On correlations in IMRT planning aims

Roy, Arkajyoti; Das, Indra J.; Nohadani, Omid

doi:10.1120/jacmp.v17i6.6411

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…All target and OAR volumes were delineated and approved by the same physician to reduce variability in contouring and minimize subjective preferences in planning criteria. 20 Outlier detection was performed on the anatomic data to identify inconsistencies in contouring, and resulted in the redelineation of 3 plans. Treatment planning was performed using Pinnacle 3 (Philips) versions 6.2b to 9.10.…”

Section: Methodsmentioning

confidence: 99%

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality

Roy

Cutright

Gopalakrishnan

et al. 2020

Advances in Radiation Oncology

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This study aimed to develop a quality control framework for intensity modulated radiation therapy plan evaluations that can account for variations in patient-and treatment-specific risk factors. Methods and Materials: Patient-specific risk factors, such as a patient's anatomy and tumor dose requirements, affect organs-at-risk (OARs) dose-volume histograms (DVHs), which in turn affects plan quality and can potentially cause adverse effects. Treatment-specific risk factors, such as the use of chemotherapy and surgery, are clinically relevant when evaluating radiation therapy planning criteria. A risk-adjusted control chart was developed to identify unusual plan quality after accounting for patient-and treatment-specific risk factors. In this proof of concept, 6 OAR DVH points and average monitor units serve as proxies for plan quality. Eighteen risk factors are considered for modeling quality: planning target volume (PTV) and OAR cross-sectional areas; volumes, spreads, and surface areas; minimum and centroid distances between OARs and the PTV; 6 PTV DVH points; use of chemotherapy; and surgery. A total of 69 head and neck cases were used to demonstrate the application of risk-adjusted control charts, and the results were compared with the application of conventional control charts. Results: The risk-adjusted control chart remains robust to interpatient variations in the studied risk factors, unlike the conventional control chart. For the brainstem, the conventional chart signaled 4 patients with unusual (out-of-control) doses to 2% brainstem volume. However, the adjusted chart did not signal any plans after accounting for their risk factors. For the spinal cord doses to 2% brainstem volume, the conventional chart signaled 2 patients, and the adjusted chart signaled a separate patient after accounting for their risk factors. Similar adjustments were observed for the other DVH points when evaluating brainstem, spinal cord, ipsilateral parotid, and average monitor Sources of support: This work had no specific funding. Disclosures: The data used in this study are from de-identifiable Digital Imaging and Communications in Medicine files.

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Section: Methodsmentioning

confidence: 99%

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality

Roy

Cutright

Gopalakrishnan

et al. 2020

Advances in Radiation Oncology

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“…The covariance between dose points is often recorded and analyzed to track the efficacy of the guidelines. In fact, in a recent study using nominal estimators on 100 head-and-neck cases, D 95 was found to be negatively correlated to D 50 (Nohadani et al 2015, Roy et al 2016. This means that these two criteria, although recommended, cannot be satisfied concurrently.…”

Section: Discussionmentioning

confidence: 98%

Robust Maximum Likelihood Estimation

Bertsimas

Nohadani

2019

INFORMS Journal on Computing

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In many applications, statistical estimators serve to derive conclusions from data, for example, in finance, medical decision making, and clinical trials. However, the conclusions are typically dependent on uncertainties in the data. We use robust optimization principles to provide robust maximum likelihood estimators that are protected against data errors. Both types of input data errors are considered: (a) the adversarial type, modeled using the notion of uncertainty sets, and (b) the probabilistic type, modeled by distributions. We provide efficient local and global search algorithms to compute the robust estimators and discuss them in detail for the case of multivariate normally distributed data. The estimator performance is demonstrated on two applications. First, using computer simulations, we demonstrate that the proposed estimators are robust against both types of data uncertainty and provide more accurate estimates compared with classical estimators, which degrade significantly, when errors are encountered. We establish a range of uncertainty sizes for which robust estimators are superior. Second, we analyze deviations in cancer radiation therapy planning. Uncertainties among plans are caused by patients' individual anatomies and the trial-and-error nature of the process. When analyzing a large set of past clinical treatment data, robust estimators lead to more reliable decisions when applied to a large set of past treatment plans.

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“…Typically, DVH points are highly correlated with one another. 5,14,15 This is illustrated on our 69 head-and-neck cases in Fig. 2 To resolve correlations in data for regression, researchers commonly use principal component analysis (PCA).…”

Section: C Principal Component Analysismentioning

confidence: 99%

“…For example, a treatment planner evaluating DVH points

D_{2}

D_{20}

D_{40}

D_{60}

D_{80}

, and

D_{98}

is forced to monitor six control charts, an impractical consideration at a clinic. Another limitation is that a treatment planner who monitors and models individual DVH points is forced to ignore correlations among the points, correlations that have a strong presence in DVH data 5,14,15 While ignoring correlations, a physician could overlook unusual DVHs, thereby increasing the likelihood of false negatives and false positives as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Treatment plan quality control using multivariate control charts

et al. 2021

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Statistical process control tools such as control charts were recommended by the American Association of Physicists in Medicine (AAPM) Task Group 218 for radiotherapy quality assurance. However, the tools needed to analyze multivariate, correlated data that are often encountered in treatment plan quality measures, are lacking. In this study, we develop quality control tools that can model multivariate plan quality measures with correlations and account for patient-specific risk factors, without adding a significant burden to clinical workflow. Methods and Materials: A multivariate, quality control chart is developed that includes a risk-adjustment model, Hotelling's T 2 statistic, and principal component analysis (PCA). Principal component analysis accounts for correlations among a set of organ-at-risk (OAR) dose-volume histogram (DVH) points that serves as proxies for plan quality. Risk-adjustment models estimate the principal components from PCA using a set of patient-and treatment-specific risk factors. The resulting residuals from the risk-adjustment models are used to compute the Hotelling's T 2 statistic; the corresponding multivariate control chart is then plotted based on the beta distribution followed by the statistic. Further, the box-cox transformation is used to account for non-normality in DVH points. We investigate the application of the proposed methodology via three multivariate control chartsa conventional chart that ignores risk-adjustment and PCA, a risk-adjusted chart ignoring PCA, and a PCA-based, risk-adjusted chart. These control charts are evaluated on 69 head-and-neck cases. Results: The conventional multivariate control chart fails to account for important patient-specific risk factors, including volumes and cross-sectional areas of the tumor and OARs and distances in-between. This failure leads to a larger number of false alarms. While the multivariate risk-adjusted control chart is able to reduce false alarms, it fails to account for correlations in DVH points. The multivariate PCA-based, risk-adjusted control chart can detect unusual plans after accounting for the correlations. By replanning, improvements are shown on an unusual plan identified by both risk-adjusted methods. Conclusions: The multivariate risk-adjusted control chart developed here enables quality control of plans prior to delivery. This methodology is generic and can be readily applied for other radiotherapy quality assurance protocols, such as gamma analysis pass rates.

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On correlations in IMRT planning aims

Cited by 8 publications

References 28 publications

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality

Robust Maximum Likelihood Estimation

Treatment plan quality control using multivariate control charts

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