Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS

ISSN 1391-0256 Journal of the Sri Lanka Association for the Advancement of Science Volume 7 Issue 1, 2025 Founded in 1944 and incorporated by the Act of Parliament No 11 of 1966. JSLAAS 1 Journal of the Sri Lanka Association for the Advancement of Science is a biannual publication. Selected research work from annual research sessions (based on scientific merit) as well as other research articles are invited to submit research manuscripts as per the guidelines provided by SLAAS. SLAAS members may also separately submit their papers for publication. 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All rights reserve. e-ISSN: 2682-6992 2 Table of Contents Page 1 Molecular docking of potential antifungal compounds from Ulva fasciatamethanolic extract gainst Pseudopestalotiopsistheae A. H. D. Alahakoon, B.K. D. M. Rodrigo, B.M. Chathuranga, M. Balasooriya, H. M. Herath, R. P. Wanigatunge 03 2 Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS K. L. I. Gunawardhana, J. Jeyasugiththan, P. De Silva and D. Satharasinghe 16 3 Development of a Solar - Powered, Automated Water Ionizer Using Graphite-Based Electrodes for Alkaline and Acidic Water Production A.M.K.L Abeykoon, M.D.Y Milani, H.M. B. I. Gunathilaka ,R. C. W. Arachchige, D.M.K Muthumala 40 16 Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS K. L. I. Gunawardhana∗1, J. Jeyasugiththan2, P. De Silva1 and D. Satharasinghe2 1Department of Radiotherapy, National Hospital Galle, Sri Lanka 2Department of Nuclear Science, University of Colombo, Colombo, 00300, Sri Lanka ABSTRACT Intensity-Modulated Radiation Therapy (IMRT) uses computer-controlled linear accelerators to deliver precise radiation doses to benign or malignant tumors or specific areas within tumors while minimizing exposure to healthy tissues and organs at risk. The purpose of this study is to evaluate the dosimetric impact on IMRT plans of changing the per control point Statistical Uncertainty (SU) from 1% to 6% in 1% increments using the Monaco Treatment Planning System (TPS) for three different diagnoses: Larynx, Oesophagus, and Prostate. The per control point SU is a key factor in determining dose calculation accuracy and calculation time. In this study, 54 IMRT plans were generated by varying the per control point SU as 1%, 2%, 3%, 4%, 5%, and 6%, using nine patients for each diagnosis. Dosimetric indices, including Conformity Index, Heterogeneity Index, Target Dose (PTV), Organ At Risk doses, Dose Calculation Time, Treatment Delivery results, and Dose Volume Histogram, were used to evaluate the generated plans. No significant differences were observed across all dosimetric indices, and an exponential relationship was found between Dose Calculation Time and the per control point SU. For IMRT plans, a 3% per Control Point SU is acceptable, providing shorter and adequately accurate dose calculation times without compromising plan quality or deliverability. Keywords: Radiotherapy, IMRT, dosimetric impact, Per Control Point, Statistical Uncertainty * [email protected] K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 17 INTRODUCTION Radiotherapy is a medical treatment that uses high doses of radiation (gamma rays, highenergy X-rays, and Electrons) to kill or damage benign and malignant tumors (Hall & Giaccia, 2019) . It is a crucial component of cancer treatment and is employed either as radiotherapy itself or in combination with surgery, chemotherapy, or immunotherapy (Delaney, Jacob, Featherstone, & Barton, 2005). Radiotherapy can be given inside or outside of our bodies. The most common kind is External Beam Radiation Therapy (EBRT). It uses a large machine called a Linear Accelerator (Linac) to treat cancer patients using high-energy X-rays and electrons. At present, there are other advanced types of machines used for radiotherapy treatment too. Such as Tomotherapy machine, Cyberknife machine, MR-Lianc, PET-Linac, ProBeam machine, GammaKnife machine, and