Radiogenomics and radiotherapy response modeling.Citation formats

Standard

Radiogenomics and radiotherapy response modeling. / El Naqa, Issam ; West, Catharine.

In: Physics in Medicine and Biology, Vol. 62, No. 16, 01.08.2017, p. R179-R206.

Research output: Contribution to journal › Article › peer-review

Harvard

El Naqa, I & West, C 2017, 'Radiogenomics and radiotherapy response modeling.', Physics in Medicine and Biology, vol. 62, no. 16, pp. R179-R206. https://doi.org/10.1088/1361-6560/aa7c55

APA

El Naqa, I., & West, C. (2017). Radiogenomics and radiotherapy response modeling. Physics in Medicine and Biology, 62(16), R179-R206. https://doi.org/10.1088/1361-6560/aa7c55

Vancouver

El Naqa I, West C. Radiogenomics and radiotherapy response modeling. Physics in Medicine and Biology. 2017 Aug 1;62(16):R179-R206. https://doi.org/10.1088/1361-6560/aa7c55

Author

El Naqa, Issam ; West, Catharine. / Radiogenomics and radiotherapy response modeling. In: Physics in Medicine and Biology. 2017 ; Vol. 62, No. 16. pp. R179-R206.

Bibtex

@article{05f958904e4747b892fb1ae7717d9e56,
title = "Radiogenomics and radiotherapy response modeling.",
abstract = "Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.",
author = "{El Naqa}, Issam and Catharine West",
year = "2017",
month = aug,
day = "1",
doi = "10.1088/1361-6560/aa7c55",
language = "English",
volume = "62",
pages = "R179--R206",
journal = "Physics in Medicine and Biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd",
number = "16",

}

RIS

TY - JOUR

T1 - Radiogenomics and radiotherapy response modeling.

AU - El Naqa, Issam

AU - West, Catharine

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

AB - Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

U2 - 10.1088/1361-6560/aa7c55

DO - 10.1088/1361-6560/aa7c55

M3 - Article

VL - 62

SP - R179-R206

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 16

ER -