Literature DB >> 25768265

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness.

A Vignati1, S Mazzetti, V Giannini, F Russo, E Bollito, F Porpiglia, M Stasi, D Regge.   

Abstract

To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman ρ and AUC values on ADC were: -0.489, 0.823 (mean); -0.522, 0.821 (median); -0.569, 0.854 (10th percentile); -0.556, 0.854 (25th percentile); -0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were: -0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC (p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.

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Year:  2015        PMID: 25768265     DOI: 10.1088/0031-9155/60/7/2685

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  39 in total

1.  Apparent diffusion coefficient value is a strong predictor of unsuspected aggressiveness of prostate cancer before radical prostatectomy.

Authors:  Raphaele Renard Penna; Geraldine Cancel-Tassin; Eva Comperat; Pierre Mozer; Priscilla Léon; Justine Varinot; Morgan Roupret; Marc-Olivier Bitker; Olivier Lucidarme; Olivier Cussenot
Journal:  World J Urol       Date:  2016-02-22       Impact factor: 4.226

Review 2.  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

3.  T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.

Authors:  Gabriel Nketiah; Mattijs Elschot; Eugene Kim; Jose R Teruel; Tom W Scheenen; Tone F Bathen; Kirsten M Selnæs
Journal:  Eur Radiol       Date:  2016-12-14       Impact factor: 5.315

Review 4.  The role of radiomics in prostate cancer radiotherapy.

Authors:  Rodrigo Delgadillo; John C Ford; Matthew C Abramowitz; Alan Dal Pra; Alan Pollack; Radka Stoyanova
Journal:  Strahlenther Onkol       Date:  2020-08-21       Impact factor: 3.621

5.  Prostate cancer radiomics and the promise of radiogenomics.

Authors:  Radka Stoyanova; Mandeep Takhar; Yohann Tschudi; John C Ford; Gabriel Solórzano; Nicholas Erho; Yoganand Balagurunathan; Sanoj Punnen; Elai Davicioni; Robert J Gillies; Alan Pollack
Journal:  Transl Cancer Res       Date:  2016-08       Impact factor: 1.241

6.  A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features.

Authors:  Valentina Giannini; Simone Mazzetti; Agnese Marmo; Filippo Montemurro; Daniele Regge; Laura Martincich
Journal:  Br J Radiol       Date:  2017-07-14       Impact factor: 3.039

7.  Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

Authors:  Jussi Toivonen; Ileana Montoya Perez; Parisa Movahedi; Harri Merisaari; Marko Pesola; Pekka Taimen; Peter J Boström; Jonne Pohjankukka; Aida Kiviniemi; Tapio Pahikkala; Hannu J Aronen; Ivan Jambor
Journal:  PLoS One       Date:  2019-07-08       Impact factor: 3.240

8.  Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features.

Authors:  V Giannini; S Mazzetti; I Bertotto; C Chiarenza; S Cauda; E Delmastro; C Bracco; A Di Dia; F Leone; E Medico; A Pisacane; D Ribero; M Stasi; D Regge
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-01-13       Impact factor: 9.236

9.  Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

Authors:  Shoshana B Ginsburg; Ahmad Algohary; Shivani Pahwa; Vikas Gulani; Lee Ponsky; Hannu J Aronen; Peter J Boström; Maret Böhm; Anne-Maree Haynes; Phillip Brenner; Warick Delprado; James Thompson; Marley Pulbrock; Pekka Taimen; Robert Villani; Phillip Stricker; Ardeshir R Rastinehad; Ivan Jambor; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2016-12-19       Impact factor: 4.813

Review 10.  Molecular imaging of prostate cancer.

Authors:  Anne Marie Boustani; Darko Pucar; Lawrence Saperstein
Journal:  Br J Radiol       Date:  2018-02-01       Impact factor: 3.039

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