Using a knowledge-based planning solution to select patients for proton therapy

Alexander R. Delaney, Max Dahele, Jim P. Tol, Ingrid T. Kuijper, Ben J. Slotman, Wilko F.A.R. Verbakel

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background and purpose Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Material and methods ModelPROT and ModelPHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2 Gy, on average. ΔPrediction ≥ 6 Gy correctly selected 4 of 5 patients for protons. Conclusions Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.

Original languageEnglish
Pages (from-to)263-270
Number of pages8
JournalRadiotherapy and Oncology
Volume124
Issue number2
DOIs
Publication statusPublished - 1 Aug 2017

Cite this

@article{836f23072838453ba80fae518f6b4f80,
title = "Using a knowledge-based planning solution to select patients for proton therapy",
abstract = "Background and purpose Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Material and methods ModelPROT and ModelPHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2 Gy, on average. ΔPrediction ≥ 6 Gy correctly selected 4 of 5 patients for protons. Conclusions Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.",
keywords = "Head and neck cancer, Knowledge-based planning, Patient selection, Proton therapy",
author = "Delaney, {Alexander R.} and Max Dahele and Tol, {Jim P.} and Kuijper, {Ingrid T.} and Slotman, {Ben J.} and Verbakel, {Wilko F.A.R.}",
year = "2017",
month = "8",
day = "1",
doi = "10.1016/j.radonc.2017.03.020",
language = "English",
volume = "124",
pages = "263--270",
journal = "Radiotherapy and Oncology",
issn = "0167-8140",
publisher = "Elsevier Ireland Ltd",
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}

Using a knowledge-based planning solution to select patients for proton therapy. / Delaney, Alexander R.; Dahele, Max; Tol, Jim P.; Kuijper, Ingrid T.; Slotman, Ben J.; Verbakel, Wilko F.A.R.

In: Radiotherapy and Oncology, Vol. 124, No. 2, 01.08.2017, p. 263-270.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Using a knowledge-based planning solution to select patients for proton therapy

AU - Delaney, Alexander R.

AU - Dahele, Max

AU - Tol, Jim P.

AU - Kuijper, Ingrid T.

AU - Slotman, Ben J.

AU - Verbakel, Wilko F.A.R.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Background and purpose Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Material and methods ModelPROT and ModelPHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2 Gy, on average. ΔPrediction ≥ 6 Gy correctly selected 4 of 5 patients for protons. Conclusions Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.

AB - Background and purpose Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Material and methods ModelPROT and ModelPHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2 Gy, on average. ΔPrediction ≥ 6 Gy correctly selected 4 of 5 patients for protons. Conclusions Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.

KW - Head and neck cancer

KW - Knowledge-based planning

KW - Patient selection

KW - Proton therapy

UR - http://www.scopus.com/inward/record.url?scp=85017359089&partnerID=8YFLogxK

U2 - 10.1016/j.radonc.2017.03.020

DO - 10.1016/j.radonc.2017.03.020

M3 - Article

VL - 124

SP - 263

EP - 270

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

SN - 0167-8140

IS - 2

ER -