Literature DB >> 24840393

Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Jose R Teruel1, Mariann G Heldahl, Pål E Goa, Martin Pickles, Steinar Lundgren, Tone F Bathen, Peter Gibbs.   

Abstract

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.
Copyright © 2014 John Wiley & Sons, Ltd.

Entities:  

Keywords:  DCE-MRI; locally advanced breast cancer; neoadjuvant chemotherapy; texture analysis; treatment response

Mesh:

Substances:

Year:  2014        PMID: 24840393     DOI: 10.1002/nbm.3132

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  48 in total

1.  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 2.  Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.

Authors:  E Sala; E Mema; Y Himoto; H Veeraraghavan; J D Brenton; A Snyder; B Weigelt; H A Vargas
Journal:  Clin Radiol       Date:  2016-10-11       Impact factor: 2.350

3.  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

4.  A novel framework for evaluating the image accuracy of dynamic MRI and the application on accelerated breast DCE MRI.

Authors:  Yuan Le; Marcel Dominik Nickel; Stephan Kannengiesser; Berthold Kiefer; Bruce Spottiswoode; Brian Dale; Victor Soon; Chen Lin
Journal:  MAGMA       Date:  2017-09-11       Impact factor: 2.310

5.  Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer.

Authors:  Jason Ostenson; Akshat C Pujara; Artem Mikheev; Linda Moy; Sungheon G Kim; Amy N Melsaether; Komal Jhaveri; Sylvia Adams; David Faul; Christopher Glielmi; Christian Geppert; Thorsten Feiweier; Kimberly Jackson; Gene Y Cho; Fernando E Boada; Eric E Sigmund
Journal:  Magn Reson Med       Date:  2016-10-25       Impact factor: 4.668

6.  Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients.

Authors:  Ahmad Chaddad; Camel Tanougast
Journal:  Med Biol Eng Comput       Date:  2016-03-10       Impact factor: 2.602

7.  Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.

Authors:  Ahmad Chaddad; Siham Sabri; Tamim Niazi; Bassam Abdulkarim
Journal:  Med Biol Eng Comput       Date:  2018-06-19       Impact factor: 2.602

8.  MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.

Authors:  Ankush Bhatia; Maxwell Birger; Harini Veeraraghavan; Hyemin Um; Florent Tixier; Anna Sophia McKenney; Marina Cugliari; Annalise Caviasco; Angelica Bialczak; Rachna Malani; Jessica Flynn; Zhigang Zhang; T Jonathan Yang; Bianca D Santomasso; Alexander N Shoushtari; Robert J Young
Journal:  Neuro Oncol       Date:  2019-12-17       Impact factor: 12.300

9.  Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients.

Authors:  Saki Kamiya; Hiroko Satake; Yoko Hayashi; Satoko Ishigaki; Rintaro Ito; Mariko Kawamura; Toshiaki Taoka; Shingo Iwano; Shinji Naganawa
Journal:  Breast Cancer       Date:  2021-09-16       Impact factor: 4.239

10.  Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer.

Authors:  Qin Li; Qin Xiao; Jianwei Li; Zhe Wang; He Wang; Yajia Gu
Journal:  Cancer Manag Res       Date:  2021-06-28       Impact factor: 3.989

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