Literature DB >> 27345946

Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.

Khémara Gnep1,2,3, Auréline Fargeas1,2, Ricardo E Gutiérrez-Carvajal1,2, Frédéric Commandeur1,2, Romain Mathieu1,2,4, Juan D Ospina1,2, Yan Rolland5, Tanguy Rohou6,5, Sébastien Vincendeau4, Mathieu Hatt7, Oscar Acosta1,2, Renaud de Crevoisier1,2,3.   

Abstract

PURPOSE: To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy.
MATERIALS AND METHODS: In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T2 -weighted sequences (T2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T2 -w and ADC images corresponding to first-order (n = 6), gradient-based (n = 4), and second-order Haralick textural features (n = 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence.
RESULTS: Three T2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P < 0.05). Twenty-eight T2 -w Haralick features and all four geometrical features were significantly associated with biochemical recurrence (P < 0.05). The most relevant features were Haralick features T2 -w contrast, T2 -w difference variance, ADC median, along with tumor volume and tumor area (C-index from 0.76 to 0.82; P < 0.05). By combining these most powerful features in an RSF model, the obtained C-index was 0.90.
CONCLUSION: T2 -w Haralick features appear to be strongly associated with biochemical recurrence following prostate cancer radiotherapy. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:103-117.
© 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  T2-weighted imaging; imaging biomarkers; magnetic resonance imaging; prostate cancer; radiomics; radiotherapy; texture analysis

Mesh:

Substances:

Year:  2016        PMID: 27345946     DOI: 10.1002/jmri.25335

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  43 in total

1.  Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.

Authors:  Rakesh Shiradkar; Soumya Ghose; Ivan Jambor; Pekka Taimen; Otto Ettala; Andrei S Purysko; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2018-05-07       Impact factor: 4.813

Review 2.  "Radio-oncomics" : The potential of radiomics in radiation oncology.

Authors:  Jan Caspar Peeken; Fridtjof Nüsslin; Stephanie E Combs
Journal:  Strahlenther Onkol       Date:  2017-07-07       Impact factor: 3.621

Review 3.  Texture analysis of medical images for radiotherapy applications.

Authors:  Elisa Scalco; Giovanna Rizzo
Journal:  Br J Radiol       Date:  2016-11-25       Impact factor: 3.039

4.  Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.

Authors:  Yuhao Dong; Qianjin Feng; Wei Yang; Zixiao Lu; Chunyan Deng; Lu Zhang; Zhouyang Lian; Jing Liu; Xiaoning Luo; Shufang Pei; Xiaokai Mo; Wenhui Huang; Changhong Liang; Bin Zhang; Shuixing Zhang
Journal:  Eur Radiol       Date:  2017-08-21       Impact factor: 5.315

5.  Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat.

Authors:  Zine-Eddine Khene; Karim Bensalah; Axel Largent; Shahrokh Shariat; Gregory Verhoest; Benoit Peyronnet; Oscar Acosta; Renaud DeCrevoisier; Romain Mathieu
Journal:  World J Urol       Date:  2018-04-19       Impact factor: 4.226

Review 6.  Background, current role, and potential applications of radiogenomics.

Authors:  Katja Pinker; Fuki Shitano; Evis Sala; Richard K Do; Robert J Young; Andreas G Wibmer; Hedvig Hricak; Elizabeth J Sutton; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-11-02       Impact factor: 4.813

7.  Utility of T2-weighted MRI to Differentiate Adrenal Metastases from Lipid-Poor Adrenal Adenomas.

Authors:  Wendy Tu; Jorge Abreu-Gomez; Amar Udare; Abdulmohsen Alrashed; Nicola Schieda
Journal:  Radiol Imaging Cancer       Date:  2020-10-30

8.  Comparison of MRI features in lipid-rich and lipid-poor adrenal adenomas using subjective and quantitative analysis.

Authors:  Wendy Tu; Rosalind Gerson; Jorge Abreu-Gomez; Amar Udare; Rachel Mcphedran; Nicola Schieda
Journal:  Abdom Radiol (NY)       Date:  2021-06-12

9.  Prognostic Value of Pretreatment MRI in Patients With Prostate Cancer Treated With Radiation Therapy: A Systematic Review and Meta-Analysis.

Authors:  Sungmin Woo; Sangwon Han; Tae-Hyung Kim; Chong Hyun Suh; Antonio C Westphalen; Hedvig Hricak; Michael J Zelefsky; Hebert Alberto Vargas
Journal:  AJR Am J Roentgenol       Date:  2019-12-04       Impact factor: 3.959

10.  A pilot study on dosimetric and radiomics analysis of urethral strictures following HDR brachytherapy as monotherapy for localized prostate cancer.

Authors:  Yat Man Tsang; Dinesh Vignarajah; Alan Mcwilliam; Hannah Tharmalingam; Gerry Lowe; Ananya Choudhury; Peter Hoskin
Journal:  Br J Radiol       Date:  2019-12-02       Impact factor: 3.039

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