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Original Article

Progress in Medical Physics 2022; 33(3): 25-35

Published online September 30, 2022 https://doi.org/10.14316/pmp.2022.33.3.25

Comparison of Dose Statistics of Intensity-Modulated Radiation Therapy Plan from Varian Eclipse Treatment Planning System with Novel Python-Based Indigenously Developed Software

Sougoumarane Dashnamoorthy1 , Karthick Rajamanickam1 , Ebenezar Jeyasingh2 , Vindhyavasini Prasad Pandey3 , Kathiresan Nachimuthu1 , Imtiaz Ahmed4 , Pitchaikannu Venkatraman5

1Department of Radiation Oncology, Thangam Cancer Hospital, Namakkal, Tamil Nadu, 2PG & Research, Department of Physics, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, Tamilnadu, 3Department of Medical Physics, Hind Institute of Medical Sciences and Hospital, Lucknow, Uttar Pradesh, 4Department of Radiation Oncology, KLES Belgaum Cancer Hospital, Belgaum, Karnataka, 5Department of Medical Physics, Bharathidasan University,Trichy, India

Correspondence to:Sougoumarane Dashnamoorthy
(dsougoumarane1969@gmail.com)
Tel: 91-8951768508

Received: May 6, 2022; Revised: August 8, 2022; Accepted: August 12, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose: Planning for radiotherapy relies on implicit estimation of the probability of tumor control and the probability of complications in adjacent normal tissues for a given dose distribution.
Methods: The aim of this pilot study was to reconstruct dose-volume histograms (DVHs) from text files generated by the Eclipse treatment planning system developed by Varian Medical Systems and to verify the integrity and accuracy of the dose statistics.
Results: We further compared dose statistics for intensity-modulated radiotherapy of the head and neck between the Eclipse software and software developed in-house. The dose statistics data obtained from the Python software were consistent, with deviations from the Eclipse treatment planning system found to be within acceptable limits.
Conclusions: The in-house software was able to provide indices of hotness and coldness for treatment planning and store statistical data generated by the software in Oracle databases. We believe the findings of this pilot study may lead to more accurate evaluations in planning for radiotherapy.

KeywordsMatrix laboratory, Python, Intensity-modulated radiation therapy, 3 dimensional conformal radiation therapy, Normal tissue complication probability, Tumor control probability

1. Ethics statement

This study was approved by the Institutional Review Board of Thangam Cancer Center (approval number: ECR/1069/Inst/TN/2018/RR-21). Informed consent was obtained from all patients.

2. Commercial language

MATLAB is a popular numerical computing environment and programming language. The major limitations of MATLAB are its commercial nature, expense, and the proprietary nature of algorithms. To overcome these limitations and develop a user-friendly alternative system, we evaluated the Python programming language as a tool for DVH analysis and biological planning.

The advantages of Python over MATLAB are lower licensing costs, extensive standard libraries, powerful data types, free availability, and cross-platform nature. Further, viewing and modifying source code, reading, programing, and turning ideas into code are technically less challenging in MATLAB. Finally, classes and functions can be defined anywhere within the program.

4. Graphical user interface

Matplotlib is a plotting library for the Python programming language and a numerical mathematics extension of NumPy. Matplotlib provides an object-oriented application programming interface (API) for embedding diagrams in applications using general-purpose graphical user interface toolkits such as Tkinter.

5. Oracle and data structures

Outputs from Python software were stored in an Oracle database. Oracle is free to download. Oracle Database 19c Standard Edition 2 Release 19.0.0.0.0 production version (Oracle USA Inc, CA, USA) was used in the present study.

6. Patient information

The present study recruited ten patients receiving RT in Thangam Cancer Hospital, Namakkal for head and neck cancer categorized under the 8th Edition of the American Joint Committee on Cancer Staging. IMRT treatment was planned for each patient.

7. DVH import

After IMRT optimization with commercial TPS such as Eclipse, the approved DVH can be imported into our software and used to compare dose statistics. The DVH text file from the graphical DVH is independent of the standard format version of the relative mode of the TPS used; version 11.0.31 (Eclipse 15.6; Varian Medical Systems, CA, USA) is currently used in this program. The size of exported DVH text files ranges from a few kilobytes to more than a few thousand lines, which is read as the input file for the Python program. Once the software is running, the application reads the text file of the patient’s DVH and stores the structures and target volumes of the DVH in special arrays called NumPy arrays. Stored arrays can be viewed in a histogram format. Many software programs have been designed and developed to biologically assess treatment plans since the 2000s [7]. Most software applications are developed in MATLAB, with few programs coded in C, Visual Basic, or Java programming languages. We believe this to be the first software program specifically designed in the high-end programming language, Python, using front-end Tkinter and back-end Oracle to store outputs generated by the program [8].

