Table of Contents

Original Articles

Cervical Cancer Incidence and Trends among Nationals of the Gulf Cooperation Council States, 1998-2012 .....................................06Eman Alkhalawi, Amal Al-Madouj, Ali Al-Zahrani

Vitamin D Receptor and Role of Vitamin D Supplementation in Advanced Gallbladder Cancer: A Prospective Study from Northern India ..................................................................................................................................................13Sanchit Mittal, Akshay Anand, Aarthi Vijayashankar, Abhinay Arun Sonkar, Nuzhat Husain, Abhijit Chandra

Total Parenteral Nutrition in Middle Eastern Cancer Patients at End of Life: Is it Justified? ..................................................................20Elie Rassy, Tarek Assi, Ziad Bakouny, Rachel Ferkh, May Fakhoury, Hanine Elias, Aline Tohme, Fadi El Karak, Fadi Farhat,

Georges Chahine, Fadi Nasr, Marwan Ghosn, Joseph Kattan

Implementation and Evaluation of Phantomless Intensity Modulated Radiotherapy Delivery Verification Using FractionCHECK .......25Buchapudi Rekha Reddy, Manickam Ravikumar, Chandraraj Varatharaj, Nathan Childress, Tirupattur Rajendran Vivek

Gastric Adenocarcinoma in a Moroccan Population: First Report on Survival Data................................................................................35N. Lahmidani, S. Miry, H. Abid, M. El Yousfi, D. Benajah , A. Ibrahimi, M. El Abkari, A. Najdi

Trends and Patterns of Primary Hepatic Carcinoma in Saudi Arabia .......................................................................................................40Fazal Hussain, Shazia Anjum, Njoud Alrshoud, Asif Mehmood, Shouki Bazarbashi, Aneela N. Hussain, Naeem Chaudhri

Trend and Characteristics of Endometrial Cancer in Lagos, Nigeria ........................................................................................................51Adeyemi Adebola Okunowo, Morakinyo Abiodun Alakaloko, Ephraim Okwudiri Ohazurike, Kehinde Sharafadeen Okunade, Rose Ihuoma Anorlu

Open Nephron Sparing Surgery for T1a Renal Tumors: Clinical Experience in an Emerging Country ....................................................59A. Fetahu, Xh. Çuni, I. Haxhiu, R. Dervishi, L. Ҫuni, S. Manxhuka, L. Shahini

Consumption Coagulopathy in Paediatric Solid Tumours: A Retrospective Analysis and Review of Literature ....................................65Yamini Krishnan, Smitha B, Sreedharan P. S.

Case Reports

Clinicoradiological Discrepancy in Multisystem Langerhans Cell Histiocytosis with Central Nervous System Involvement ...............71Hussein Algahtani, Bader Shirah, Mohammed Bajunaid, Ahmad Subahi, Hatim Al-Maghraby

Primary Ewing’s Sarcoma of Maxillary sinus: A Case Report ..................................................................................................................77Chauhan Richa, Trivedi Vinita, Kumari Nishi, Rani Rita, Singh Usha

A Diagnostic Dilemma of Sinonasal T Cell Lymphoma: Report of A Unique Case and Literature Review ..............................................82Selvamalar V, S.P. Thamby, Mohammad Ahmed Issa Al-Hatamleh, Rohimah Mohamud, Baharudin Abdullah

Conference Highlights/Scientific Contributions

• BookReview-IARCHandbooksonCancerPrevention,Volume17ColorectalCancerScreening ...................................................89

• NewsNotes ............................................................................................................................................................................................91

• ScientificeventsintheGCCandtheArabWorldfor2019 ..................................................................................................................95

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Corresponding author: Dr. Manickam Ravikumar, Director & Professor of Radiation Physics,

Department of Radiotherapy, Sri Shankara Cancer Hospital & Research Centre, Basavanagudi, Bangalore,

India -560004. Phone: +91-80-26981007. Email: drmravi59@yahoo.com

Abstract

Introduction: The objective was to implement the DynaLog file-based automated FractionCHECKTM software which verifies the delivery accuracy of all patient treatments without any measuring devices and evaluate this tool for dynamic IMRT delivery verification of 40 cases. The present work was aimed to find the generalized results with user-defined tolerances of all treatment deliveries and asses the delivery results in terms of Pass/Warn/fail analysis. In addition to this, an in-depth analysis of mean RMS error of each BANK of the MLC’s and mean 95th Percentile error of all the leaves and percentage of pixels passing Gamma between the planned and delivered fluences for all 40 dynamic IMRT cases.

