Literature DB >> 28980886

Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy.

Foucauld Chamming's1, Yoshiko Ueno1, Romuald Ferré1, Ellen Kao1, Anne-Sophie Jannot1, Jaron Chong1, Atilla Omeroglu1, Benoît Mesurolle1, Caroline Reinhold1, Benoit Gallix1.   

Abstract

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. © RSNA, 2017.

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Year:  2017        PMID: 28980886     DOI: 10.1148/radiol.2017170143

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  41 in total

Review 1.  Image-based biomarkers for solid tumor quantification.

Authors:  Peter Savadjiev; Jaron Chong; Anthony Dohan; Vincent Agnus; Reza Forghani; Caroline Reinhold; Benoit Gallix
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

2.  Dynamic Contrast-Enhanced MRI Evaluation of Pathologic Complete Response in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancer After HER2-Targeted Therapy.

Authors:  Laura Heacock; Alana Lewin; Abimbola Ayoola; Melanie Moccaldi; James S Babb; Sungheon G Kim; Linda Moy
Journal:  Acad Radiol       Date:  2019-08-20       Impact factor: 3.173

3.  Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy.

Authors:  Qianqian Xiong; Xuezhi Zhou; Zhenyu Liu; Chuqian Lei; Ciqiu Yang; Mei Yang; Liulu Zhang; Teng Zhu; Xiaosheng Zhuang; Changhong Liang; Zaiyi Liu; Jie Tian; Kun Wang
Journal:  Clin Transl Oncol       Date:  2019-04-11       Impact factor: 3.405

Review 4.  Precision diagnostics based on machine learning-derived imaging signatures.

Authors:  Christos Davatzikos; Aristeidis Sotiras; Yong Fan; Mohamad Habes; Guray Erus; Saima Rathore; Spyridon Bakas; Rhea Chitalia; Aimilia Gastounioti; Despina Kontos
Journal:  Magn Reson Imaging       Date:  2019-05-06       Impact factor: 2.546

5.  Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Authors:  Elizabeth Hope Cain; Ashirbani Saha; Michael R Harowicz; Jeffrey R Marks; P Kelly Marcom; Maciej A Mazurowski
Journal:  Breast Cancer Res Treat       Date:  2018-10-16       Impact factor: 4.872

6.  Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.

Authors:  Tiantian Bian; Zengjie Wu; Qing Lin; Haibo Wang; Yaqiong Ge; Shaofeng Duan; Guangming Fu; Chunxiao Cui; Xiaohui Su
Journal:  Br J Radiol       Date:  2020-09-02       Impact factor: 3.039

7.  Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma.

Authors:  Amir A Borhani; Rohit Dewan; Alessandro Furlan; Natalie Seiser; Amer H Zureikat; Aatur D Singhi; Brian Boone; Nathan Bahary; Melissa E Hogg; Michael Lotze; Herbert J Zeh; Mitchell E Tublin
Journal:  AJR Am J Roentgenol       Date:  2019-12-04       Impact factor: 3.959

8.  Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy.

Authors:  Jia Wu; Guohong Cao; Xiaoli Sun; Juheon Lee; Daniel L Rubin; Sandy Napel; Allison W Kurian; Bruce L Daniel; Ruijiang Li
Journal:  Radiology       Date:  2018-05-01       Impact factor: 11.105

9.  Texture analysis based on quantitative magnetic resonance imaging to assess kidney function: a preliminary study.

Authors:  Gumuyang Zhang; Yan Liu; Hao Sun; Lili Xu; Jianqing Sun; Jing An; Hailong Zhou; Yanhan Liu; Limeng Chen; Zhengyu Jin
Journal:  Quant Imaging Med Surg       Date:  2021-04

10.  MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.

Authors:  Mitsuteru Tsuchiya; Takayuki Masui; Kazuma Terauchi; Takahiro Yamada; Motoyuki Katyayama; Shintaro Ichikawa; Yoshifumi Noda; Satoshi Goshima
Journal:  Eur Radiol       Date:  2022-01-19       Impact factor: 5.315

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