ZAP-X machine (Palta & Mackie, 2011). There are ordinary and advanced treatment techniques currently used in the oncology field using those advanced machines. Such as three-dimensional conformal Radiotherapy (3DCRT), IntensityModulated Radiation Therapy (IMRT), Image-Guided Radiation Therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), Stereotactic Body Radiation Therapy (SBRT), Proton Therapy, and Adaptive Radiation Therapy (ART) (Khan & Gibbons, 2014). Apart from those machines, the cobalt 60 machine is used to treat cancer patients using Gamma-rays, and it is an older version of a raditherapy treatment machine (Podgorsak, 2005) . Intensity-Modulated Radiation Therapy, also called IMRT, is an advanced type of radiation therapy technique, and it is an inverse planning technique (Bortfeld, 2006) . Inverse planning is a technique that uses a computer program to automatically achieve a treatment plan that has an optimal merit. Here, it is less dependent on the geometric parameters but more on the specification of volumes of tumor targets and organs at risk, as well as their dose constraints. IMRT uses computer-controlled linear accelerators to deliver precise radiation doses to a benign or malignant tumor or specific areas within the tumor. IMRT allows for the radiation dose to conform more precisely to the three-dimensional (3-D) shape of the tumor by modulating or controlling the intensity of the radiation beam in multiple small segments. Also, IMRT allows higher radiation doses to be focused on the tumor while minimizing the dose to surrounding normal critical structures. Because the ratio of normal tissue dose to tumor dose is reduced to a minimum with the IMRT approach, higher and more effective radiation doses can safely be delivered to tumors with fewer side effects compared with conventional radiotherapy techniques. IMRT also has the potential to reduce treatment toxicity, even when doses are not increased. Due to its complexity, IMRT does require slightly longer daily treatment times, additional planning, and safety checks before starting the patient treatment (IMRT Patient Specific Quality Assurance) when compared with conventional radiotherapy (Gupta, Agarwal, Ghosh-Laskar, & Shrivastava, 2009) . IMRT was first conceptualised in the 1960s (Intensity Modulated Radiation Therapy Collaborative Working Group, 2001) . Although the concept of IMRT and early algorithms for planning were developed in Sweden, clinical application did not begin until a fully integrated IMRT planning and delivery system, namely, the NOMOS Peacock system, was invented and commissioned in 1993 by the collaborated effort between NOMOS and Baylor College of Medicine/the Methodist Hospital (Houston, TX, USA) (Mohan, 2005). After obtaining investigational device exemptions and protocol approval by Baylor’s Investigational Review Board, the first patient with brain metastases was to have three brain tumors treated simultaneously using IMRT in September 1993 (Lawrence & Cox, 1995). In 1994, the NOMOS Peacock system was introduced as the first commercial IMRT delivery unit. The Peacock system required the use of a beam modulation device known as a dynamic multivane intensityK. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 18 modulating collimator (MIMiC). This particular form of IMRT, called serial tomotherapy, could be treated by a continuously rotating gantry (Carol, 1995). Step and shoot IMRT represents another commonly used technique whereby multiple static beams are subdivided into ‘segments’ (LoSasso, Chui, & Ling, 1998). In the sliding window technique (dynamic Multileaf CollimatordMLC), a window defined by the MLC leaves sweeps across the treatment field at variable speed, while the monitor units are delivered continuously (Zhang et al., 2007). Dose calculation accuracy in IMRT is an important and crucial factor to prevent mistreatment of radiation treatment delivery using linear accelerator machines (Papiez & Langer, 2006). Among the commercially