8. Dose-volume histogram analysis

We choose cumulative DVH as the software input as the Python program is designed to read cumulative relative DVH as an input. The software internally converts the accumulated DVH into the differential DVH according to the program requirement [8]. This basic Python code was developed to export Eclipse DVH text files and reconstruct graphical DVH for physical and biological assessment of treatment plans [9]. Python software calculates volume and minimum, maximum, and mean doses from special arrays, called NumPy arrays, stored in dose data bins. In addition to dose statistics, organ at risk (OAR) volumes can also be determined in Python to check the compatibility of the original DVH from the Eclipse TPS. A specific window of the software allows reading of the DVH file and the corresponding analysis of DVH to be performed. Volumes and dose statistics can be visualized for all OARs and planning target volumes (PTVs). Python software allows scaling, panning, enlargement, export, printing, and saving of the DVH to a desired destination.

Differential dose-volume histograms (dDVHs) obtained by Python software from DVH text files from a commercial TPS (Eclipse; Varian Medical Systems) were used to calculate IOH and IOC values for SeqB and SIB-IMRT using equations 1 and 2 shown below. In these equations, V is the total volume of the target, vi, Di denotes the ith bin of the differential, DVH is the volume of the ith voxel in the target volume, and DiRx and DiPlan are the prescribed and planned dose of the ith voxel, respectively.

IOH and IOC represent overdose or underdose at the target as evaluated by DVH. To assess the feasibility of the proposed index benchmark, calculations were performed using an example IMRT plan from a previous study [10].

IOH=1+Di,PlanDi,RxDiRx2XviV(Vi=0ifDiPlanDiRx)
IOC=1Di,PlanDi,RxDiRx2XviV(Vi=0ifDiPlanDiRx)

The software includes Oracle database connectivity to allow storage of generated outputs as a backup.

1. Computed tomography

Immobilization was achieved with commercially-available rigid devices including headrests, leg abductors, and shoulder retractors. Computed tomography (CT) scans were performed in the supine position with a resolution between 0.93 to 0.98 mm in the axial plane and a slice thickness of 2.5 mm using a special GE Discovery IQ PET–CT 16 slice machine (GE Health care, Chicago, IL, USA). Data were exported to Eclipse version V15. PTV and OAR volumes were contoured by the attending radiation oncologist.

2. Treatment parameters

A seven-field IMRT plan was initially created using Eclipse TPS for standard fractionation treatment. The IMRT plan consisted of seven beam angles (gantry angles; 51°C, 102°C, 153°C, 204°C, 255°C, and 306°C) with the entire beam having a 6 MV beam quality. The dose calculation algorithm was performed by the Analytical Anisotropic Algorithm after use of the Leaf Motion Calculator [11].

3. Image verification

Cone beam computed tomography imaging guidance was applied to all patients using the Clinac iX integrated imaging system (Varian Medical Systems). A two-stage matching strategy was used. First, a fully automated registration based on bony anatomy (bone matching [BM]) was performed. After BM, matching was adjusted by a physician using direct visualization of the center of mass shift. After performing the fitting procedures, a final correction of translations and roll angle rotation (axis of rotation, head–toe) was automatically applied. Patients were subsequently treated with delivery of five fractions per week for precise dose delivery.

4. DVH dose statistic comparison

The Python program reads DVH text files in the relative dose mode (kilobyte size) rather than the absolute dose mode (megabyte size). The displayed DVH is then processed by the Python program in milliseconds (less than 1 second). The text file generated from the DVH by Eclipse TPS occurs at speeds in M/s. The Varian Medical System plan for IMRT is passed as an input to the developed program [12]. Dose statistics calculated using this software program is shown in Table 1. The maximum/mean dose values are within the acceptable deviation (0.3%), while minimum dose values had a deviation of 2%.