Methods and Materials: The DynaLog files were analyzed from all 40 IMRT deliveries which include ten plans each of head & neck, brain, cervix and oesophagus patients treated using 6 MV X-ray beam at DHX linear accelerator equipped with Millennium120 multileaf collimator (MLC). Before processing actual patient data, we have validated the FractionCHECKTM software using routine DMLC QA test fields such as X-wedge, Y-Wedge, Garden fence test and Pyramid Test. The mean RMS error and 95th percentile of each leaf was studied in four different sites of dynamic IMRT and fluence delivery accuracy at 1%, 1mm gamma criterion was determined over the entire course of 40 patients.

Results: We have analyzed a total of 1134 fractions of dynamic IMRT cases of 40 patients in this study. Out of 25,166 log files evaluated for the entire course of these patients, 23,906 files have met pass criteria (Green), 1022 log files resulted in warning (Yellow), and 238 files have resulted in failure mode (RED) in the analysis. The overall mean RMS error for all 40 IMRT cases was found to be 0.34±0.08 mm, and the mean 95th percentile error of all leaves was 0.66 ± 0.18 mm, which was well below the recommended by TG142 as well with other published works.

Discussion and Conclusion: In a busy department where a large number of IMRT cases are being treated, the DynaLog analysis-based FractionCHECKTM software can be used as one of the efficient methods in verifying IMRT treatment deliveries. It can be implemented much quicker without long delay and can also be used for routine DMLC QA tests. We conclude that FractionCHECKTM is a fast, simple and powerful tool for routine QA of the IMRT fields and that based on our test field and patient data results, FractionCHECKTM could be used for treatment delivery assessment accuracy.

Keywords: MLC, RMS error, IMRT, DynaLog file, FractionCHECK

Original Article

Implementation and Evaluation of Phantomless Intensity Modulated Radiotherapy Delivery Verification Using

FractionCHECK Buchapudi Rekha Reddy1, Manickam Ravikumar2, Chandraraj Varatharaj1, Nathan Childress3,

Tirupattur Rajendran Vivek4

1 Dept. of Radiation Physics, Kidwai Memorial Institute of Oncology, Bangalore, India. 2 Dept. of Radiotherapy, Sri Shankara Cancer Hospital & Research Centre, Bangalore, India.

3 Mobius Portfolio, Varian Medical Systems, CA, USA. 4 Twam Hospital, Al Ain, UAE.

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IntroductionThe introduction of intensity modulated radiation

therapy (IMRT) using multileaf collimator (MLC) delivery mode represents a substantial advancement with great potential for the discipline of radiotherapy (1). IMRT delivers highly conformal and complex dose distributions using multiple beams to create steep dose gradients to shield critical organs at risk (OARs) from adjacent high dose regions. The potential errors in IMRT treatment delivery include inaccuracies in dose calculation, plan transfer errors, beam delivery errors and target localization uncertainties (2).

The clinical implementation of IMRT requires the ability to verify very complex radiation beams. However, a practical drawback on the implementation of IMRT into the clinical routine remains where time-consuming patient-specific quality assurance (QA) that precedes the actual treatment must be undertaken. The most widely used form of pre-treatment QA for IMRT generally consists of absolute dose measurements combined with planar dose distribution measurements in a phantom. The phantom has to be equipped with dose measuring devices such as films, 2D ion-chamber or diode-based detectors. Even though the film dosimetry is widely accepted due to its spatial resolution, doing QA with the film is a time-consuming process, and also there are issues with uncertainties due to processor artifacts. Ion chamber and diode array-based detectors allow for fast analysis (3,4), but they have a reduced data density compared to film. Additionally, ion chambers exhibit marked volume averaging that requires the TPS calculated dose to be blurred prior to IMRT QA (5). The limited physics resources could impede the provision of IMRT for an increasing number of patients/treatment sites and the implementation of more complex delivery modalities. Thus, there is a need to develop a more efficient QA procedure to verify IMRT QA treatment deliveries. (6)