available dose calculation algorithms, Monte Carlo (MC) is considered to be potentially more accurate and complex than others. Although MC dose calculation algorithms are recognized as the most accurate dose computation algorithms for treatment planning, their inherent Statistical Uncertainty (SU) determines the accuracy of the dose calculation and the time span of the dose calculation (Taleei & Tabrizi, 2019) . The SU decreases inversely with the square root of the time span of the dose calculation. By decreasing the SU, one can increase the dose calculation accuracy. But the SU decreases, resulting in a significant increase in the time span of the dose calculation. Therefore, it should be a compromise between the SU and the dose calculation accuracy, with a suitable time span for the dose calculation in IMRT planning. Therefore, by studying this, it is possible to get an idea about how to optimize the accuracy of the dose calculation and the SU with a suitable time span of the dose calculation in IMRT planning (Chetty et al., 2007) . There are many Treatment Planning Systems (TPSs) that can be used to develop IMRT plans, utilizing their own dose calculation algorithm (Vassiliev et al., 2010) . The Elekta’s Monaco TPS is one of the most powerful tool that bring increased automation, intelligent workflows, and high-quality treatment planning to a wide range of radiotherapy treatment delivery systems. The Monaco TPS combines t h e MC dose calculation algorithm with robust optimization tools to provide high-quality radiotherapy treatment plans for IMRT, VMAT, and SBRT (Goodall & Ebert, 2020) . The Monaco 5.11.02 TPS used two kinds of SUs. Such as per control point SU and per calculation SU, and the planner can manually select one of them. In this research, we used a per control point SU to generate IMRT plans. Also, the Monaco 5.11.02 TPS has an option to choose different percentage values between 0.1 % - 10 % (Kry et al., 2019). In the per control point SU, the percentage uncertainty is based on the per voxel on a per segment. Also, the uncertainty was not the same in all voxels. The low-dose voxels in the peripheral regions of the patient had a higher uncertainty of dose than the voxels in the region of the maximum dose (PTV) (Keall, Siebers, Jeraj, & Mohan, 2000) . The dose uncertainty in the target volume (PTV) for the final plan was calculated and appeared in the TPS console window after the second stage dose calculation. There are a few other studies that have previously evaluated the overall effect of SU on dose calculation. But not about altering the per control point SU in IMRT plans. In 2000, Keall, Siebers, Jeraj, and Mohan (2000) found that the dose in Monte Carlo (MC) calculation does not significantly affect isodose lines and Dose Volume Histogram (DVH) for SU of 2% or lesser values. In 2004, Cheong, Suh, and Cho (2004) investigated the effect of SU on photon dose calculation using BEAMnrc and DOXXYZnrc MC simulation systems and evaluated SU based on DVH, isodose comparison, and root mean-square. In 2005, Ma et al. (2005) studied the issues related to the statistical analysis of MC dose calculations for realistic clinical beams using various variance reduction or time-saving techniques. Also, they discussed the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and IMRT based on MC simulations. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 19 In 2016, Sarkar et al. (2016) investigated the interplay between Monte Carlo Variance (MCV) and Fluence Smoothing Factor (FSF) in VMAT for carcinoma of esophagus patients using a CMSMonaco TM Treatment Planning System (TPS). They reported that variation in FSF causes a difference in dosimetric and physical parameters for the treatment plan. In 2019, Palanisamy et al. (2019) explored the dosimetric impact of varying SU when calculating the dose of VMAT plans, and in 2021, Rembish et al. (2021) determined the severity of the effects on VMAT dose calculations caused by varying per