Dose statistics calculated in Eclipse or Python from DVH graphs in patients with IMRT plans for head and neck cancers

PatientPrimary siteStagingOrgan at riskMinimum dose (cGy)Maximum dose (cGy)Mean dose (cGy)

Varian EclipsePython software% deviationVarian EclipsePython software% deviationVarian EclipsePython software% deviation
1Ca hypopharynx4ABrain_stem60.7584.442,0142,0090.24180.5182−0.83
Spinal cord12.71113.383,250.23,2410.281,651.11,652−0.05
Parotid_left230.62242.865,464.75,4600.082,332.12,338−0.25
Parotid_right266.52660.185,496.95,4670.542,581.32,583−0.06
2Ca tongue4ABrain_stem48.3480.627377320.67119.5120−0.41
Spinal cord625.36240.203,281.23,2760.152,272.32,274−0.07
Parotid_left172.41682.556,230.46,2280.032,468.22,472−0.15
Parotid_right80.2782.741,289.11,2840.39402.7408−1.31
3Ca tongue4ABrain_stem109.11062.842,5672,560.50.25323.2328.8−1.73
Spinal cord299.8293.82.003,566.33,560.90.152,591.52,595.5−0.15
Parotid_left2402371.255,923.65,918.60.082,628.62,630.5−0.07
Parotid_right270.2265.81.625,8765,876.6−0.012,5882,588.4−0.01
4Ca pyriform fossae4BBrain_stem112.6111.90.622,465.72,462.60.12294.4300.8−2.17
Spinal cord4241.90.232,977.42,973.30.131,934.71,937.9−0.16
Parotid_left387.7384.80.746,131.36,135.5−0.063,356.63,358.1−0.04
Parotid_right439.3433.71.276,194.36,191.50.043,277.23,281.1−0.11
5Ca cricopharynx4ABrain_stem135.5132.91.913,134.93,134.20.02829832.5−0.42
Spinal cord100.397.942.353,086.53,085.20.041,759.91,763−0.17
Parotid_left1,140.51,140.30.016,922.96,9190.053,418.13,421−0.08
Parotid_right638.8636.60.346,923.76,9190.063,448.73,456−0.211
6Ca supraglottis3Brain_stem49.147.04.2349.9348.10.5113.8112.21.4
Spinal cord128.91262.2498063,503.83,5000.108452,209.32,212−0.122211
Parotid_left1931892.0725396,063.46,0620.023091,280.71,288−0.570001
Parotid_right179.41752.452626,179.36,1740.085771,330.21,3300.0150353
7Ca pyriform fossae3Brain_stem110.81091.6245494,668.74,662.80.126371,034.21,036−0.174048
Spinal cord25.4239.4488194,592.54,5920.010892,467.12,471−0.15808
Parotid_left312.83081.5345275,7085,7050.052563,103.53,108−0.144998
Parotid_right283.12801.0950197,046.17,0420.058193,3713,374−0.088994
8Ca pyriform fossae3Brain_stem90.4847.0796463,413.83,4090.140612,134.82,142−0.337268
Spinal cord2672660.3745326,555.56,5520.053392,419.82,422−0.090917
Parotid_left271.62662.0618566,401.46,3980.053112,4412,450−0.368701
Parotid_right1,3161,3210.3799396,9096,913.1−0.05933,6053,604.50.0138696
9Ca supraglottis3Brain_stem53.4522.621723556.1549.31.2228118.7122.1−2.864364
Spinal cord105.3101.73.4188033,372.13,371.70.011861,727.91,729.9−0.115747
Parotid_left201196.72.1393035,867.95,861.40.110771,827.41,831.7−0.235307
Parotid_right201196.72.1393035,919.35,915.60.062512,069.72,075.9−0.29956

DVH, dose-volume histogram; IMRT, intensity-modulated radiotherapy.

When optimizing with a Millennium MLC-120 multi-leaf collimator, the minimum dose criterion of 95% of the prescribed dose of the PTV was given top priority [13]. The American Association of Physicists in Medicine, Radiation Therapy Oncology Group (RTOG), and Quantitative Analyzes of Normal Tissue Effects in the Clinic dose limit targets were used for the OAR. For all patients, both dose distributions were calculated based on planning CT scans with a grid size of 2.5×2.5 mm2. The text file of the DVH after the energy fluence calculation was exported in text format from the Eclipse system. The energy fluence of the treatment fields after treatment plan optimization is shown in Fig. 1. The DVH with target volumes of several organs, including the brainstem, spinal cord, and parallel similar parotid glands, are shown in Fig. 2.

Figure 1.Energy fluence of treatment fields after treatment plan optimization.

Figure 2.Dose-volume histograms with target volumes for organs at risk in Eclipse treatment planning system.

5. Dose statistics

Dose statistics including the minimum, maximum, and mean dose to the OAR were plotted in Microsoft Excel to compare the values calculated by Eclipse and Python (Fig. 35).

