Literature DB >> 25501017

Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.

Carlo N De Cecco1, Balaji Ganeshan, Maria Ciolina, Marco Rengo, Felix G Meinel, Daniela Musio, Francesca De Felice, Nicola Raffetto, Vincenzo Tombolini, Andrea Laghi.   

Abstract

OBJECTIVE: The aim of this study was to determine whether texture features of rectal cancer on T2-weighted (T2w) magnetic resonance images can predict tumoral response in patients treated with neoadjuvant chemoradiotherapy (CRT).
MATERIALS AND METHODS: We prospectively enrolled 15 consecutive patients (6 women, 63.2 ± 13.4 years) with rectal cancer, who underwent pretreatment and midtreatment 3-T magnetic resonance imaging. Treatment protocol consisted of neoadjuvant CRT with oxaliplatin and 5-fluorouracile. Texture analysis using a filtration-histogram technique was performed using a commercial research software algorithm (TexRAD Ltd, Somerset, England, United Kingdom) on unenhanced axial T2w images by manually delineating a region of interest around the tumor outline for the largest cross-sectional area. The technique selectively filters and extracts textures at different anatomic scales followed by quantification of the histogram using kurtosis, entropy, skewness, and mean value of positive pixels. After CRT, all patients underwent complete surgical resection and the surgical specimen served as the gold standard.
RESULTS: Six patients showed pathological complete response (pCR), and 4 patients, partial response (PR). Five patients were classified as nonresponders (NRs). Pretreatment medium texture-scale quantified as kurtosis was significantly lower in the pCR subgroup in comparison with the PR + NR subgroup (P = 0.01). Midtreatment kurtosis without filtration was significantly higher in pCR in comparison with PR + NR (P = 0.045). The change in kurtosis between midtreatment and pretreatment images was significantly lower in the PR + NR subgroup compared with the pCR subgroup (P = 0.038). Pretreatment area under the receiver operating characteristic curves, to discriminate between pCR and PR + NR, was significantly higher for kurtosis (0.907, P < 0.001) compared with all other parameters. The optimal cutoff value for pretreatment kurtosis was 0.19 or less. Using this value, the sensitivity and specificity for pCR prediction were 100% and 77.8%, respectively.
CONCLUSION: Texture parameters derived from T2w images of rectal cancer have the potential to act as imaging biomarkers of tumoral response to neoadjuvant CRT.

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Year:  2015        PMID: 25501017     DOI: 10.1097/RLI.0000000000000116

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  75 in total

1.  Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy.

Authors:  Arnaud Hocquelet; Thibaut Auriac; Cynthia Perier; Clarisse Dromain; Marie Meyer; Jean-Baptiste Pinaquy; Alban Denys; Hervé Trillaud; Baudouin Denis De Senneville; Véronique Vendrely
Journal:  Eur Radiol       Date:  2018-02-05       Impact factor: 5.315

2.  Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging.

Authors:  Angelo Iannarelli; Beatrice Sacconi; Francesca Tomei; Marco Anile; Flavia Longo; Mario Bezzi; Alessandro Napoli; Luca Saba; Michele Anzidei; Giulia D'Ovidio; Roberto Scipione; Carlo Catalano
Journal:  Radiol Med       Date:  2018-01-06       Impact factor: 3.469

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.  Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

Authors:  Imon Banerjee; Sadhika Malladi; Daniela Lee; Adrien Depeursinge; Melinda Telli; Jafi Lipson; Daniel Golden; Daniel L Rubin
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-02

5.  Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer.

Authors:  Yuan Cheng; Yahong Luo; Yue Hu; Zhaohe Zhang; Xingling Wang; Qing Yu; Guanyu Liu; Enuo Cui; Tao Yu; Xiran Jiang
Journal:  Abdom Radiol (NY)       Date:  2021-07-24

6.  Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

Authors:  Eiman Al Ajmi; Behzad Forghani; Caroline Reinhold; Maryam Bayat; Reza Forghani
Journal:  Eur Radiol       Date:  2018-01-02       Impact factor: 5.315

7.  Value of combined multiparametric MRI and FDG-PET/CT to identify well-responding rectal cancer patients before the start of neoadjuvant chemoradiation.

Authors:  Niels W Schurink; Lisa A Min; Maaike Berbee; Wouter van Elmpt; Joost J M van Griethuysen; Frans C H Bakers; Sander Roberti; Simon R van Kranen; Max J Lahaye; Monique Maas; Geerard L Beets; Regina G H Beets-Tan; Doenja M J Lambregts
Journal:  Eur Radiol       Date:  2020-02-07       Impact factor: 5.315

Review 8.  Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.

Authors:  Natally Horvat; David D B Bates; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2019-11

Review 9.  Functional MR Imaging Techniques in Oncology in the Era of Personalized Medicine.

Authors:  Matthias R Benz; Hebert Alberto Vargas; Evis Sala
Journal:  Magn Reson Imaging Clin N Am       Date:  2015-09-26       Impact factor: 2.266

10.  Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings.

Authors:  Jacob Antunes; Satish Viswanath; Justin T Brady; Benjamin Crawshaw; Pablo Ros; Scott Steele; Conor P Delaney; Raj Paspulati; Joseph Willis; Anant Madabhushi
Journal:  Acad Radiol       Date:  2018-01-19       Impact factor: 3.173

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