Dose homogeneity in patients at the time of dose delivery can only be assured if leaves are in the correct position at each moment regardless of the MLC design and delivery method. Because the dose delivered throughout the target volume with IMRT is sensitive to leaf positioning errors, and that even 1 mm error in positioning will produce about 5% errors in dose delivery, as reported by Thomas LoSasso et al. (7). Hence, the aim is to maintain the positioning errors below 0.2 mm and to ensure this, proper QA (8) of the MLC must be carried out regularly; these may include picket fence and garden fence tests with and without intentionally introduced errors. This test can detect the error in positioning by 0.2 mm as observed earlier (9). More specific evaluation

of the MLC control system and its function can be done using the information contained in the dynamic log files, called DynaLog Files in the case of MLC’s in Varian linear accelerator (linac) (10). These files contain leaf position and delivered dose fraction information recorded every 50 milliseconds. This file information can be used as part of the overall QA to evaluate the function of different parts of an IMRT system (11).

DynaLog files accessible on Varian linac’s provide the details of cumulative dose fraction, beam on status, segment number as well as expected and actual MLC leaf positions for both static and dynamic IMRT deliveries (12). Few studies have independently validated the accuracy of the DynaLog file beam data using film, EPID dosimetry and array detectors (13,14). DynaLog files can verify beam delivery in terms of MLC positional errors and fractional MU delivery (15) when used in conjunction with record and verify (R/V) data, DynaLogs can verify plan transfer (16). Monte Carlo-based dose verification, reconstructed from the DynaLog delivery data has been presented to check TPS IMRT dose calculation accuracy (17). Additionally, DynaLogs provide detailed information on machine performance, and unlike geometric phantom verification, DynaLogs enable diagnosis and offer the potential to predict errors in treatment field deliveries (18). The Task group no 142 (AAPM – TG 142) report recommends dynamic testing of MLC on an annual basis to assess the MLC positioning error values in terms of root mean square (RMS) error during delivery and 95th percentile error of all leaves for the entire course of treatment. They have also given the maximum tolerance for these both tests as 3.5 mm. (19)

In the present work, we have implemented the DynaLog file analysis using FractionCHECKTM (Mobius Medical Systems) software to assess the Dynamic IMRT plan deliverability. In total, we have analyzed more than 25,000 DynaLog files, over the full course of 40 patient’s treatment fractions. We assessed treatment delivery accuracy in terms of RMS error of all MLC leaves, 95th percentile error of all leaves and fluence delivery by using gamma analysis of planned and delivered fluence maps.

Materials and Methods

DynaLog file description

A DynaLog file is a file record of the actual dose fraction (dose dynamic) versus actual MLC leaf positions from either dynamic treatment or a segmental treatment, generated in ASCII format. A dynamic treatment is a treatment during which the MLC leaves, collimator, and gantry moves while the beam is ‘ON’ while dose-rate and speed of the leaves are continuously adjusted by the

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Phantomless QA for IMRT using DynaLog files, Buchapudi Rekha Reddy, et. al.

control system. The DynaLog data are taken every 50 ms by the MLC controller. These details are recorded and written to a file on the control system computer after each IMRT plan delivery.

FractionCHECKTM Software

FractionCHECKTM software is a part of Dose Lab Pro software package (Mobius Medical Systems, LP, TX, USA) which is intended to be used as part of a dosimetry verification system for linear accelerators and to be used as a routine QA purpose. FractionCHECKTM software can analyze thousands of treatment log files at once to find:

• Delivery errors in patient treatments

• Linac performance problems (such as beam off lag)

• MLC problems (such as leaves whose motors need to be replaced)

The main features of the software are described below:

• QA can be performed daily/weekly in 1 minute or performed continuously in the background in real-time.

• TG-142-compliant machine QA can be performed on test fields

• It features advanced data visualization tools, including beam fluences.

The following terms and the techniques are used in FractionCHECKTM while analyzing the data and the interface of the software as shown in Figure 1.

Figure 1. Screen shot of the FractionCHECKTM software Interface

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1. 95th Percentile |Error| (95P)—This value is calculated for each log file. It is the 95th percentile of positioning error magnitude for all moving MLC leaves. It is reported in mm.

2. Beam Hold—An accelerator asserts (turns on) a beam hold when the beam should be turned off. Beam holds are usually asserted due to a machine parameter out of tolerance, such as an MLC leaf too far from its expected position.

3. Beam Off Lag—The time between a beam hold being asserted and the beam actually turning off. FractionCHECK reports this in units of data cycles.

