Literature DB >> 29571795

Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases-A pilot study.

Carolin Reischauer1, René Patzwahl2, Dow-Mu Koh3, Johannes M Froehlich4, Andreas Gutzeit5.   

Abstract

OBJECTIVE: To evaluate whole-lesion volumetric texture analysis of apparent diffusion coefficient (ADC) maps for assessing treatment response in prostate cancer bone metastases.
MATERIALS AND METHODS: Texture analysis is performed in 12 treatment-naïve patients with 34 metastases before treatment and at one, two, and three months after the initiation of androgen deprivation therapy. Four first-order and 19 second-order statistical texture features are computed on the ADC maps in each lesion at every time point. Repeatability, inter-patient variability, and changes in the feature values under therapy are investigated. Spearman rank's correlation coefficients are calculated across time to demonstrate the relationship between the texture features and the serum prostate specific antigen (PSA) levels.
RESULTS: With few exceptions, the texture features exhibited moderate to high precision. At the same time, Friedman's tests revealed that all first-order and second-order statistical texture features changed significantly in response to therapy. Thereby, the majority of texture features showed significant changes in their values at all post-treatment time points relative to baseline. Bivariate analysis detected significant correlations between the great majority of texture features and the serum PSA levels. Thereby, three first-order and six second-order statistical features showed strong correlations with the serum PSA levels across time.
CONCLUSION: The findings in the present work indicate that whole-tumor volumetric texture analysis may be utilized for response assessment in prostate cancer bone metastases. The approach may be used as a complementary measure for treatment monitoring in conjunction with averaged ADC values.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Apparent diffusion coefficient; Prostate cancer bone metastases; Texture analysis; Treatment monitoring; Treatment response

Mesh:

Year:  2018        PMID: 29571795     DOI: 10.1016/j.ejrad.2018.02.024

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  6 in total

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2.  Early Response to Chemotherapy in Malignant Pleural Mesothelioma Evaluated Using Diffusion-Weighted Magnetic Resonance Imaging: Initial Observations.

Authors:  Sebastian Curcean; Lin Cheng; Simona Picchia; Nina Tunariu; David Collins; Matthew Blackledge; Sanjay Popat; Mary O'Brien; Anna Minchom; Martin O Leach; Dow-Mu Koh
Journal:  JTO Clin Res Rep       Date:  2021-11-02

3.  Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.

Authors:  Peipei Zhang; Xiangde Min; Zhaoyan Feng; Zhen Kang; Basen Li; Wei Cai; Chanyuan Fan; Xi Yin; Jinke Xie; Wenzhi Lv; Liang Wang
Journal:  Cancer Manag Res       Date:  2021-01-28       Impact factor: 3.989

4.  Bone Cancer Detection Using Feature Extraction Based Machine Learning Model.

Authors:  Ashish Sharma; Dhirendra P Yadav; Hitendra Garg; Mukesh Kumar; Bhisham Sharma; Deepika Koundal
Journal:  Comput Math Methods Med       Date:  2021-12-20       Impact factor: 2.238

Review 5.  Radiomics in prostate cancer: an up-to-date review.

Authors:  Matteo Ferro; Ottavio de Cobelli; Gennaro Musi; Francesco Del Giudice; Giuseppe Carrieri; Gian Maria Busetto; Ugo Giovanni Falagario; Alessandro Sciarra; Martina Maggi; Felice Crocetto; Biagio Barone; Vincenzo Francesco Caputo; Michele Marchioni; Giuseppe Lucarelli; Ciro Imbimbo; Francesco Alessandro Mistretta; Stefano Luzzago; Mihai Dorin Vartolomei; Luigi Cormio; Riccardo Autorino; Octavian Sabin Tătaru
Journal:  Ther Adv Urol       Date:  2022-07-04

Review 6.  Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.

Authors:  Simon K B Spohn; Alisa S Bettermann; Fabian Bamberg; Matthias Benndorf; Michael Mix; Nils H Nicolay; Tobias Fechter; Tobias Hölscher; Radu Grosu; Arturo Chiti; Anca L Grosu; Constantinos Zamboglou
Journal:  Theranostics       Date:  2021-07-06       Impact factor: 11.556

  6 in total

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