control point SU in an MC-based TPS. Also, they assessed the impact of the uncertainty during DVH evaluation. The goal of IMRT planning is to shape the radiation dose to avoid or reduce exposure of healthy tissue and limit the side effects of treatment while delivering a therapeutic dose to the cancer. According to the best of our knowledge, no precise data are available for the optimal acceptance level of SU% % per control point for different diagnoses in IMRT. Also, no one studied the Dosimetric Impact on IMRT Plans of altering the per control point SU in Monaco TPS. Therefore, the purpose of this study is to evaluate the dosimetric impact on IMRT plans of altering the per control point SU (1% - 6%) using Elekta’s Monaco TPS for three different diagnoses (Larynx, Esophagus, and Prostate). METHODOLOGY CT Simulation and Radiotherapy Treatment Machine In this research work, three different diagnoses, which have high diversity, such as the Larynx, Oesophagus, and Prostate, were planned using the IMRT technique. A total of nine patients, three from each diagnosis, were selected for this study. The necessary CT image sets of all nine patients were obtained using a CT simulator (Siemens Healthineers, SOMATOM Confidence). The CT slice thickness of 5 mm was obtained for each clinical case for treatment planning. All generated IMRT plans were delivered using a 6 MV photon beam of Elekta Synergy Platform linear accelerator and its having a 1 cm multi-leaf collimator (MLC) at the iso-center. Contouring and Dose Prescription The tumor volume (Planning Target Volume - PTV) and Organs At Risk (OARs) volumes were contoured, and the doses prescribed to the Larynx, Oesophagus, and Prostate were 66 Gy/30 fractions, 50.4 Gy/28 fractions, and 78 Gy/39 fractions, respectively. Treatment Planning System (TPS) In this research work, the Monaco 5.11.02 TPS (IMPAC Medical System, Inc., Maryland Heights, MU, USA) was used to generate IMRT plans. It has a two-stage process of optimizing dose distribution. At the first stage, the ideal fluence distribution of a beam is optimized to meet a user-defined prescription for a single set of beams. At the second stage, the ideal distribution is transmitted into a set of segments where the shapes and weights are optimized based on the same prescription. For this research work, the Monte Carlo (MC) algorithm was used for dose calculation to generate an IMRT plan. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 20 The Dosimetric Parameters used in Monaco TPS All the 54 IMRT plans were generated using the dosimetric parameters, which are given in Table 1 below. By keeping these parameters constant, IMRT plans were generated using MC dose calculation algorithm only by varying per control point SU 1%, 2%, 3%, 4%, 5%, and 6%. To analyze IMRT plans, different dosimetry indices were used as mentioned below. Table 1. The Dosimetric Parameters used in Monaco TPS Parameter Value Delivery Mode dMLC CT Slice Thickness 0.5 cm Grid Size 0.3 cm Beamlet Width 0.3 cm Control Points 40 Segment Width 0.5 cm Auto Flash Margin 0.2 cm Surface Margin 0.3 cm Target Margin 0.8 cm Fluence Smoothing Medium Dosimetric Indices used for IMRT Plan Evaluation In this study, we used several dosimetric indices for IMRT Plan evaluation. Such as, Conformity Index (CI), Heterogeneity Index (HI), Target Dose and Critical Organ Doses, Dose Calculation Time (DCT), Treatment Delivery Results (IMRT Patient Specific QA and Gamma Indices), and Dose Volume Histogram (DVH). • Conformity Index (CI) The Conformity Index (CI) describes the degree to which the prescribed isodose volume conforms to the shape and size of the target volume. This value is only reported for Monaco plans (Elekta, 2017) . The CI formula is given in equation 1:   2 * Rx RI V CI TV V  (1) where, TV = Structure Volume, V Rx = is the structure volume covered by the Dose of Interest and V RI is the total volume of the Dose of Interest. