Reduced‐order constrained optimization (ROCO): Clinical application to head‐and‐neck IMRT. Issue 2 (28th January 2013)

• Record Type: Journal Article
• Authors: Rivera, Linda; Yorke, Ellen; Kowalski, Alex; Yang, Jie; Radke, Richard J.; Jackson, Andrew
• Is Part Of: Medical physics. Volume 40:Issue 2(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-01-28
• Subjects: Optimization; lung -- optimisation -- principal component analysis -- radiation therapy -- tumours; IMRT -- head‐and‐neck -- constrained optimization -- dimensionality reduction; Radiation therapy; Dosimetry -- Intensity modulated radiation therapy -- Optimization -- Cancer -- Anatomy -- Medical treatment planning -- Lungs -- Larynx -- Radiation therapy -- Therapeutics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4788653
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head‐and‐neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial‐and‐error interaction using the proposed approach. Methods: : The ROCO method used for H&N IMRT planning consists of three major steps. First, the intensity space of treatment plans is sampled by solving a series of unconstrained optimization problems with a parameter range based on previously treated patient data. Second, the dominant modes in the intensity space are estimated by dimensionality reduction using principal component analysis (PCA). Third, a constrained optimization problem over this basis is quickly solved to find an IMRT plan that meets organ‐at‐risk (OAR) and target coverage constraints. The quality of the plan is assessed using evaluation tools within Memorial Sloan‐Kettering Cancer Center (MSKCC)'s treatment planning system (TPS). Results: : The authors generated ten H&N IMRT plans for previously treated patients using the ROCO method and processed them for deliverability by a dynamic multileaf collimator (DMLC). The authors quantitatively compared the ROCO plans to the previously achieved clinical plans using the TPS tools used at MSKCC, including DVH and isodose contour analysis, and concluded that the ROCO plans would be clinically acceptable. In our current implementation, ROCO H&N plans can be generated using about 1.6 h of offline computation followed by 5–15 min of semiautomatic planning time. Additionally, the authors conducted a live session for a plan designated by MSKCC performed together with an expert H&N planner. A technical assistant set up the first two steps, which were performed without further human interaction, and then collaborated in a virtual meeting with the expert planner to perform the third (constrained optimization) step. The expert planner performed in‐depth analysis of the resulting ROCO plan and deemed it to be clinically acceptable and in some aspects superior to the clinical plan. This entire process took 135 min including two constrained optimization runs, in comparison to the estimated 4 h that would have been required using traditional clinical planning tools. Conclusions: : The H&N site is very challenging for IMRT planning, due to several levels of prescription and a large, variable number (6–20) of OARs that depend on the location of the tumor. ROCO for H&N shows promise in generating clinically acceptable plans both more quickly and with substantially less human interaction.