4. Fluence - The 2D distribution of radiation. Treatment files include the planned and delivered MLC positions as a function of fractional dose.

5. Gamma Passing Rate—When 2D planned and delivered fluences are compared, the gamma passing rate is the percent of pixels that pass gamma using the selected dose and distance criteria.

6. RMS Error—The RMS error is used to condense a set of errors into a single representative value. The RMS error is always greater than or equal to the mean of the absolute value of the errors. FractionCHECKTM calculates RMS errors for individual leaves in files, ranges of gantry angles in files, and entire leaf banks in a collection of files.

Planning and Delivery

The dynamic IMRT plans were optimized using Eclipse treatment planning systems (Version 8.1, Varian Medical Systems). We have analyzed a total of 40 Dynamic IMRT patients of four different treatment sites, which includes 10 patients each for head & neck, brain, cervix and oesophagus cases. All plans were created using 6 MV photons and equally spaced angles of the gantry and delivered with DHX linear accelerator (Varian Medical Systems, Palo Alto CA, USA) for dynamic IMRT treatments. The DHX linac is equipped with Millennium120 MLC’s. The head & neck plans were generated using 9 fields with gantry angle of 0ᵒ, 40ᵒ, 80ᵒ, 120ᵒ, 160ᵒ, 200ᵒ, 240ᵒ, 280ᵒ, 320ᵒ and the oesophagus and cervix plans were planned using 7 fields IMRT with gantry positioned at 0ᵒ, 51ᵒ, 102ᵒ, 153ᵒ, 204ᵒ, 255ᵒ, 306ᵒ. Some of the brain IMRT cases were planned using either by 7 fields or 5 fields with gantry angle of 0ᵒ, 51ᵒ, 102ᵒ, 153ᵒ, 204ᵒ, 255ᵒ, 306ᵒ or 0ᵒ ,72ᵒ ,144ᵒ, 216ᵒ,288ᵒ with collimator and couch positioned at 0ᵒ. Prior to treatment, all the plans have passed our institute’s IMRT patient-specific QA protocol of absolute dose within ±2% using ion chamber measurements and fluence matching of 95% and above with 3% / 3 mm gamma criteria measured using 2D array of ion chambers.

The FractionCHECKTM software was validated using routine DMLC QA test fields prior to treatment delivery analysis. The minimum and maximum leaf speed, maximum beam off log and fluence delivery accuracy at 1%, 1mm gamma criterion were determined over the entire course of 40 patients’ treatment delivery. Though we have tried to include all fractions of entire course of IMRT treatments, we might have missed one or two fractions data for few patients due to termination of treatment due to skin reactions or any other clinical reasons.

The results were displayed in a different color in the analysis box of the program. The ‘Green’ color indicates pass, ‘Yellow’ signifies warning and ‘Red’ shows fail analysis. The tolerance level for beam off log was set at 0-1 for pass, 2 for warning and 3 for fail mode. For the gamma analysis, the distance to agreement (DTA) was set at 1 mm, and the percentage dose difference (% DD) was set at 1%. The percentage of pixels passing gamma will be shown as pass for more than 96% matching and warning for 95% to 96% and fail for less than 95% matching of intended and delivered fluences. The entire warning/ fail tolerance criteria used in the present study is as shown in Figure 2.

In addition to the above analysis the FractionCHECKTM software also provides mean RMS leaf error, 95th percentile error, gantry minimum, maximum and mean speed, beam ON time and maximum log. In the present work, it is aimed to find the generalized results with user-defined tolerances of all treatment deliveries and the results were projected in terms Pass/Warn/fail. In addition to this an in-depth analysis of mean RMS error of each BANK of the MLC’s and mean 95th percentile error of all the leaves and percentage of pixels passing gamma between the planned and delivered fluences for the 40 dynamic IMRT cases were also analyzed.

Results Before using the FractionCHECKTM software for

analyzing the routine IMRT patient data, we have validated the software using standard DMLC QA test fields such as Garden fence test, Picket fence test, X-Wedge. Y- Wedge, and Pyramid test fields. All the test results have

Figure 2. Screen shot of the tolerance criteria used for warn / fail for the analysis of 40 patients.