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 21 • Heterogeneity Index (HI) The Heterogeneity Index (HI) describes the uniformity of dose within a target volume and is directly calculated from the statistics of the DVH. This value is only reported for Monaco plans (Elekta, 2017). The HI formula is is given in equation 2: 5% 95% D HI D  (2) where, D5% is the dose delivered to the hottest 5% of the tissue volume. D95% is the minimum dose received by 95% of the tissue volume. • Target Dose and Organ At Risk (OAR) Doses The target dose (Dose to PTV) was analyzed as D95% (the dose received by 95% of the volume of PTV and V95% (the volume received 95% of the prescribed dose). Moreover, the maximum dose (Dmax) and mean dose (Dmean) for PTV and Organ at Risk (OAR) were analyzed for all three clinical cases. The OAR dose constraints (Table 2) were taken from the Practical Radiotherapy Planning book (5th Edition) (Morris, Roques, Ahmad, & Loo, 2023). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 28 Figures 3-5 show the average Dose Volume Histogram (DVH) for Larynx, Oesophagus, and Prostate, respectively, for six different per control point SUs (Such as 1%, 2%, 3%, 4%, 5%, 6%). The measured average DVH results showed a very small dose difference between the per control point SU 1% - 6% and no significant dose differences were observed. According to the measured average results shown in Figure 6, there is an exponential relationship between dose calculation time and the per control point SU. The increase of per control point SU leads to a decrease in MC dose calculation time with a significant difference. Approximately reducing the per control point SU by a factor of two requires a four times increase in Central Processing Unit (CPU) time (Palanisamy, David, Durai, Bhalla, & Puri, 2019) . Figure 7 indicates a linear relationship between average final dose uncertainty and the per control point SU values from 1% to 6%. The important fact is th at the final dose uncertainty should be approximately 1% for the entire plan, and it shouldn’t be greater than 1% for the entire plan (Elekta AB, 2017) . Figure 6. Average Dose Calculation Time (DCT) (min) Vs. Per Control Point Statistical Uncertainty (SU) (%) K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 29 Figure 7: Average Final Dose Uncertainty (%) Vs. Per Control Point Statistical Uncertainty (SU) (%) K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 30 Table 3: Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Larynx Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 66 D95% (cGy) 6208.60 ± 11.60 PTV 66 Dmax (cGy) 6908.70 ± 12.90 PTV 66 Dmean (cGy) 6487.07 ± 12.12 6238.73 ± 23.11 6924.47 ± 25.63 6511.67 ± 24.11 6209.03 ± 33.96 6925.10 ± 37.86 6490.13 ± 35.48 6219.47 ± 45.65 6932.83 ± 50.85 6501.53 ± 47.69 6213.13 ± 56.78 6961.60 ± 63.59 6496.07 ± 59.35 6224.47 ± 68.31 6982.30 ± 76.58 6512.03 ± 71.43 PTV 66 V95% (%) 93.81 ± 0.19 94.38 ± 0.37 93.73 ± 0.55 93.78 ± 0.73 93.78 ± 0.91 94.16 ± 1.10 PTV 66 Heterogeneity Index 1.08 ± 0.002 1.08 ± 0.004 1.08 ± 0.006 1.08 ± 0.008 1.08 ± 0.009 1.08 ± 0.012 PTV 66 Conformity Index 0.80 ± 0.001 0.79 ± 0.003 0.80 ± 0.004 0.79 ± 0.006 0.80 ± 0.007 0.79 ± 0.009 Brain Stem Dmax (cGy) 1072.60 ± 2.68 1202.47 ± 6.08 1152.77 ± 8.75 1136.80 ± 11.36 1102.43 ± 13.59 1164.63 ± 17.52 Spinal Cord Dmax (cGy) 3680.90 ± 6.89 3679.20 ± 13.66 3678.57 ± 20.14 3687.30 ± 27.08 3712.33 ± 33.93 3676.87 ± 40.34 Lt Parotid Dmean (cGy) 1841.53 ± 3.66 1794.90 ± 7.13 1785.67 ± 10.54 Rt Parotid Dmean (cGy) 1775.77 ± 3.50 1836.20 ± 7.27 1824.23 ± 10.63 1819.60 ± 14.29 1810.53 ± 14.14 1797.97 ± 17.57 1790.10 ± 17.29 1792.87 ± 20.91 1784.20 ± 20.72 Lt Lens Dmax (cGy) 60.77 ± 0.12 62.33 ± 0.23 61.77 ± 0.35 63.83 ± 0.48 61.17 ± 0.57 64.07 ± 0.72 Rt Lens Dmax (cGy) 61.53 ± 0.12 62.30 ± 0.24 61.63 ± 0.35 63.17 ± 0.47 61.87 ± 0.58 64.10 ± 0.72 Global Max of the Plan (%) 104.68 ± 0.19 104.92 ± 0.37 104.92 ± 0.55 105.11 ± 0.73 105.48 ± 0.91 105.96 ± 1.10 Dose Calculation Time (mins) 77.32 ± 3.91 20.14 ± 1.02 10.54 ± 0.53 6.55 ± 0.33 4.90 ± 0.25 4.14 ± 0.22 Gamma Pass Rate 3%, 3 mm 98.1 ± 2.94 98.5 ± 2.95 98.3 ± 2.95 98.6 ± 2.96 98.4 ± 2.95 98.8 ± 2.96 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 31 Table 4. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Oesophagus Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 4950.47 ± 8.43 4963.87 ± 16.73 PTV 50.4 Dmax (cGy) 5189.47 ± 8.83 5209.37 ± 17.56 PTV 50.4 Dmean (cGy) 4997.70 ± 8.50 5013.07 ± 16.90 4952.93 ± 25.12 5210.03 ± 26.44 5002.13 ± 25.37 4955.83 ± 33.41 5212.10 ± 35.14 5010.37 ± 33.79 4939.50 ± 38.88 5226.63 ± 44.16 4999.80 ± 42.23 4949.03 ± 49.71 5261.10 ± 52.85 5011.73 ± 50.34 PTV 50.4 V95% 98.67 ± 0.17 98.71 ± 0.34 98.68 ± 0.51 98.73 ± 0.67 98.70 ± 0.84 98.71 ± 1.00 PTV 50.4 Heterogeneity Index 1.03 ± 0.002 1.03 ± 0.004 1.03 ± 0.005 1.03 ± 0.007 1.03 ± 0.009 1.03 ± 0.010 PTV 50.4 Conformity Index 0.68 ± 0.001 0.68 ± 0.002 0.68 ± 0.003 0.68 ± 0.005 0.70 ± 0.006 0.68 ± 0.007 Heart Dmean (cGy) 2303.07 ± 3.93 2278.23 ± 7.70 2285.87 ± 11.64 2276.40 ± 15.38 2273.73 ± 19.26 2290.83 ± 23.09 Spinal Cord Dmax (cGy) 3560.20 ± 6.23 3584.77 ± 12.43 3576.33 ± 18.70 3558.87 ± 24.73 3572.70 ± 31.15 3547.00 ± 36.66 Rt Lung Dmean (cGy) 1523.03 ± 2.61 1516.70 ± 5.11 1522.23 ± 7.74 1522.50 ± 10.28 1511.73 ± 12.79 1518.63 ± 15.28 Lt Lung Dmean (cGy) 1458.57 ± 2.49 1456.60 ± 4.92 1455.77 ± 740 1455.80 ± 9.83 1448.57 ± 12.26 1456.53 ± 14.65 Lt Kidney Dmean (cGy) 168.80 ± 0.38 167.77 ± 0.78 164.23 ± 1.12 162.93 ± 1.50 165.97 ± 1.91 162.77 ± 2.22 Rt Kidney Dmean (cGy) 40.70 ± 0.07 40.73 ± 0.15 40.80 ± 0.22 40.67 ± 0.29 40.57 ± 0.36 40.60 ± 0.43 Global Max of the Plan (%) 102.97 ± 0.17 103.36 ± 0.34 103.38 ± 0.51 103.42 ± 0.67 103.70 ± 0.84 104.39 ± 1.00 Dose Calculation Time (mins) 90.8 ± 4.66 26.06 ± 1.32 12.18 ± 0.62 8.42 ± 0.43 5.85 ± 0.30 4.89 ± 0.25 Gamma Pass Rate 3%, 3 mm 97.5 ± 2.93 98.3 ± 2.95 97.9 ± 2.94 98.1 ± 2.94 97.7 ± 2.93 98.4 ± 2.95 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 32 Table 5. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Prostate Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 7657.57 7672.97 7681.73 7662.20 7633.23 7615.20 ± 18.89 ± 38.12 ± 56.61 ± 75.37 ± 91.90 ± 109.41 PTV 78 Dmax (cGy) 8046.73 8075.70 8118.27 8139.67 8132.50 8202.93 ± 19.85 ± 40.12 ± 59.83 ± 80.06 ± 97.93 ± 117.88 PTV 78 Dmean (cGy) 7793.60 7811.20 7822.13 7810.90 7813.40 7827.07 ± 19.23 ± 38.80 ± 57.65 ± 76.83 ± 94.07 ± 112.47 PTV 78 V95% (cm3) 99.43 ± 0.2 99.54 ± 0.50 99.53 ± 0.74 99.39 ± 0.98 99 ± 1.20 98.77 ± 1.44 PTV 78 Heterogeneity Index 1.03 ± 0.003 1.03 ± 0.005 1.03 ± 0.008 1.03 ± 0.010 1.04 ± 0.012 1.05 ± 0.014 PTV 78 Conformity Index 0.72 ± 0.002 0.70 ± 0.004 0.70 ± 0.005 0.71 ± 0.007 0.72 ± 0.009 0.71 ± 0.011 Rectum Dmean (cGy) 4109.13 4164.23 4144.10 4132.20 4098.40 4083.50 ± 9.89 ± 20.69 ± 30.53 ± 40.63 ± 49.35 ± 58.68 Bladder Dmean (cGy) 3778.87 3811.00 3801.77 3768.13 3741.43 3741.30 ± 9.40 ± 19.09 ± 28.27 ± 37.42 ± 45.53 ± 54.08 Lt Pelvic Bone Dmean (cGy) 2168.93 2180.77 2170.37 2159.70 2181.27 2193.07 ± 5.39 ± 10.92 ± 16.11 ± 21.40 ± 26.47 ± 31.65 Rt Pelvic Bone Dmean (cGy) 2189.97 2189.70 2199.67 2187.13 2200.87 2223.80 ± 5.46 ± 11.02 ± 16.41 ± 21.80 ± 26.84 ± 32.26 Global Max of the Plan (%) 103.21 103.73 104.08 104.45 104.37 105.16 ± 0.25 ± 0.50 ± 0.74 ± 0.98 ± 1.20 ± 1.44 Dose Calculation Time (mins) 81.5 ± 4.11 24.74 ± 1.24 14.03 ± 0.71 9.12 ± 0.47 7.06 ± 0.36 5.92 ± 0.30 Gamma Pass Rate 3%, 3 mm 97.1 ± 2.91 97.7 ± 2.93 97.5 ± 2.93 97.6 ± 2.93 97.2 ± 2.92 97.6 ± 2.93 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 33 DISCUSSION The Monte Carlo (MC) methods are mainly used in three distinct problem classes: optimization, numerical integration, and generating draws from a probability distribution. In Monaco 5.11.02 TPS, MC was used for optimization, and the MC method has been identified as the gold standard for dose calculation (Clements, Schupp, Tattersall, Brown, & Larson, 2018) . At present, MC simulation calculates the dose very closely to reality, taking into account the