
Journal:  Eur Radiol       Date:  2018-01-19       Impact factor: 5.315

4.  Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer.

Authors:  Mayra Evelia Jiménez de Los Santos; Juan Armando Reyes-Pérez; Victor Domínguez Osorio; Yolanda Villaseñor-Navarro; Liliana Moreno-Astudillo; Itzel Vela-Sarmiento; Isabel Sollozo-Dupont
Journal:  World J Gastroenterol       Date:  2022-06-21       Impact factor: 5.374

5.  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

6.  Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient.

Authors:  Jin You Kim; Jin Joo Kim; Ji Won Lee; Nam Kyung Lee; Geewon Lee; Taewoo Kang; Heesung Park; Yo Han Son; Robert Grimm
Journal:  Eur Radiol       Date:  2018-08-02       Impact factor: 5.315

7.  Role of Functional Magnetic Resonance Imaging Derived Parameters as Imaging Biomarkers and Correlation with Clinicopathological Features in Carcinoma of Uterine Cervix.

Authors:  Ramireddy Jeba Karunya; Putta Tharani; Subhashini John; Ramani Manoj Kumar; Saikat Das
Journal:  J Clin Diagn Res       Date:  2017-08-01

Review 8.  Functional imaging to predict tumor response in locally advanced cervical cancer.

Authors:  Tara D Barwick; Alexandra Taylor; Andrea Rockall
Journal:  Curr Oncol Rep       Date:  2013-12       Impact factor: 5.075

Review 9.  Improving tumour heterogeneity MRI assessment with histograms.

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

10.  High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features.

Authors:  Ahmad Chaddad; Camel Tanougast
Journal:  Adv Bioinformatics       Date:  2015-10-20
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