Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer

Bonou Malomon Aime; Topanou Roland Guy Boniface; Hounsossou Cocou Hubert; Gbossa Eddy Hans; Dossou Julien; Biaou Olivier

doi:doi:10.11648/j.ijmi.20200803.11

Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer

Bonou Malomon Aime, Topanou Roland Guy Boniface, Hounsossou Cocou Hubert, Gbossa Eddy Hans, Dossou Julien, Biaou Olivier

Published in International Journal of Medical Imaging (Volume 8, Issue 3)

Received: 15 June 2020 Accepted: 8 July 2020 Published: 17 July 2020

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Abstract

High grade breast cancer is recognized as more aggressive cancer type and is the worst survival prognostic. To explore the association of quantitative features extracted from mammograms with histological high-grade breast cancer. We conducted a retrospective study using an open source data got from figshare repository. These anonymized data were collected and used for a study approved by the institutional review board. Cranio-Caudal (CC) and Medio-lateral (MLO) mammograms and their tumor segmented images from 66 patients subdivided in two groups high histological grade (n=23) low-grade (low and intermediate, n=41). From breast cancer image segmentation, we extracted 480 features using python software radiomics package Pyradiomics 2.2. With the features extracted from CC and MLO images, we used them separately for histological high-grade breast, relevant feature selection. We performed univariate feature selection based on ANOVA test using machine learning python package: sklearn. A feature was considered relevant when P value is at least 0.05. At the end we represented the boxplot of the distribution of the low-and high-grade subject using each relevant feature selected. Twenty (20) CC images features were selected, seventen (17) were based on wavelets and three (3) were from original image. Their p values were ranged between 0.017 and 0.046. In the case of MLO features, four (04) relevant features were exclusively based on wavelets with 0.046 as the maximum p-value and 0.006 as minimum. These results suggested mammogram quantitative feature based on wavelets will be useful for high-grade breast cancer identification on mammographic image. In this study we explored the association between IBSI 2D quantitative features from mammogram with the histological high-grade breast cancer. Finally, we recorded twenty (20) relevant features from CC projection and four for MLO mammogram projection. Wavelets based features were more represented in relevant quantitative feature.

Published in	International Journal of Medical Imaging (Volume 8, Issue 3)
DOI	10.11648/j.ijmi.20200803.11
Page(s)	39-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Quantitative Feature, Mammography, High Grade Cancer, Breast

References

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APA Style

Bonou Malomon Aime, Topanou Roland Guy Boniface, Hounsossou Cocou Hubert, Gbossa Eddy Hans, Dossou Julien, et al. (2020). Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. International Journal of Medical Imaging, 8(3), 39-44. https://doi.org/10.11648/j.ijmi.20200803.11

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ACS Style

Bonou Malomon Aime; Topanou Roland Guy Boniface; Hounsossou Cocou Hubert; Gbossa Eddy Hans; Dossou Julien, et al. Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. Int. J. Med. Imaging 2020, 8(3), 39-44. doi: 10.11648/j.ijmi.20200803.11

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AMA Style

Bonou Malomon Aime, Topanou Roland Guy Boniface, Hounsossou Cocou Hubert, Gbossa Eddy Hans, Dossou Julien, et al. Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. Int J Med Imaging. 2020;8(3):39-44. doi: 10.11648/j.ijmi.20200803.11