Figure 3.Minimum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Minimum dose statistics of brain stem. (b) Minimum dose statistics of spinal cord. (c) Minimum dose statistics of parotid right. (d) Minimum dose statistics of parotid left.

Figure 4.Maximum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Maximum dose statistics of brain stem. (b) Maximum dose statistics of spinal cord. (c) Maximum dose statistics of parotid right. (d) Maximum dose statistics of parotid left.

Figure 5.Mean dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Mean dose statistics of brain stem. (b) Mean dose statistics of spinal cord. (c) Mean dose statistics of parotid right. (d) Mean dose statistics of parotid left.

The results generated by the software were recorded. An application screen was used to show dose statistics with hot and cold spots for head and neck cases as shown in Fig. 6. A window on the left side of the screen allows the desired patient details to be selected. The lower part of the screen shows the total volume and minimum, maximum, and average doses for each plan and structure. A corresponding histogram is displayed in the middle of the screen in the middle window. The toolbar in the graphical window can be used to resize, pan, and enlarge the DVH and export DVH image files for printing [14].

Figure 6.Screenshot of the application program with dose statistics and hot and cold indices.

6. Analyses of hotness and coldness indices

Based on the consistency and reproducibility of the DVH plot, the IOH and IOC for cervical SIB-IMRT were calculated and tabulated in Table 2. The results are displayed on the same application screen at the bottom of the window. For all indices, 1 indicates a perfect match between the prescribed and planned dose, while values further from 1 indicate greater dissimilarity. It should be noted that the IOC has values equal to or less than 1, representing under dosing of the target volume, while the IOH has values equal to or greater than 1.

Hot and cold indices calculated in the Python software program from SIB-IMRT treatment plans for patients with head and neck cancers

PatientPrimary siteStageTarget volume 70Target volume 63Target volume 60Target volume 59.4Target volume 56Target volume 54Target volume 50

HotColdHotColdHotColdHotColdHotColdHotColdHotCold
1Ca hypopharynx4A1.0540.9461.0950.905
2Ca tongue4A1.0050.9951.0390.961
3Ca cricopharynx4A1.0080.9921.0580.9421.090.91
4Ca tongue4A1.0060.9941.0780.9221.110.89
5Ca pyriform fossae31.0040.9961.040.961.0780.922
6Ca pyriform fossae31.0070.9931.0410.9591.080.92
7Ca pyriform fossae4B1.0080.9921.0570.9431.0920.908
8Ca supraglottis31.0040.9961.0580.9421.090.91
9Ca supraglottis31.0080.9921.0690.941.0920.908

Deviations from the hotness and coldness indices were analyzed and presented graphically in Fig. 7. The resultant graph shows that higher target volumes reduce deviation when the dose-volume constraint is prioritized in the optimization window of the Eclipse TPS.

Figure 7.Indices of hotness and coldness from simultaneously integrated boost-intensity-modulated radiotherapy (SIB-IMRT) treatment plans for the entire target volume. PTV, planning target volume.

We believe this to be the first biology-based treatment plan evaluation software written in the high-end Python programming language with DVH text files generated from the Varian Eclipse commercial planning system. Generated outputs can be displayed in Tkinter frames and simultaneously stored in the Oracle database for further use [15].

We termed this software program the Relative Dose System (RDS) as relative DVH in text files are imported as inputs by the software. The software extracts data points from the text file and stores them in NumPy arrays allowing reconstruction of DVH and analysis of biological and physical parameters. We demonstrate the data points of the generated DVH chart were in excellent agreement with the Eclipse TPS. Clinically, the RDS software has greater utility in examining DVH statistics from SIB-IMRT plans, a newly developed approach for the application of different radiation doses to different areas in a single session [16].

This software was developed using the high-end Python programming language, which compares Eclipse TPS dose statistics with reconstructed DVH in the Python program [17]. This preliminary and pilot study demonstrates dose statistics including mean, minimum, and maximum doses for tumors or targets and OAR can be obtained from graphical DVH reconstructed from text files generated from planning DVH [18].

We compared dose statistics calculated from DVH generated by Eclipse and DVH reconstructed in Python and found that the mean deviation in maximum dose values was less than 0.1% and the mean deviation in mean dose values was less than 0.3%. The mean deviation in minimum doses values was approximately 1% as there were fewer data points in the bins of the relative DVH than absolute data points. Evaluating the minimum dose is not essential when creating a biological model as the dose tolerance of normal structures is based on the maximum and mean dose.

