Machine Learning for Image-Based Radiotherapy Outcome Prediction

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Standard

Machine Learning for Image-Based Radiotherapy Outcome Prediction. / Ibragimov, Bulat.

Artificial Intelligence in Radiation Oncology and Biomedical Physics. red. / Gilmer Valdes; Lei Xing. CRC Press, 2023. s. 25-52.

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Harvard

Ibragimov, B 2023, Machine Learning for Image-Based Radiotherapy Outcome Prediction. i G Valdes & L Xing (red), Artificial Intelligence in Radiation Oncology and Biomedical Physics. CRC Press, s. 25-52. https://doi.org/10.1201/9781003094333-2

APA

Ibragimov, B. (2023). Machine Learning for Image-Based Radiotherapy Outcome Prediction. I G. Valdes, & L. Xing (red.), Artificial Intelligence in Radiation Oncology and Biomedical Physics (s. 25-52). CRC Press. https://doi.org/10.1201/9781003094333-2

Vancouver

Ibragimov B. Machine Learning for Image-Based Radiotherapy Outcome Prediction. I Valdes G, Xing L, red., Artificial Intelligence in Radiation Oncology and Biomedical Physics. CRC Press. 2023. s. 25-52 https://doi.org/10.1201/9781003094333-2

Author

Ibragimov, Bulat. / Machine Learning for Image-Based Radiotherapy Outcome Prediction. Artificial Intelligence in Radiation Oncology and Biomedical Physics. red. / Gilmer Valdes ; Lei Xing. CRC Press, 2023. s. 25-52

Bibtex

@inbook{a2d21d0f90934bad8a58dfb40259b43a,
title = "Machine Learning for Image-Based Radiotherapy Outcome Prediction",
abstract = "Medical images represent the main source of information for radiotherapy (RT) planning. Historically, medical images have been utilized to qualitatively and quantitatively access the location and size of the tumor, the shape and positions of the surrounding healthy organs, and the spatial relationships between these organs and the tumor. The images, however, contain much more information than it is usually extracted during RT planning. The appearance of the tumor and healthy organs can potentially give insights about the cancer aggressiveness, risks associated with the treatment, and most likely treatment outcomes. The challenge remains to correctly identify such predictive image biomarkers, and map their predictive powers to the RT outcomes of interest. This book chapter summarizes the existing machine learning solutions for image-based RT outcome prediction. The chapter critically access different aspects of existing solutions, including the type of utilized imaging modality, biomarker extraction protocol, machine learning algorithm, and algorithm performance evaluation. This chapter also critically summarizes the conclusions of the existing studies, and highlights the promising directions for future research.",
author = "Bulat Ibragimov",
year = "2023",
doi = "10.1201/9781003094333-2",
language = "English",
isbn = "9780367538101",
pages = "25--52",
editor = "Gilmer Valdes and Lei Xing",
booktitle = "Artificial Intelligence in Radiation Oncology and Biomedical Physics",
publisher = "CRC Press",

}

RIS

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T1 - Machine Learning for Image-Based Radiotherapy Outcome Prediction

AU - Ibragimov, Bulat

PY - 2023

Y1 - 2023

N2 - Medical images represent the main source of information for radiotherapy (RT) planning. Historically, medical images have been utilized to qualitatively and quantitatively access the location and size of the tumor, the shape and positions of the surrounding healthy organs, and the spatial relationships between these organs and the tumor. The images, however, contain much more information than it is usually extracted during RT planning. The appearance of the tumor and healthy organs can potentially give insights about the cancer aggressiveness, risks associated with the treatment, and most likely treatment outcomes. The challenge remains to correctly identify such predictive image biomarkers, and map their predictive powers to the RT outcomes of interest. This book chapter summarizes the existing machine learning solutions for image-based RT outcome prediction. The chapter critically access different aspects of existing solutions, including the type of utilized imaging modality, biomarker extraction protocol, machine learning algorithm, and algorithm performance evaluation. This chapter also critically summarizes the conclusions of the existing studies, and highlights the promising directions for future research.

AB - Medical images represent the main source of information for radiotherapy (RT) planning. Historically, medical images have been utilized to qualitatively and quantitatively access the location and size of the tumor, the shape and positions of the surrounding healthy organs, and the spatial relationships between these organs and the tumor. The images, however, contain much more information than it is usually extracted during RT planning. The appearance of the tumor and healthy organs can potentially give insights about the cancer aggressiveness, risks associated with the treatment, and most likely treatment outcomes. The challenge remains to correctly identify such predictive image biomarkers, and map their predictive powers to the RT outcomes of interest. This book chapter summarizes the existing machine learning solutions for image-based RT outcome prediction. The chapter critically access different aspects of existing solutions, including the type of utilized imaging modality, biomarker extraction protocol, machine learning algorithm, and algorithm performance evaluation. This chapter also critically summarizes the conclusions of the existing studies, and highlights the promising directions for future research.

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DO - 10.1201/9781003094333-2

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AN - SCOPUS:85164019870

SN - 9780367538101

SN - 9780367556198

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BT - Artificial Intelligence in Radiation Oncology and Biomedical Physics

A2 - Valdes, Gilmer

A2 - Xing, Lei

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