Literature DB >> 32528211

Radiomics Analysis of MRI for Predicting Molecular Subtypes of Breast Cancer in Young Women.

Qinmei Li1,2, James Dormer1, Priyanka Daryani3, Deji Chen2, Zhenfeng Zhang2, Baowei Fei1,3.   

Abstract

Breast cancer in young women is commonly aggressive, in part because the proportion of high-grade, triple-negative (TN) tumor is too high. There are certain limitations in the detection of biopsies or surgical specimens which only select part of tumor sample tissue and ignore the possible heterogeneity of tumors. In clinical practice, MRI is used for the diagnosis of breast cancer. MRI-based radiomics is a developing approach that may provide not only the diagnostic value for breast cancer but also the predictive or prognostic associations between the images and biological characteristics. In this work, we used radiomics methods to analyze MR images of breast cancer in 53 young women, and correlated the radiomics data with molecular subtypes. The results indicated a significant difference between TN type and non-TN type of breast cancer in young women on the radiomics features based on T2-weighted MR images. This may be helpful for the identification of TN type and guiding the therapeutic strategies.

Entities:  

Year:  2019        PMID: 32528211      PMCID: PMC7289058          DOI: 10.1117/12.2512056

Source DB:  PubMed          Journal:  Proc SPIE Int Soc Opt Eng        ISSN: 0277-786X


  21 in total

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4.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

Authors:  Thibaud P Coroller; Patrick Grossmann; Ying Hou; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Gretchen Hermann; Philippe Lambin; Benjamin Haibe-Kains; Raymond H Mak; Hugo J W L Aerts
Journal:  Radiother Oncol       Date:  2015-03-04       Impact factor: 6.280

5.  Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.

Authors:  Lars J Grimm; Jing Zhang; Maciej A Mazurowski
Journal:  J Magn Reson Imaging       Date:  2015-03-17       Impact factor: 4.813

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Authors:  Rachel A Freedman; Ann H Partridge
Journal:  Clin Cancer Res       Date:  2017-06-01       Impact factor: 12.531

7.  Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.

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Journal:  Ann Oncol       Date:  2011-06-27       Impact factor: 32.976

8.  Robust Radiomics feature quantification using semiautomatic volumetric segmentation.

Authors:  Chintan Parmar; Emmanuel Rios Velazquez; Ralph Leijenaar; Mohammed Jermoumi; Sara Carvalho; Raymond H Mak; Sushmita Mitra; B Uma Shankar; Ron Kikinis; Benjamin Haibe-Kains; Philippe Lambin; Hugo J W L Aerts
Journal:  PLoS One       Date:  2014-07-15       Impact factor: 3.240

9.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

10.  Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer.

Authors:  Constance A Owens; Christine B Peterson; Chad Tang; Eugene J Koay; Wen Yu; Dennis S Mackin; Jing Li; Mohammad R Salehpour; David T Fuentes; Laurence E Court; Jinzhong Yang
Journal:  PLoS One       Date:  2018-10-04       Impact factor: 3.240

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