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@article{10.11648/j.ijmi.20200803.11,
  author = {Bonou Malomon Aime and Topanou Roland Guy Boniface and Hounsossou Cocou Hubert and Gbossa Eddy Hans and Dossou Julien and Biaou Olivier},
  title = {Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer},
  journal = {International Journal of Medical Imaging},
  volume = {8},
  number = {3},
  pages = {39-44},
  doi = {10.11648/j.ijmi.20200803.11},
  url = {https://doi.org/10.11648/j.ijmi.20200803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20200803.11},
  abstract = {High grade breast cancer is recognized as more aggressive cancer type and is the worst survival prognostic. To explore the association of quantitative features extracted from mammograms with histological high-grade breast cancer. We conducted a retrospective study using an open source data got from figshare repository. These anonymized data were collected and used for a study approved by the institutional review board. Cranio-Caudal (CC) and Medio-lateral (MLO) mammograms and their tumor segmented images from 66 patients subdivided in two groups high histological grade (n=23) low-grade (low and intermediate, n=41). From breast cancer image segmentation, we extracted 480 features using python software radiomics package Pyradiomics 2.2. With the features extracted from CC and MLO images, we used them separately for histological high-grade breast, relevant feature selection. We performed univariate feature selection based on ANOVA test using machine learning python package: sklearn. A feature was considered relevant when P value is at least 0.05. At the end we represented the boxplot of the distribution of the low-and high-grade subject using each relevant feature selected. Twenty (20) CC images features were selected, seventen (17) were based on wavelets and three (3) were from original image. Their p values were ranged between 0.017 and 0.046. In the case of MLO features, four (04) relevant features were exclusively based on wavelets with 0.046 as the maximum p-value and 0.006 as minimum. These results suggested mammogram quantitative feature based on wavelets will be useful for high-grade breast cancer identification on mammographic image. In this study we explored the association between IBSI 2D quantitative features from mammogram with the histological high-grade breast cancer. Finally, we recorded twenty (20) relevant features from CC projection and four for MLO mammogram projection. Wavelets based features were more represented in relevant quantitative feature.},
 year = {2020}
}

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TY - JOUR
T1 - Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer
AU - Bonou Malomon Aime
AU - Topanou Roland Guy Boniface
AU - Hounsossou Cocou Hubert
AU - Gbossa Eddy Hans
AU - Dossou Julien
AU - Biaou Olivier
Y1 - 2020/07/17
PY - 2020
N1 - https://doi.org/10.11648/j.ijmi.20200803.11
DO - 10.11648/j.ijmi.20200803.11
T2 - International Journal of Medical Imaging
JF - International Journal of Medical Imaging
JO - International Journal of Medical Imaging
SP - 39
EP - 44
PB - Science Publishing Group
SN - 2330-832X
UR - https://doi.org/10.11648/j.ijmi.20200803.11
AB - High grade breast cancer is recognized as more aggressive cancer type and is the worst survival prognostic. To explore the association of quantitative features extracted from mammograms with histological high-grade breast cancer. We conducted a retrospective study using an open source data got from figshare repository. These anonymized data were collected and used for a study approved by the institutional review board. Cranio-Caudal (CC) and Medio-lateral (MLO) mammograms and their tumor segmented images from 66 patients subdivided in two groups high histological grade (n=23) low-grade (low and intermediate, n=41). From breast cancer image segmentation, we extracted 480 features using python software radiomics package Pyradiomics 2.2. With the features extracted from CC and MLO images, we used them separately for histological high-grade breast, relevant feature selection. We performed univariate feature selection based on ANOVA test using machine learning python package: sklearn. A feature was considered relevant when P value is at least 0.05. At the end we represented the boxplot of the distribution of the low-and high-grade subject using each relevant feature selected. Twenty (20) CC images features were selected, seventen (17) were based on wavelets and three (3) were from original image. Their p values were ranged between 0.017 and 0.046. In the case of MLO features, four (04) relevant features were exclusively based on wavelets with 0.046 as the maximum p-value and 0.006 as minimum. These results suggested mammogram quantitative feature based on wavelets will be useful for high-grade breast cancer identification on mammographic image. In this study we explored the association between IBSI 2D quantitative features from mammogram with the histological high-grade breast cancer. Finally, we recorded twenty (20) relevant features from CC projection and four for MLO mammogram projection. Wavelets based features were more represented in relevant quantitative feature.
VL - 8
IS - 3
ER -

