Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

I. Abdelhalim; A. Alksas; H. M. Balaha; M. Badawy; M. Abou El-Ghar; N. S. Alghamdi; M. Ghazal; S. Contractor; E. V. Bogaert; D. Gondim; S. R. Silva; F. Khalifa; A. El-Baz

doi:10.1109/ISBI56570.2024.10635491

Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

I. Abdelhalim
, A. Alksas
, H. M. Balaha
, M. Badawy
, M. Abou El-Ghar
, N. S. Alghamdi
, M. Ghazal
, S. Contractor
, E. V. Bogaert
, D. Gondim
, S. R. Silva
, F. Khalifa
, A. El-Baz

Abu Dhabi University

University of Louisville
Mansoura University
Princess Nourah Bint Abdulrahman University
University of Louisville
Morgan State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.

Original language	English
Title of host publication	IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350313338
DOIs	https://doi.org/10.1109/ISBI56570.2024.10635491
State	Published - 2024
Event	21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Athens, Greece Duration: 27 May 2024 → 30 May 2024

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Country/Territory	Greece
City	Athens
Period	27/05/24 → 30/05/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Clinical Markers
Deep Learning
Demographic Markers
Hormonal Therapy
Pathology Markers
Prostate Cancer
Prostate-Specific Antigen
Transfer Learning

Access to Document

10.1109/ISBI56570.2024.10635491

Cite this

Abdelhalim, I., Alksas, A., Balaha, H. M., Badawy, M., El-Ghar, M. A., Alghamdi, N. S., Ghazal, M., Contractor, S., Bogaert, E. V., Gondim, D., Silva, S. R., Khalifa, F., & El-Baz, A. (2024). Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings. In IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI56570.2024.10635491

@inproceedings{fab1edd110874700977724cf0e37d788,

title = "Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings",

abstract = "Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5\% sensitivity and 100\% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.",

keywords = "Clinical Markers, Deep Learning, Demographic Markers, Hormonal Therapy, Pathology Markers, Prostate Cancer, Prostate-Specific Antigen, Transfer Learning",

author = "I. Abdelhalim and A. Alksas and Balaha, \{H. M.\} and M. Badawy and El-Ghar, \{M. Abou\} and Alghamdi, \{N. S.\} and M. Ghazal and S. Contractor and Bogaert, \{E. V.\} and D. Gondim and Silva, \{S. R.\} and F. Khalifa and A. El-Baz",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 ; Conference date: 27-05-2024 Through 30-05-2024",

year = "2024",

doi = "10.1109/ISBI56570.2024.10635491",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings",

address = "United States",

}

Abdelhalim, I, Alksas, A, Balaha, HM, Badawy, M, El-Ghar, MA, Alghamdi, NS, Ghazal, M, Contractor, S, Bogaert, EV, Gondim, D, Silva, SR, Khalifa, F & El-Baz, A 2024, Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings. in IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings. Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, 27/05/24. https://doi.org/10.1109/ISBI56570.2024.10635491

Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings. / Abdelhalim, I.; Alksas, A.; Balaha, H. M. et al.
IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings. IEEE Computer Society, 2024. (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment

T2 - 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024

AU - Abdelhalim, I.

AU - Alksas, A.

AU - Balaha, H. M.

AU - Badawy, M.

AU - El-Ghar, M. Abou

AU - Alghamdi, N. S.

AU - Ghazal, M.

AU - Contractor, S.

AU - Bogaert, E. V.

AU - Gondim, D.

AU - Silva, S. R.

AU - Khalifa, F.

AU - El-Baz, A.

PY - 2024

Y1 - 2024

N2 - Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.

AB - Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.

KW - Clinical Markers

KW - Deep Learning

KW - Demographic Markers

KW - Hormonal Therapy

KW - Pathology Markers

KW - Prostate Cancer

KW - Prostate-Specific Antigen

KW - Transfer Learning

UR - https://www.scopus.com/pages/publications/85203392922

U2 - 10.1109/ISBI56570.2024.10635491

DO - 10.1109/ISBI56570.2024.10635491

M3 - Conference contribution

AN - SCOPUS:85203392922

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings

PB - IEEE Computer Society

Y2 - 27 May 2024 through 30 May 2024

ER -

Abdelhalim I, Alksas A, Balaha HM, Badawy M, El-Ghar MA, Alghamdi NS et al. Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings. In IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings. IEEE Computer Society. 2024. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI56570.2024.10635491

Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this