Risk-adaptive optimization

Selective boosting of high-risk tumor subvolumes

Yusung Kim, Wolfgang A. Tome

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

Background and Purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented. Methods and Materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, δ, between TCP and NTCP on risk-adaptive optimization was investigated. Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter δ had little effect on risk-adaptive optimization. However, the clinical parameters D50 and γ50 that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.

Original languageEnglish (US)
Pages (from-to)1528-1542
Number of pages15
JournalInternational Journal of Radiation Oncology Biology Physics
Volume66
Issue number5
DOIs
StatePublished - Dec 1 2006
Externally publishedYes

Fingerprint

tumors
optimization
Neoplasms
dosage
Organs at Risk
organs
Tumor Burden
geometry
Prostatic Neoplasms
planning
cancer

Keywords

  • Biologic optimization
  • IMRT
  • Inverse planning
  • NTCP
  • TCP

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Radiation

Cite this

Risk-adaptive optimization : Selective boosting of high-risk tumor subvolumes. / Kim, Yusung; Tome, Wolfgang A.

In: International Journal of Radiation Oncology Biology Physics, Vol. 66, No. 5, 01.12.2006, p. 1528-1542.

Research output: Contribution to journalArticle

@article{18681b71b8e74d0c87f8871d20e72cd1,
title = "Risk-adaptive optimization: Selective boosting of high-risk tumor subvolumes",
abstract = "Background and Purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented. Methods and Materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, δ, between TCP and NTCP on risk-adaptive optimization was investigated. Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter δ had little effect on risk-adaptive optimization. However, the clinical parameters D50 and γ50 that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.",
keywords = "Biologic optimization, IMRT, Inverse planning, NTCP, TCP",
author = "Yusung Kim and Tome, {Wolfgang A.}",
year = "2006",
month = "12",
day = "1",
doi = "10.1016/j.ijrobp.2006.08.032",
language = "English (US)",
volume = "66",
pages = "1528--1542",
journal = "International Journal of Radiation Oncology Biology Physics",
issn = "0360-3016",
publisher = "Elsevier Inc.",
number = "5",

}

TY - JOUR

T1 - Risk-adaptive optimization

T2 - Selective boosting of high-risk tumor subvolumes

AU - Kim, Yusung

AU - Tome, Wolfgang A.

PY - 2006/12/1

Y1 - 2006/12/1

N2 - Background and Purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented. Methods and Materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, δ, between TCP and NTCP on risk-adaptive optimization was investigated. Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter δ had little effect on risk-adaptive optimization. However, the clinical parameters D50 and γ50 that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.

AB - Background and Purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented. Methods and Materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, δ, between TCP and NTCP on risk-adaptive optimization was investigated. Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter δ had little effect on risk-adaptive optimization. However, the clinical parameters D50 and γ50 that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.

KW - Biologic optimization

KW - IMRT

KW - Inverse planning

KW - NTCP

KW - TCP

UR - http://www.scopus.com/inward/record.url?scp=33751194101&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33751194101&partnerID=8YFLogxK

U2 - 10.1016/j.ijrobp.2006.08.032

DO - 10.1016/j.ijrobp.2006.08.032

M3 - Article

VL - 66

SP - 1528

EP - 1542

JO - International Journal of Radiation Oncology Biology Physics

JF - International Journal of Radiation Oncology Biology Physics

SN - 0360-3016

IS - 5

ER -