R Scheuermann

A Study to Quantify the Effectiveness of Daily Endorectal Balloon for Prostate Intrafraction Motion Management

PURPOSE To quantify intrafraction prostate motion between patient groups treated with and without daily endorectal balloon (ERB) employed during prostate radiotherapy and establish the effectiveness of the ERB. METHODS Real-time intrafraction prostate motion from 29 non-ERB (1,061 sessions) and 30 ERB (1,008 sessions) patients was evaluated based on three-dimensional (3D), left, right, cranial, caudal, anterior, and posterior displacements. The average percentage of time with 3D and unidirectional prostate displacements >2, 3, 4, 5, 6, 7, 8, 9, and 10 mm in 1-min intervals was calculated for up to 6 min of treatment time. The Kolmogorov-Smirnov method was used to evaluate the intrafraction prostate motion pattern between both groups. RESULTS Large 3D motion (up to 1 cm or more) was only observed in the non-ERB group. The motion increased as a function of elapsed time for displacements >2-8 mm for the non-ERB group and >2-4 mm for the ERB group (p < 0.05). The percentage time distributions between the two groups were significantly different for motion >5 mm (p < 0.05). The 3D symmetrical internal margin (IM) can be reduced from 5 to 3 mm (40% reduction), whereas the asymmetrical IM can be reduced from 3 to 2 mm (33% reduction) in cranial, caudal, anterior, and posterior for 6 min of treatment, when ERB is used. Beyond 6 min, the symmetrical 3D and asymmetrical cranial, caudal, anterior, and posterior IMs can be reduced from 9, 4, 7, 7, and 8 to 5, 2, 5, 3, and 4 mm, respectively (up to 57% reduction). CONCLUSION The percentage of time that the prostate was displaced in any direction was less in the ERB group for almost all magnitudes of motion considered. The directional analysis shows that the ERB reduced IMs in almost all directions, especially the anterior-posterior direction.

A mathematical framework for virtual IMRT QA using machine learning.

PURPOSE It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The purpose of this study was to develop an algorithm capable of predicting IMRT QA passing rates a priori. METHODS From all treatment, 498 IMRT plans sites were planned in eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. 3%/3 mm local dose/distance-to-agreement (DTA) was recorded using a commercial 2D diode array. Each plan was characterized by 78 metrics that describe different aspects of their complexity that could lead to disagreements between the calculated and measured dose. A Poisson regression with Lasso regularization was trained to learn the relation between the plan characteristics and each passing rate. RESULTS Passing rates 3%/3 mm local dose/DTA can be predicted with an error smaller than 3% for all plans analyzed. The most important metrics to describe the passing rates were determined to be the MU factor (MU per Gy), small aperture score, irregularity factor, and fraction of the plan delivered at the corners of a 40 × 40 cm field. The higher the value of these metrics, the worse the passing rates. CONCLUSIONS The Virtual QA process predicts IMRT passing rates with a high likelihood, allows the detection of failures due to setup errors, and it is sensitive enough to detect small differences between matched Linacs.

IMRT QA using machine learning: A multi‐institutional validation

Abstract Purpose To 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. Methods A 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. Results The 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. Conclusions We 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.

MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

PURPOSE To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. METHODS A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. 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. RESULTS In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predict gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. CONCLUSION We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.

SU‐E‐J‐145: Complete Study to Characterize the Effectiveness of Daily Endorectal Balloon (ERB) for Prostate Intrafraction Motion Management

Purpose: To quantify intrafraction prostate motion between patient groups treated with and without daily endorectal balloon (ERB) employed during prostate radiotherapy and establish the effectiveness of the ERB. Methods: Real time intrafraction prostate motion from 29 non‐ERB (1061 sessions) and 30 ERB (1008 sessions) patients was evaluated based on three‐dimensional (3D), left, right, cranial, caudal, anterior and posterior displacements. The average percentage of time with 3D and unidirectional prostate displacements > 2, 3, 4, 5, 6, 7, 8, 9, and 10mm in 1 minute intervals was calculated for up to 6 minutes of treatment time. The Kolmogorov‐Smirnov method was used to evaluate the intrafraction prostate motion pattern between both groups. Results: 3D motion >=1 cm was observed for the non‐ERB group only. The motion increased as a function of elapsed time for displacements > 2 to 8mm for the non‐ERB group and > 2 to 4 mm for the ERB group (p 5 mm (p < 0.05). The 3D internal margin (IM) covering 95% treatment time can be reduced from 5 to 3 mm (40% reduction) while the asymmetrical IM can be reduced from 3 to 2 mm (33% reduction) in cranial, caudal, anterior, and posterior for 6 minutes treatment, when ERB is employed. Beyond 6 minutes, the 3D and cranial, caudal, anterior, and posterior IMs can be reduced from 9, 4, 7, 7, and 8 to 5, 2, 5, 3, and 4 mm, respectively (up to 57% reduction). The calculated IMs apply to the worst case scenario patient as well. Conclusions: The ERB effectively reduces prostate motion and allows for smaller IMs. Therefore, daily ERB has the potential to further improve toxicity profiles in prostate radiotherapy.

SU‐FF‐T‐327: Effect of Rectal Balloon Filling Content On Rectal Wall Dose Distribution in Prostate Radiotherapy

Purpose: To estimate the dose difference at the level of the rectal wall for air‐ and water‐filled rectal balloons for conformal and IMRT prostate treatment plans. Method and Materials: A Radiadyne rectal balloon was filled with 100 cc of either air or water and placed in water equivalent phantom. CT simulations of the phantom were performed with air and water‐filled balloons. Eight marked points along the circumference of the balloon were identified. Treatment fields for both conformal and IMRT prostate plans were copied and calculated on both phantom image sets using the Varian Eclipse TPS v8.1 (AAA). Thermoluminescent detectors(TLDs) were then positioned at each of the 8 marked positions. A treatment fraction was delivered to the phantom using a Varian 2300 IX, 15MV photon beam. The dose measured at corresponding positions was then compared between water vs. air and against the Eclipse calculated dose.Results: The measurements confirm a difference in dose deposited along the rectal wall for air‐ and water‐filled rectal balloons. Points along the anterior rectal wall saw an increase in dose for water‐filling relative to air‐filling, while points along the posterior rectal wall exhibited a reduction in dose for water‐filling relative to air. The average dose increase along the anterior wall for IMRT and conformal plans was 12% (range 10–15%; IMRT) and 7% (range 3–13%; conformal) respectively, while the average dose decrease along the posterior wall was 7% (range 3–11%; IMRT) and 11% (range 0–18%) respectively. Conclusion: This study demonstrates the need for careful consideration when implementing rectal balloons clinically, as a dose increase of 12% could result along the anterior rectum for water‐filling vs. air‐filling.