The results were randomly cross-checked with the existing ETPS and found to be in exact agreement with the proprietary Python software program [19] by checking the volume, mean, and maximum dose for the DVH structure.

Evaluation methods in plan comparison studies remain controversial as treatment plans do not always correlate with clinical outcomes such as side effects and toxicity. Accordingly, there is often disagreement between physicians and physicists regarding the impact of treatment plans on patient outcomes. Accordingly, the software developed in-house during the present study may allow clinical and radiobiological optimization of treatment plans, thereby providing new directions for radiobiological plan assessment studies.

The present study demonstrates the development of internally developed software that uses DVH in all perspective dimensions to calculate physical indices [20], such as conformity index and homogeneity index of target volumes, and biological indices, such as TCP and NTCP. We intend to conduct further studies to develop software for the calculation of uncomplicated TCP (UTCP) [21] for effective OAR volumes with homogeneous and inhomogeneous irradiation together with plan evaluations using the APM, QUANTEC, EMAMI (Dr. B. Emami), and RTOG protocols [22].

Dose statistics computed from Eclipse-generated DVH and Python-reconstructed DVH were compared, demonstrating a mean deviation in maximum dose values of less than 0.1% and a mean deviation in minimum dose values of approximately 1%. The mean deviation in mean dose values was less than 0.3%, possibly due to fewer data points in relative DVH bins compared to absolute data points. The results were compared with an existing TPS demonstrating the dose statistics calculated using our novel method were in exact agreement with the TPS. Based on the consistency and integrity of the program, treatment plans with indices of hotness and coldness were calculated as the speed of the program depends on the NumPy module implemented in the Python program. Our in-house software has been tested and found to have utility in creating DVH graphs from text files derived from IMRT plans and is able to reconstruct 16 DVH graphs in less than one second.

The research was supported by Thangam Cancer Hospital, Namakkal, Tamilnadu, India.

The authors have nothing to disclose.

The data that support the findings of this study are available on request from the corresponding author.

Conceptualization: Sougoumarane Dashnamoorthy, Karthick Rajamanickam, and Ebenezar Jeyasingh. Data curation: Sougoumarane Dashnamoorthy. Formal analysis: Sougoumarane Dashnamoorthy, Karthick Rajamanickam, and Imtiaz Ahmed. Funding acquisition: Karthick Rajamanickam. Investigation: Sougoumarane Dashnamoorthy, Ebenezar Jeyasingh, and Vindhyavasini Prasad Pandey. Methodology: Ebenezar Jeyasingh. Project administration: Karthick Rajamanickam. Resources: Karthick Rajamanickam and Kathiresan Nachimuthu. Software: Sougoumarane Dashnamoorthy. Supervision: Ebenezar Jeyasingh. Validation: Sougoumarane Dashnamoorthy and Ebenezar Jeyasingh. Visualization: Vindhyavasini Prasad Pandey and Pitchaikannu Venkatraman. Writing – original draft: Sougoumarane Dashnamoorthy. Writing – review & editing: Karthick Rajamanickam, Ebenezar Jeyasingh, and Vindhyavasini Prasad Pandey.

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Article

Original Article

Progress in Medical Physics 2022; 33(3): 25-35

Published online September 30, 2022 https://doi.org/10.14316/pmp.2022.33.3.25

Comparison of Dose Statistics of Intensity-Modulated Radiation Therapy Plan from Varian Eclipse Treatment Planning System with Novel Python-Based Indigenously Developed Software

Sougoumarane Dashnamoorthy1 , Karthick Rajamanickam1 , Ebenezar Jeyasingh2 , Vindhyavasini Prasad Pandey3 , Kathiresan Nachimuthu1 , Imtiaz Ahmed4 , Pitchaikannu Venkatraman5

1Department of Radiation Oncology, Thangam Cancer Hospital, Namakkal, Tamil Nadu, 2PG & Research, Department of Physics, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, Tamilnadu, 3Department of Medical Physics, Hind Institute of Medical Sciences and Hospital, Lucknow, Uttar Pradesh, 4Department of Radiation Oncology, KLES Belgaum Cancer Hospital, Belgaum, Karnataka, 5Department of Medical Physics, Bharathidasan University,Trichy, India

Correspondence to:Sougoumarane Dashnamoorthy
(dsougoumarane1969@gmail.com)
Tel: 91-8951768508

