Quantifying the performance of <i>in vivo</i> portal dosimetry in detecting four types of treatment parameter variations

Bojechko, C.; Ford, Eric

doi:10.1118/1.4935093

Cited by 61 publications

(63 citation statements)

References 35 publications

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“…Receiver‐operating characteristic (ROC) curves, often used in medicine to assess the detectability of a given end point, were generated by varying the gamma pass rate. They plot the true positive rate (sensitivity) versus the false positive rate (1‐specificity) of a given test.…”

Section: Methodsmentioning

confidence: 99%

“…One of these plans, a 7-field step-and-shoot IMRT plan (Plan IMRT0 in Table 1), was delivered to the IROC-H phantom after standard patient-specific QA and physics checks were performed. Receiver-operating characteristic (ROC) curves, often used in medicine to assess the detectability of a given end point, 9,25 were generated by varying the gamma pass rate. They plot the true posi- Five step-and-shoot IMRT plans and four full arc VMAT plans were individually optimized to ensure a variety of plans with unique solutions were investigated.…”

Section: A | Iroc-h Phantom Irradiation and Tps Beam Modelmentioning

confidence: 99%

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Impact of the MLC leaf‐tip model in a commercial TPS: Dose calculation limitations and IROC‐H phantom failures

Koger

Price

Wang

et al. 2020

J Applied Clin Med Phys

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Purpose Treatment planning system (TPS) dose calculation is sensitive to multileaf collimator (MLC) modeling, especially when treating with intensity‐modulated radiation therapy (IMRT) or VMAT. This study investigates the dosimetric impact of the MLC leaf‐tip model in a commercial TPS (RayStation v.6.1). The detectability of modeling errors was assessed through both measurements with an anthropomorphic head‐and‐neck phantom and patient‐specific IMRT QA using a 3D diode array. Methods and Materials An Agility MLC (Elekta Inc.) was commissioned in RayStation. Nine IMRT and VMAT plans were optimized to treat the head‐and‐neck phantom from the Imaging and Radiation Oncology Core Houston branch (IROC‐H). Dose distributions for each plan were re‐calculated on 27 beam models, varying leaf‐tip width (2.0, 4.5, and 6.5 mm) and leaf‐tip offset (−2.0 to +2.0 mm) values. Doses were compared to phantom TLD measurements. Patient‐specific IMRT QA was performed, and receiver‐operating characteristic (ROC) analysis was performed to determine the detectability of modeling errors. Results Dose calculations were very sensitive to leaf‐tip offset values. Offsets of ±1.0 mm resulted in dose differences up to 10% and 15% in the PTV and spinal cord TLDs respectively. Offsets of ±2.0 mm caused dose deviations up to 50% in the spinal cord TLD. Patient‐specific IMRT QA could not reliably detect these deviations, with an ROC area under the curve (AUC) value of 0.537 for a ±1.0 mm change in leaf‐tip offset, corresponding to >7% dose deviation. Leaf‐tip width had a modest dosimetric impact with <2% and 5.6% differences in the PTV and spinal cord TLDs respectively. Conclusions Small changes in the MLC leaf‐tip offset in this TPS model can cause large changes in the calculated dose for IMRT and VMAT plans that are difficult to identify through either dose curves or standard patient‐specific IMRT QA. These results may, in part, explain the reported high failure rate of IROC‐H phantom tests.

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Section: Methodsmentioning

confidence: 99%

Section: A | Iroc-h Phantom Irradiation and Tps Beam Modelmentioning

confidence: 99%

Impact of the MLC leaf‐tip model in a commercial TPS: Dose calculation limitations and IROC‐H phantom failures

Koger

Price

Wang

et al. 2020

J Applied Clin Med Phys

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“…In a recent paper by Bojecko et al, 11 the authors state that patient position displacements of 5 and 10 mm in each of the 3 cardinal directions in their selected patient cohort was not readily detectable using the gamma index. Our results confirmed the findings of Kruse,12 who determined that per-field gamma analysis was a poor predictor of dosimetric accuracy.…”

Section: Discussionmentioning

confidence: 99%

Can a commercially available EPID dosimetry system detect small daily patient setup errors for cranial IMRT/SRS?

Hsieh

Hansen

Kent

et al. 2017

Practical Radiation Oncology

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“…Machine learning is a subfield of data science that focuses on designing algorithms that can learn from and make predictions on data. Machine learning applications in radiotherapy have emerged increasingly in recent years, with applications including predictive modeling of treatment outcome in radiation oncology,1, 2, 3, 4, 5, 6, 7 treatment optimization,8, 9, 10, 11 error detection and prevention,12, 13, 14, 15 and treatment machine quality assurance (QA) 16, 17, 18, 19. These machine learning techniques have provided physicians and physicists information for more effective and accurate treatment delivery as well as the ability to achieve personalized treatment.…”

Section: Introductionmentioning

confidence: 99%

IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

J Applied Clin Med Phys

129

138

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PurposeTo validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

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Quantifying the performance of in vivo portal dosimetry in detecting four types of treatment parameter variations

Abstract: In vivo EPID dosimetry is able to detect relatively small variations in overall dose, systematic shifts of the MLC's, and changes in the patient habitus. Shifts in the patient's position which can introduce large changes in the target dose coverage were not readily detected.

Cited by 61 publications

References 35 publications

Impact of the MLC leaf‐tip model in a commercial TPS: Dose calculation limitations and IROC‐H phantom failures

Impact of the MLC leaf‐tip model in a commercial TPS: Dose calculation limitations and IROC‐H phantom failures

Can a commercially available EPID dosimetry system detect small daily patient setup errors for cranial IMRT/SRS?

IMRT QA using machine learning: A multi‐institutional validation

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