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SU‐F‐T‐605: Implementation of the Conformity/Gradient Index (CGI) to Intracranial, Linac‐Based Stereotactic Plans to Evaluate Possible Improvements in Treatment Planning
Author(s) -
Sandu A,
Thompson S,
Ayan A,
Woollard J,
Gupta N
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4956790
Subject(s) - nuclear medicine , computer science , isocenter , radiation treatment planning , lesion , linear particle accelerator , metric (unit) , medicine , medical physics , radiology , physics , optics , surgery , imaging phantom , radiation therapy , beam (structure) , operations management , economics
Purpose: To retrospectively evaluate the planning quality of a sample of intracranial, linac‐based, dynamic arc MLC and VMAT stereotactic plans by calculating the effective gradient, Conformity Score (CGIc) and Gradient Score Index (CGIg), and determine if the Conformity/Gradient Index (CGI) may be a useful metric to guide our current treatment planning process. Methods: We evaluated single lesion VMAT, single lesion dynamic arc, and single isocenter, 2‐lesion linac‐based, intracranial, VMAT treatment plans originally created in Eclipse™. The CGI index was calculated for each lesion using the Target Volume (TV), the Prescription Isodose Volume (PIV), and the 50% Isodose Volume. The effective gradient was obtained directly from the Eclipse “gradient measure.” Results: From the single lesion cases, target volumes of ≤ ∼2cc reliably achieved an effective gradient of ∼3mm and a CGI > 95. For lesions ≤ ∼20cc, the majority of cases have an effective gradient of <5mm and a CGI > 90‐95. For our multilesion data, we determined that lesions separated by <2cm typically had CGI values of 75‐85, lesions separated by 2cm‐3cm typically had CGI values > 80‐85, and lesions separated by >3cm can have CGI over 90. Conclusion: The CGI metric is a simple tool that can be applied to MLC‐based stereotactic planning, requires only three pieces of data, and quickly allows planners to evaluate and determine if a plan is suboptimal. Our data identified several cases that might be improved with additional effort and/or re‐planning, but the majority of the treatment plans evaluated fell within a desirable CGI range. Cases deemed suboptimal are currently under investigation and will be re‐planned to determine if CGI values can be improved.