Received: May 6, 2022; Revised: August 8, 2022; Accepted: August 12, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose: Planning for radiotherapy relies on implicit estimation of the probability of tumor control and the probability of complications in adjacent normal tissues for a given dose distribution.
Methods: The aim of this pilot study was to reconstruct dose-volume histograms (DVHs) from text files generated by the Eclipse treatment planning system developed by Varian Medical Systems and to verify the integrity and accuracy of the dose statistics.
Results: We further compared dose statistics for intensity-modulated radiotherapy of the head and neck between the Eclipse software and software developed in-house. The dose statistics data obtained from the Python software were consistent, with deviations from the Eclipse treatment planning system found to be within acceptable limits.
Conclusions: The in-house software was able to provide indices of hotness and coldness for treatment planning and store statistical data generated by the software in Oracle databases. We believe the findings of this pilot study may lead to more accurate evaluations in planning for radiotherapy.

Keywords: Matrix laboratory, Python, Intensity-modulated radiation therapy, 3 dimensional conformal radiation therapy, Normal tissue complication probability, Tumor control probability

1. Ethics statement

This study was approved by the Institutional Review Board of Thangam Cancer Center (approval number: ECR/1069/Inst/TN/2018/RR-21). Informed consent was obtained from all patients.

2. Commercial language

MATLAB is a popular numerical computing environment and programming language. The major limitations of MATLAB are its commercial nature, expense, and the proprietary nature of algorithms. To overcome these limitations and develop a user-friendly alternative system, we evaluated the Python programming language as a tool for DVH analysis and biological planning.

The advantages of Python over MATLAB are lower licensing costs, extensive standard libraries, powerful data types, free availability, and cross-platform nature. Further, viewing and modifying source code, reading, programing, and turning ideas into code are technically less challenging in MATLAB. Finally, classes and functions can be defined anywhere within the program.

4. Graphical user interface

Matplotlib is a plotting library for the Python programming language and a numerical mathematics extension of NumPy. Matplotlib provides an object-oriented application programming interface (API) for embedding diagrams in applications using general-purpose graphical user interface toolkits such as Tkinter.

5. Oracle and data structures

Outputs from Python software were stored in an Oracle database. Oracle is free to download. Oracle Database 19c Standard Edition 2 Release 19.0.0.0.0 production version (Oracle USA Inc, CA, USA) was used in the present study.

6. Patient information

The present study recruited ten patients receiving RT in Thangam Cancer Hospital, Namakkal for head and neck cancer categorized under the 8th Edition of the American Joint Committee on Cancer Staging. IMRT treatment was planned for each patient.

7. DVH import

After IMRT optimization with commercial TPS such as Eclipse, the approved DVH can be imported into our software and used to compare dose statistics. The DVH text file from the graphical DVH is independent of the standard format version of the relative mode of the TPS used; version 11.0.31 (Eclipse 15.6; Varian Medical Systems, CA, USA) is currently used in this program. The size of exported DVH text files ranges from a few kilobytes to more than a few thousand lines, which is read as the input file for the Python program. Once the software is running, the application reads the text file of the patient’s DVH and stores the structures and target volumes of the DVH in special arrays called NumPy arrays. Stored arrays can be viewed in a histogram format. Many software programs have been designed and developed to biologically assess treatment plans since the 2000s [7]. Most software applications are developed in MATLAB, with few programs coded in C, Visual Basic, or Java programming languages. We believe this to be the first software program specifically designed in the high-end programming language, Python, using front-end Tkinter and back-end Oracle to store outputs generated by the program [8].

8. Dose-volume histogram analysis

We choose cumulative DVH as the software input as the Python program is designed to read cumulative relative DVH as an input. The software internally converts the accumulated DVH into the differential DVH according to the program requirement [8]. This basic Python code was developed to export Eclipse DVH text files and reconstruct graphical DVH for physical and biological assessment of treatment plans [9]. Python software calculates volume and minimum, maximum, and mean doses from special arrays, called NumPy arrays, stored in dose data bins. In addition to dose statistics, organ at risk (OAR) volumes can also be determined in Python to check the compatibility of the original DVH from the Eclipse TPS. A specific window of the software allows reading of the DVH file and the corresponding analysis of DVH to be performed. Volumes and dose statistics can be visualized for all OARs and planning target volumes (PTVs). Python software allows scaling, panning, enlargement, export, printing, and saving of the DVH to a desired destination.