contribution of secondary photons and electrons scattering and dose absorption, especially in homogeneous environments (Tugrul, 2021). The per control point SU is the percentage (%) SU per voxel, on a per-segment basis, that is willing to accept for the final dose calculation. So, the mean, per voxel, uncertainty in a central region of the dose of a segment is equal to the user-specified SU at the end of the dose calculation. A voxel is a measurement of volume in a structure that is to be imaged. Each voxel represents a defined volume and can be localized by coordinates on a three-dimensional (3D) grid. Here, the very important fact is that the smaller the per control point SU, the longer the dose calculation time. Also, when we used per control point SU values between 0.1% - 10%, the results should be a final dose uncertainty of approximately 1% for the plan in the central region of the target volume. The main difference between Per Calculation SU and Per Control Point SU is based on the number of histories (Uyar & Günekbay, 2023) and the voxel. In other words, Per Calculation SU is fast because it estimates the number of histories for the entire plan (recommended value 1%). Per control Point SU gives better resolution because it uses percentage uncertainty per voxel on a per-segment basis. So it should vary for the number of control points. In this work, we used 40 control points to generate each IMRT plan. The MC dose calculation without any SU is the most worthwhile in an IMRT plan from the accuracy point of view. However, it would take infinite time to calculate. So the planner should accept a certain range for this calculation uncertainty. The SU of MC is inversely proportional to the volume of the dose voxel (Mohan, Antolak, & Hendee, 2001) . For example, when decreasing the voxel size from 5 mm to 3 mm, it caused to increase in the Monte Carlo calculation time of approximately fivefold. Also, when reducing the SU by a factor of two, it caused the MC calculation time to be fourfold (Figure 6). So it was very crucial in decreasing/ increasing voxel size or SU in both ways. Overall analysis of this study suggests that there were no diagnosis-specific dosimetric variations. As reported by Jiang et al. (Jiang, Pawlicki, & Ma, 2000) , large Statistical Uncertainties (SUs) are expected to blur the Dose Volume Histogram (DVH) curves and may become unreliable. The statistical noise should have practically no effect on inverse treatment planning (as IMRT) because the intensity along a ray is affected by the average of dose values over a large number of voxels lying along the ray and not by the dose in any one voxel (Mohan, Antolak, & Hendee, 2001) . It was suggested that large SU can be used for large tumors and OARs such as parallel organs (Palanisamy, David, Durai, Bhalla, & Puri, 2019) . The effect of the per control point SU in this study showed no significant dose differences on the mean dose to the target and OAR volumes. So, it is suggested that SU can be used up to 5% for parallel organs. For the structures with small volumes (such as small tumors, lens), Monaco does not recommend using per control point SU higher than 5%. If we use a higher value (higher than 5%), it causes the system to underestimate the cost function value assigned to that structure (Elekta, 2017). Significant variation was observed in average dose calculation time and the per control point SU. There is an exponential relationship observed between average dose K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 34 calculation time and the per control point SU (Figure 6). The dose calculation time may not be too long. Also, the gamma index showed a good pass rate for all three different diagnoses (such as Larynx, Oesophagus, and Prostate), and there are no significant variations were observed in the gamma pass rate for all three diagnoses. The final dose uncertainty should be approximately 1% for the entire plan (Elekta, 2017), and it shouldn’t be greater than 