Literature DB >> 34826254

Association between breast cancer's prognostic factors and 3D textural features of non-contrast-enhanced T1 weighted breast MRI.

Anni Lepola1,2, Otso Arponen3,4, Hidemi Okuma1,2, Kirsi Holli-Helenius4,5, Heikki Junkkari6,7, Mervi Könönen2, Päivi Auvinen8,9, Mazen Sudah1,2, Anna Sutela1,2, Ritva Vanninen1,2.   

Abstract

OBJECTIVES: The aim of this exploratory study was to evaluate whether three-dimensional texture analysis (3D-TA) features of non-contrast-enhanced T1 weighted MRI associate with traditional prognostic factors and disease-free survival (DFS) of breast cancer.
METHODS: 3D-T1 weighted images from 78 patients with 81 malignant histopathologically verified breast lesions were retrospectively analysed using standard-size volumes of interest. Grey-level co-occurrence matrix (GLCM)-based features were selected for statistical analysis. In statistics the Mann-Whitney U and the Kruskal-Wallis tests, the Cox proportional hazards model and the Kaplan-Meier method were used.
RESULTS: Tumours with higher histological grade were significantly associated with higher contrast (1 voxel: p = 0.033, 2 voxels: p = 0.036). All the entropy parameters showed significant correlation with tumour grade (p = 0.015-0.050) but there were no statistically significant associations between other TA parameters and tumour grade. The Nottingham Prognostic Index (NPI) was correlated with contrast and sum entropy parameters. A higher sum variance TA parameter was a significant predictor of shorter DFS.
CONCLUSION: Texture parameters, assessed by 3D-TA from non-enhanced T1 weighted images, indicate tumour heterogeneity but have limited independent prognostic value. However, they are associated with tumour grade, NPI, and DFS. These parameters could be used as an adjunct to contrast-enhanced TA parameters. ADVANCES IN KNOWLEDGE: 3D-TA of non-contrast enhanced T1 weighted breast MRI associates with tumour grade, NPI, and DFS. The use of non-contrast 3D-TA parameters in adjunct with contrast-enhanced 3D-TA parameters warrants further research.

Entities:  

Mesh:

Year:  2021        PMID: 34826254      PMCID: PMC8822552          DOI: 10.1259/bjr.20210702

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  34 in total

1.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.

Authors:  G Collewet; M Strzelecki; F Mariette
Journal:  Magn Reson Imaging       Date:  2004-01       Impact factor: 2.546

Review 2.  Texture analysis of medical images.

Authors:  G Castellano; L Bonilha; L M Li; F Cendes
Journal:  Clin Radiol       Date:  2004-12       Impact factor: 2.350

3.  Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

Authors:  Weijie Chen; Maryellen L Giger; Hui Li; Ulrich Bick; Gillian M Newstead
Journal:  Magn Reson Med       Date:  2007-09       Impact factor: 4.668

4.  Magnetic resonance imaging texture analysis classification of primary breast cancer.

Authors:  S A Waugh; C A Purdie; L B Jordan; S Vinnicombe; R A Lerski; P Martin; A M Thompson
Journal:  Eur Radiol       Date:  2015-06-12       Impact factor: 5.315

5.  DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.

Authors:  Ming Fan; Hu Cheng; Peng Zhang; Xin Gao; Juan Zhang; Guoliang Shao; Lihua Li
Journal:  J Magn Reson Imaging       Date:  2017-12-08       Impact factor: 4.813

6.  Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

Authors:  Mahesh B Nagarajan; Markus B Huber; Thomas Schlossbauer; Gerda Leinsinger; Andrzej Krol; Axel Wismüller
Journal:  J Med Biol Eng       Date:  2013-01-01       Impact factor: 1.553

7.  Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Hon J Yu; Yong Chu; Orhan Nalcioglu; Min-Ying Su
Journal:  Acad Radiol       Date:  2008-12       Impact factor: 3.173

8.  MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study.

Authors:  Kirsi Holli-Helenius; Annukka Salminen; Irina Rinta-Kiikka; Ilkka Koskivuo; Nina Brück; Pia Boström; Riitta Parkkola
Journal:  BMC Med Imaging       Date:  2017-12-29       Impact factor: 1.930

9.  Plasticity and intratumoural heterogeneity of cell surface antigen expression in breast cancer.

Authors:  Ján Remšík; Radek Fedr; Jiří Navrátil; Lucia Binó; Eva Slabáková; Pavel Fabian; Marek Svoboda; Karel Souček
Journal:  Br J Cancer       Date:  2018-02-20       Impact factor: 7.640

10.  Diffusion-Weighted Imaging in 3.0 Tesla Breast MRI: Diagnostic Performance and Tumor Characterization Using Small Subregions vs. Whole Tumor Regions of Interest.

Authors:  Otso Arponen; Otso Arponent; Mazen Sudah; Amro Masarwah; Mikko Taina; Suvi Rautiainen; Mervi Könönen; Reijo Sironen; Veli-Matti Kosma; Anna Sutela; Juhana Hakumäki; Ritva Vanninen
Journal:  PLoS One       Date:  2015-10-12       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.