Differential dose-volume histograms (dDVHs) obtained by Python software from DVH text files from a commercial TPS (Eclipse; Varian Medical Systems) were used to calculate IOH and IOC values for SeqB and SIB-IMRT using equations 1 and 2 shown below. In these equations, V is the total volume of the target, vi, Di denotes the ith bin of the differential, DVH is the volume of the ith voxel in the target volume, and DiRx and DiPlan are the prescribed and planned dose of the ith voxel, respectively.

IOH and IOC represent overdose or underdose at the target as evaluated by DVH. To assess the feasibility of the proposed index benchmark, calculations were performed using an example IMRT plan from a previous study [10].

$IOH=1+∑Di,Plan−Di,RxDiRx2XviV(Vi=0 if DiPlan≤DiRx)$
$IOC=1−∑Di,Plan−Di,RxDiRx2XviV(Vi=0 if DiPlan≥DiRx)$

The software includes Oracle database connectivity to allow storage of generated outputs as a backup.

1. Computed tomography

Immobilization was achieved with commercially-available rigid devices including headrests, leg abductors, and shoulder retractors. Computed tomography (CT) scans were performed in the supine position with a resolution between 0.93 to 0.98 mm in the axial plane and a slice thickness of 2.5 mm using a special GE Discovery IQ PET–CT 16 slice machine (GE Health care, Chicago, IL, USA). Data were exported to Eclipse version V15. PTV and OAR volumes were contoured by the attending radiation oncologist.

2. Treatment parameters

A seven-field IMRT plan was initially created using Eclipse TPS for standard fractionation treatment. The IMRT plan consisted of seven beam angles (gantry angles; 51°C, 102°C, 153°C, 204°C, 255°C, and 306°C) with the entire beam having a 6 MV beam quality. The dose calculation algorithm was performed by the Analytical Anisotropic Algorithm after use of the Leaf Motion Calculator [11].

3. Image verification

Cone beam computed tomography imaging guidance was applied to all patients using the Clinac iX integrated imaging system (Varian Medical Systems). A two-stage matching strategy was used. First, a fully automated registration based on bony anatomy (bone matching [BM]) was performed. After BM, matching was adjusted by a physician using direct visualization of the center of mass shift. After performing the fitting procedures, a final correction of translations and roll angle rotation (axis of rotation, head–toe) was automatically applied. Patients were subsequently treated with delivery of five fractions per week for precise dose delivery.

4. DVH dose statistic comparison

The Python program reads DVH text files in the relative dose mode (kilobyte size) rather than the absolute dose mode (megabyte size). The displayed DVH is then processed by the Python program in milliseconds (less than 1 second). The text file generated from the DVH by Eclipse TPS occurs at speeds in M/s. The Varian Medical System plan for IMRT is passed as an input to the developed program [12]. Dose statistics calculated using this software program is shown in Table 1. The maximum/mean dose values are within the acceptable deviation (0.3%), while minimum dose values had a deviation of 2%.

Dose statistics calculated in Eclipse or Python from DVH graphs in patients with IMRT plans for head and neck cancers.

PatientPrimary siteStagingOrgan at riskMinimum dose (cGy)Maximum dose (cGy)Mean dose (cGy)