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Author Information

Bonou Malomon Aime

Non-Communicable Diseases and Cancer Research Unit, Laboratory of Applied Biology Research, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Topanou Roland Guy Boniface

Non-Communicable Diseases and Cancer Research Unit, Laboratory of Applied Biology Research, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Hounsossou Cocou Hubert

Non-Communicable Diseases and Cancer Research Unit, Laboratory of Applied Biology Research, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Gbossa Eddy Hans

Non-Communicable Diseases and Cancer Research Unit, Laboratory of Applied Biology Research, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Dossou Julien

Non-Communicable Diseases and Cancer Research Unit, Laboratory of Applied Biology Research, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Biaou Olivier

Medical Imaging Unit of "National Hospital and University center H. K. Maga", Cotonou, Benin

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Plain Text BibTeX RIS

APA Style

Bonou Malomon Aime, Topanou Roland Guy Boniface, Hounsossou Cocou Hubert, Gbossa Eddy Hans, Dossou Julien, et al. (2020). Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. International Journal of Medical Imaging, 8(3), 39-44. https://doi.org/10.11648/j.ijmi.20200803.11

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ACS Style

Bonou Malomon Aime; Topanou Roland Guy Boniface; Hounsossou Cocou Hubert; Gbossa Eddy Hans; Dossou Julien, et al. Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. Int. J. Med. Imaging 2020, 8(3), 39-44. doi: 10.11648/j.ijmi.20200803.11

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AMA Style

Bonou Malomon Aime, Topanou Roland Guy Boniface, Hounsossou Cocou Hubert, Gbossa Eddy Hans, Dossou Julien, et al. Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer. Int J Med Imaging. 2020;8(3):39-44. doi: 10.11648/j.ijmi.20200803.11

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@article{10.11648/j.ijmi.20200803.11,
  author = {Bonou Malomon Aime and Topanou Roland Guy Boniface and Hounsossou Cocou Hubert and Gbossa Eddy Hans and Dossou Julien and Biaou Olivier},
  title = {Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer},
  journal = {International Journal of Medical Imaging},
  volume = {8},
  number = {3},
  pages = {39-44},
  doi = {10.11648/j.ijmi.20200803.11},
  url = {https://doi.org/10.11648/j.ijmi.20200803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20200803.11},
  abstract = {High grade breast cancer is recognized as more aggressive cancer type and is the worst survival prognostic. To explore the association of quantitative features extracted from mammograms with histological high-grade breast cancer. We conducted a retrospective study using an open source data got from figshare repository. These anonymized data were collected and used for a study approved by the institutional review board. Cranio-Caudal (CC) and Medio-lateral (MLO) mammograms and their tumor segmented images from 66 patients subdivided in two groups high histological grade (n=23) low-grade (low and intermediate, n=41). From breast cancer image segmentation, we extracted 480 features using python software radiomics package Pyradiomics 2.2. With the features extracted from CC and MLO images, we used them separately for histological high-grade breast, relevant feature selection. We performed univariate feature selection based on ANOVA test using machine learning python package: sklearn. A feature was considered relevant when P value is at least 0.05. At the end we represented the boxplot of the distribution of the low-and high-grade subject using each relevant feature selected. Twenty (20) CC images features were selected, seventen (17) were based on wavelets and three (3) were from original image. Their p values were ranged between 0.017 and 0.046. In the case of MLO features, four (04) relevant features were exclusively based on wavelets with 0.046 as the maximum p-value and 0.006 as minimum. These results suggested mammogram quantitative feature based on wavelets will be useful for high-grade breast cancer identification on mammographic image. In this study we explored the association between IBSI 2D quantitative features from mammogram with the histological high-grade breast cancer. Finally, we recorded twenty (20) relevant features from CC projection and four for MLO mammogram projection. Wavelets based features were more represented in relevant quantitative feature.},
 year = {2020}
}

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