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Med Phys. 2011 Apr;38(4):2027-34.

Assessing software upgrades, plan properties and patient geometry using intensity modulated radiation therapy (IMRT) complexity metrics.

Author information

  • 1Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast BT9 7AB, Northern Ireland, United Kingdom. conor.mcgarry@belfasttrust.hscni.net

Abstract

PURPOSE:

The aim of this study is to compare the sensitivity of different metrics to detect differences in complexity of intensity modulated radiation therapy (IMRT) plans following upgrades, changes to planning parameters, and patient geometry. Correlations between complexity metrics are also assessed.

METHOD:

A program was developed to calculate a series of metrics used to describe the complexity of IMRT fields using monitor units (MUs) and multileaf collimator files: Modulation index (MI), modulation complexity score (MCS), and plan intensity map variation (PIMV). Each metric, including the MUs, was used to assess changes in beam complexity for six prostate patients, following upgrades in the inverse planning optimization software designed to incorporate direct aperture optimization (DAO). All beams were delivered to a 2D ionization chamber array and compared to those calculated using gamma analysis. Each complexity metric was then calculated for all beams, on a different set of six prostate IMRT patients, to assess differences between plans calculated using different minimum field sizes and different maximum segment numbers. Different geometries, including CShape, prostate, and head and neck phantoms, were also assessed using the metrics. Correlations between complexity metrics were calculated for 20 prostate IMRT patients.

RESULTS:

MU, MCS, MI, and PIMV could all detect reduced complexity following an upgrade to the optimization leaf sequencer, although only MI and MCS could detect a reduction in complexity when one-step optimization (DAO) was employed rather than two-step optimization. All metrics detected a reduction in complexity when the minimum field size was increased from 1 to 4 cm and all apart from PIMV detected reduced complexity when the number of segments was significantly reduced. All metrics apart from MI showed differences in complexity depending on the treatment site. Significant correlations exist between all metrics apart from MI and PIMV for prostate IMRT patients. Treatment deliverability appeared to be more correlated with MI and MCS than MU or PIMV.

CONCLUSIONS:

The application of complexity metrics in the IMRT treatment planning process has been demonstrated. Complexity of treatment plans can vary for different inverse planning software versions and can depend on planning parameters and the treatment site. MCS is most suitable for inclusion within the cost function to limit complexity in IMRT optimization due to its sensitivity to complexity changes and correlation to treatment deliverability.

PMID:
21626935
[PubMed - indexed for MEDLINE]
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