Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617

Implications for Plan Quality Controls in Cooperative Group Trials

James A. Kavanaugh, Sarah Holler, Todd DeWees, Clifford G. Robinson, Jeffrey D. Bradley, Puneeth Iyengar, Kristin A. Higgins, Sasa Mutic, Lindsey A. Olsen

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. Methods and Materials: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617–defined dosimetric endpoints. Results: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2%, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9%, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37% (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. Conclusions: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.

Original languageEnglish (US)
JournalPractical Radiation Oncology
DOIs
StateAccepted/In press - Jan 1 2019

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Radiation Oncology
Quality Control
Radiotherapy
Lung
Clinical Trials
Organs at Risk
Non-Small Cell Lung Carcinoma
Prescriptions
Anatomy
Software
Guidelines

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617 : Implications for Plan Quality Controls in Cooperative Group Trials. / Kavanaugh, James A.; Holler, Sarah; DeWees, Todd; Robinson, Clifford G.; Bradley, Jeffrey D.; Iyengar, Puneeth; Higgins, Kristin A.; Mutic, Sasa; Olsen, Lindsey A.

In: Practical Radiation Oncology, 01.01.2019.

Research output: Contribution to journalArticle

Kavanaugh, James A. ; Holler, Sarah ; DeWees, Todd ; Robinson, Clifford G. ; Bradley, Jeffrey D. ; Iyengar, Puneeth ; Higgins, Kristin A. ; Mutic, Sasa ; Olsen, Lindsey A. / Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617 : Implications for Plan Quality Controls in Cooperative Group Trials. In: Practical Radiation Oncology. 2019.
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abstract = "Purpose: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. Methods and Materials: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37{\%} and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617–defined dosimetric endpoints. Results: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2{\%}, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9{\%}, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37{\%} (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. Conclusions: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.",
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AU - Holler, Sarah

AU - DeWees, Todd

AU - Robinson, Clifford G.

AU - Bradley, Jeffrey D.

AU - Iyengar, Puneeth

AU - Higgins, Kristin A.

AU - Mutic, Sasa

AU - Olsen, Lindsey A.

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N2 - Purpose: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. Methods and Materials: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617–defined dosimetric endpoints. Results: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2%, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9%, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37% (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. Conclusions: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.

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