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Phantomless QA for IMRT using DynaLog files, Buchapudi Rekha Reddy, et. al.

shown 100% gamma pass with set criteria of 1%, 1mm with a threshold of 10%. In addition to this, to verify the accuracy of the results, intentional errors were created manually in the TPS for the leaf numbers 20, 30, 35 in the BANK A and leaf numbers 30 and 40 in the BANK B and have been evaluated for the Garden fence test. The errors

created manually in TPS were seen clearly in the results, as shown in Figure 3. The comparison of planned and delivered fluence, gamma passing percentage and mean and maximum leaf speed for each leaf for a particular field of a head & neck case is shown in Figure 4.

Figure 3. A) Garden fence test field results in terms of max and mean leaf speed without any error. B). Manually created error for Garden fence test field results in terms of max and mean leaf speed.

A

B

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Figure.4. A). Comparison of planned and delivered fluence B)MeanandMaxleafspeedforeachleavesofahead&neckcase.

A

B

In the present study, 40 cases of four different sites which include 1134 fractions were analyzed. Out of these 40 cases with 25166 log files evaluated, 23906 files have met pass criteria (Green), 1022 log files resulted in warning (Yellow), and 238 files have resulted in failure mode (RED)

in the analysis. The overall details of the number of files analyzed with their results for each treatment site are shown in Table 1.

Figure 5 shows the mean RMS error for ten patients each of head & neck, brain, cervix and oesophagus

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Phantomless QA for IMRT using DynaLog files, Buchapudi Rekha Reddy, et. al.

Table 1. Summary of patient data included in this study

S. No Treatment Site No. of patients No. of fractionsNo. of DynaLog

files

Results

PASS WARNING FAIL

1 H& N 10 288 7352 6983 299 70

2 Brain 10 272 3396 3226 138 32

3 Cervix 10 306 8030 7628 326 76

4 Oesophagus 10 268 6388 6069 259 60

Total 40 1134 25166 23906 1022 238

Figure 5. Mean RMS errors between actual MLC positions and planned MLC positions for 40 dynamic IMRT patients in the entire course of treatment which includes A) H&N Cases, B) Brain cases, C) Oesophagus cases and D) Cervix cases.

cases for all the leaves of MLC Bank A and MLC Bank B. The mean RMS error for Bank A leaves was found to be 0.32±0.08 mm, 0.33±0.07 mm, 0.35±0.08 mm and 0.34±0.08 mm respectively for the head & neck, brain, cervix and oesophagus dynamic IMRT cases. The results for the same cases for Bank B leaves were 0.32±0.09 mm, 0.33±0.07 mm, 0.36±0.09 mm and 0.34±0.09 mm. The overall mean RMS error for all 40 IMRT cases was found to be 0.34±0.08 mm.

The results for mean 95th Percentile error for Bank A and Bank B for ten patients each of head & neck, brain, cervix and oesophagus cases, are shown in Figure 6.

The mean 95th percentile error for all leaves in Bank A for all above cases was 0.67 ± 0.15 mm, 0.63 ±0.17 mm, 0.68 ±0.16mm and 0.65 ±0.19 mm respectively. Similarly, for Bank B leaves the mean 95th percentile errors for the above said cases were found to be 0.67 ± 0.19 mm, 0.62 ±0.16 mm, 0.68 ±0.19 mm and 0.65 ±0.19 mm respectively. The overall mean for the entire 40 patients was 0.66 ± 0.18 mm. The results indicated that the mean RMS error of the leaves of both BANK’s of MLC and mean 95th percentile was not found to be treatment site-specific. The mean percentage of pixels passing 1% 1 mm Gamma criterion <1 for 40 dynamic

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IMRT patients for the entire course of the treatment was above 99% (99.19% ± 1.32%), and the respective mean graphical representation might not show any differences qualitatively; hence a first day treatment delivery results of all the patients are shown in Figure 7.

DiscussionThe DynaLog files of the delivered dynamic IMRT

consists of significant information which can be used to assess the delivery errors, as with the study by Dinesh Kumar et al., (20). In their study, they stated that DynaLog file is a promising tool for IMRT QA automation which reduces the time spent for IMRT QA by physics staff and provides a tool to analyze entire IMRT day-to-day delivery.