1% for the entire plan (Figure 7). Finally, based on all the measured average results, as well as considering other important factors and constraints, we recommend maintaining a control point SU value of 3% without compromising the quality or delivery of the plan. Additionally, we analyzed the percentage variations concerning the 3% per control point SU for the Larynx, Oesophagus, and Prostate (refer to Tables 6-8). Table 6. Percentage Variation values with respect to 3% for Larynx Target and OARs Larynx (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 66 D95% (cGy) 0.01 0.48 0.17 0.07 0.25 PTV 66 Dmax (cGy) 0.24 0.01 0.11 0.53 0.83 PTV 66 Dmean (cGy) 0.05 0.33 0.18 0.09 0.34 PTV 66 V95% (%) 0.09 0.69 0.05 0.05 0.46 PTV 66 Heterogeneity Index 0 0 0 0 0 PTV 66 Conformity Index 0 1.25 1.25 0 1.25 Brain Stem Dmax (cGy) 6.95 4.31 1.39 4.37 1.03 Spinal Cord Dmax (cGy) 0.06 0.02 0.24 0.92 0.05 Lt Parotid Dmean (cGy) 3.13 0.52 1.9 0.69 0.4 Rt Parotid Dmean (cGy) 2.66 0.66 0.75 1.87 2.19 Lt Lens Dmax (cGy) 1.62 0.91 3.33 0.97 3.72 Rt Lens Dmax (cGy) 0.16 1.09 2.5 0.39 4.01 Global Max of the Plan (%) 0.23 0 0.18 0.53 0.99 Gamma Pass Rate 3%, 3 mm 0.20 0.20 0.31 0.10 0.51 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 35 Table 7: Percentage Variation values with respect to 3% for Oesophagus Target and OARs Oesophagus (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 0.05 0.22 0.06 0.27 0.08 PTV 50.4 Dmax (cGy) 0.39 0.01 0.04 0.32 0.98 PTV 50.4 Dmean (cGy) 0.09 0.22 0.16 0.05 0.19 PTV 50.4 V95% 0.01 0.03 0.05 0.02 0.03 PTV 50.4 Heterogeneity Index 0.00 0.00 0.00 0.00 0.00 PTV 50.4 Conformity Index 0.00 0.00 0.00 2.94 0.00 Heart Dmean (cGy) 0.75 0.33 0.41 0.53 0.22 Spinal Cord Dmax (cGy) 0.45 0.24 0.49 0.10 0.82 Rt Lung Dmean (cGy) 0.05 0.36 0.02 0.69 0.24 Lt Lung Dmean (cGy) 0.19 0.06 0.00 0.49 0.05 Lt Kidney Dmean (cGy) 2.78 2.16 0.79 1.06 0.89 Rt Kidney Dmean (cGy) 0.25 0.17 0.32 0.56 0.49 Global Max of the Plan (%) 0.40 0.02 0.04 0.31 0.98 Gamma Pass Rate 3%, 3 mm 0.41 0.41 0.20 0.20 0.51 Table 8. Percentage Variation values with respect to 3% for Prostate Target and OARs Prostate (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 0.31 0.11 0.25 0.63 0.87 PTV 78 Dmax (cGy) 0.88 0.52 0.26 0.18 1.04 PTV 78 Dmean (cGy) 0.36 0.14 0.14 0.11 0.06 PTV 78 V95% (cm3) 0.1 0.01 0.14 0.53 0.76 PTV 78 Heterogeneity Index 0 0 0 0.97 1.94 PTV 78 Conformity Index 2.86 0 1.43 2.86 1.43 Rectum Dmean (cGy) 0.84 0.49 0.29 1.1 1.46 Bladder Dmean (cGy) 0.6 0.24 0.88 1.59 1.59 Lt Pelvic Bone Dmean (cGy) 0.07 0.48 0.49 0.5 1.05 Rt Pelvic Bone Dmean (cGy) 0.44 0.45 0.57 0.05 1.1 Global Max of the Plan (%) 0.84 0.34 0.36 0.28 1.04 Gamma Pass Rate 3%, 3 mm 0.41 0.21 0.10 0.31 0.10 According to the calculated percentage variation values (Table 6-8) with respect to a 3% per control point SU, showed no any significant variation between the dosimetric indices and the per control point SU (%) values for all three diagnoses (Larynx, Oesophagus, and Prostate). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 36 CONCLUTION This study proposed an optimal acceptable range for the per Control Point Statistical Uncertainty (SU) in Monte Carlo Dose calculations during IMRT planning in Monaco 5.11.02 Treatment Planning System (TPS). Based on the measured average results, a 3% per control point SU is acceptable for all three diagnoses (Larynx, Oesophagus, and Prostate) in IMRT planning, allowing for reduced calculation time without compromising target coverage, Organ at Risk (OAR) doses, or plan delivery. Conflict of Interest None declared. Financial Disclosure The authors did not receive any kind of grant or financial support from any organization for the present study. REFERENCES [1] Bortfeld, T. (2006). IMRT: A review and preview. Physics in Medicine and Biology, 51(13), R363– R379. https://doi.org/10.1088/0031-9155/51/13/R21 [2] Carol, M. P. (1995). 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Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39