The universally followed routine IMRT patient-specific QA methods are mainly based on measurements on QA phantoms which is time-consuming and potentially inaccurate since the measurement is done in phantoms rather than actual patients. It was debated that it would be more accurate and considerably less time consuming to perform IMRT QA with software rather than hardware (21). The DynaLog files were analyzed with user-defined tolerances as per published recommendations like TG

142. It was found that 95% of the log files could pass the set criteria (green) and 4% have resulted in warning level (yellow) and 1% files failed to meet the set tolerances (red). It was observed that the reasons behind this warning and failed results could be due to the maximum number of beam lag and percentage of pixels passing gamma is less for few fractions. The reason for log files not meeting set tolerance could be due to a defective MLC, speed of the MLC leaf was slow while beam is ON and others reasons include machine malfunctioning, dose rate fluctuation and internal MLC tolerance setting.

In a multi-institution study (22), the positioning accuracy of MLC by analyzing DynaLog files of 6 linacs from 4 different centres was performed The metrics assessed were those proposed by TG-142, which includes leaf RMS error and 95th percentile error. The average values obtained were 0.306 mm and 0.693 mm for Varianlinacs in IMRT treatments. They have concluded that DynaLog files contain a great deal of useful information. Agnew et al., (23) evaluated the use of Varian radiotherapy dynamic treatment log files to verify IMRT plan delivery by comparing the planned and delivered beam data in terms of MLC leaf position errors. In their study they

Figure 6. Mean 95th Percentile errors between actual MLC positions and planned MLC positions for 40 dynamic IMRT patients in the entire course of treatment which includes A) H&N Cases, B) Brain cases, C) Oesophagus cases and D) Cervix cases.

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Phantomless QA for IMRT using DynaLog files, Buchapudi Rekha Reddy, et. al.

have evaluated all treatment fractions which comprise of 5 prostate, 5 prostate and pelvic node (PPN) and 5 head & neck step and shoot IMRT plans totaling 82 IMRT fields with 5500 DynaLog files. From this study, it was concluded that DynaLogs are efficient and effective tool for QA of IMRT plan transfer and beam deliverability.

Olasolo-Alonso et al., (24) examined MLC positional accuracy via MLC logs from six institutions and three delivery techniques in order to evaluate typical positional accuracy and mechanical parameters that affect accuracy. The achieved accuracy was compared against TG-142 recommendations for MLC performance. They have found that leaf RMS error and 95th percentile leaf error was consistent between institutions but varied by treatment type. For dynamic treatments the mean RMS leaf error was 0.32 mm, and 95th percentile leaf error was 0.64 mm. Most leaf errors were found to be well below TG-142 recommended tolerances of 3.5 mm, which is critical to understanding the current performance level of MLCs. They have recommended that tolerances of leaf RMS and error counts for all treatment types should be tightened more appropriate for clinical performance than the TG-142 recommended limits. The values of 1 mm for the

Figure 7. Percentage of pixels passing 1% 1 mm Gamma criterion <1 for 40 dynamic IMRT patients on the first day of the treatment which includes A) H&N Cases, B) Brain cases, C) Oesophagus cases and D) Cervix cases

95th percentile of leaf RMS error and 1.5 mm for the 95th percentile leaf error were suggested as action levels for all treatment techniques. The present work also confirms this recommendation with the results of mean RMS error for the entire delivery of 40 dynamic IMRT patients at 0.34±0.08 mm and the mean 95th percentile error of all leaves at 0.66 ± 0.18 mm. The obtained limits are well below the recommended by TG142 and they are comparable with other published works (22,24).

ConclusionIn conclusion, FractionCHECKTM is fast, simple, and

powerful tool for the routine QA of the IMRT fields. Its efficient workflow allows each field of every fraction of all patient treatments to be verified. Its data visualization makes it possible to analyze thousands of treatment logs at once and draw meaningful conclusions using a set of tolerances. It also allows physicists to simply look for pass, warn, and fail results. In addition to this, we have extensively analyzed the DynaLog files in terms of mean RMS error, and 95th percentile error of all MLC leaves and found that it is well below the TG 142 recommended value. In a busy department where a large number of

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IMRT cases are treated, this DynaLog analysis-based FractionCHECKTM software can be one of the efficient methods to verify the IMRT treatment deliveries in a short period of time and. without much delay. It can also be used for routine DMLC QA tests. Based on our test field and patient data results, FractionCHECKTM could be used for treatment delivery assessment accuracy.

AcknowledgementThe authors would like to thank the service engineers,

C. Gobinathan and Azmath Ulla (Varian Medical Systems) for their timely technical help as and when required.

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