Varian EclipsePython software% deviationVarian EclipsePython software% deviationVarian EclipsePython software% deviation
1Ca hypopharynx4ABrain_stem60.7584.442,0142,0090.24180.5182−0.83
Spinal cord12.71113.383,250.23,2410.281,651.11,652−0.05
Parotid_left230.62242.865,464.75,4600.082,332.12,338−0.25
Parotid_right266.52660.185,496.95,4670.542,581.32,583−0.06
2Ca tongue4ABrain_stem48.3480.627377320.67119.5120−0.41
Spinal cord625.36240.203,281.23,2760.152,272.32,274−0.07
Parotid_left172.41682.556,230.46,2280.032,468.22,472−0.15
Parotid_right80.2782.741,289.11,2840.39402.7408−1.31
3Ca tongue4ABrain_stem109.11062.842,5672,560.50.25323.2328.8−1.73
Spinal cord299.8293.82.003,566.33,560.90.152,591.52,595.5−0.15
Parotid_left2402371.255,923.65,918.60.082,628.62,630.5−0.07
Parotid_right270.2265.81.625,8765,876.6−0.012,5882,588.4−0.01
4Ca pyriform fossae4BBrain_stem112.6111.90.622,465.72,462.60.12294.4300.8−2.17
Spinal cord4241.90.232,977.42,973.30.131,934.71,937.9−0.16
Parotid_left387.7384.80.746,131.36,135.5−0.063,356.63,358.1−0.04
Parotid_right439.3433.71.276,194.36,191.50.043,277.23,281.1−0.11
5Ca cricopharynx4ABrain_stem135.5132.91.913,134.93,134.20.02829832.5−0.42
Spinal cord100.397.942.353,086.53,085.20.041,759.91,763−0.17
Parotid_left1,140.51,140.30.016,922.96,9190.053,418.13,421−0.08
Parotid_right638.8636.60.346,923.76,9190.063,448.73,456−0.211
6Ca supraglottis3Brain_stem49.147.04.2349.9348.10.5113.8112.21.4
Spinal cord128.91262.2498063,503.83,5000.108452,209.32,212−0.122211
Parotid_left1931892.0725396,063.46,0620.023091,280.71,288−0.570001
Parotid_right179.41752.452626,179.36,1740.085771,330.21,3300.0150353
7Ca pyriform fossae3Brain_stem110.81091.6245494,668.74,662.80.126371,034.21,036−0.174048
Spinal cord25.4239.4488194,592.54,5920.010892,467.12,471−0.15808
Parotid_left312.83081.5345275,7085,7050.052563,103.53,108−0.144998
Parotid_right283.12801.0950197,046.17,0420.058193,3713,374−0.088994
8Ca pyriform fossae3Brain_stem90.4847.0796463,413.83,4090.140612,134.82,142−0.337268
Spinal cord2672660.3745326,555.56,5520.053392,419.82,422−0.090917
Parotid_left271.62662.0618566,401.46,3980.053112,4412,450−0.368701
Parotid_right1,3161,3210.3799396,9096,913.1−0.05933,6053,604.50.0138696
9Ca supraglottis3Brain_stem53.4522.621723556.1549.31.2228118.7122.1−2.864364
Spinal cord105.3101.73.4188033,372.13,371.70.011861,727.91,729.9−0.115747
Parotid_left201196.72.1393035,867.95,861.40.110771,827.41,831.7−0.235307
Parotid_right201196.72.1393035,919.35,915.60.062512,069.72,075.9−0.29956

DVH, dose-volume histogram; IMRT, intensity-modulated radiotherapy..

When optimizing with a Millennium MLC-120 multi-leaf collimator, the minimum dose criterion of 95% of the prescribed dose of the PTV was given top priority [13]. The American Association of Physicists in Medicine, Radiation Therapy Oncology Group (RTOG), and Quantitative Analyzes of Normal Tissue Effects in the Clinic dose limit targets were used for the OAR. For all patients, both dose distributions were calculated based on planning CT scans with a grid size of 2.5×2.5 mm2. The text file of the DVH after the energy fluence calculation was exported in text format from the Eclipse system. The energy fluence of the treatment fields after treatment plan optimization is shown in Fig. 1. The DVH with target volumes of several organs, including the brainstem, spinal cord, and parallel similar parotid glands, are shown in Fig. 2.

Figure 1. Energy fluence of treatment fields after treatment plan optimization.

Figure 2. Dose-volume histograms with target volumes for organs at risk in Eclipse treatment planning system.

5. Dose statistics

Dose statistics including the minimum, maximum, and mean dose to the OAR were plotted in Microsoft Excel to compare the values calculated by Eclipse and Python (Fig. 35).

Figure 3. Minimum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Minimum dose statistics of brain stem. (b) Minimum dose statistics of spinal cord. (c) Minimum dose statistics of parotid right. (d) Minimum dose statistics of parotid left.

Figure 4. Maximum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Maximum dose statistics of brain stem. (b) Maximum dose statistics of spinal cord. (c) Maximum dose statistics of parotid right. (d) Maximum dose statistics of parotid left.

Figure 5. Mean dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Mean dose statistics of brain stem. (b) Mean dose statistics of spinal cord. (c) Mean dose statistics of parotid right. (d) Mean dose statistics of parotid left.

The results generated by the software were recorded. An application screen was used to show dose statistics with hot and cold spots for head and neck cases as shown in Fig. 6. A window on the left side of the screen allows the desired patient details to be selected. The lower part of the screen shows the total volume and minimum, maximum, and average doses for each plan and structure. A corresponding histogram is displayed in the middle of the screen in the middle window. The toolbar in the graphical window can be used to resize, pan, and enlarge the DVH and export DVH image files for printing [14].

Figure 6. Screenshot of the application program with dose statistics and hot and cold indices.

6. Analyses of hotness and coldness indices

Based on the consistency and reproducibility of the DVH plot, the IOH and IOC for cervical SIB-IMRT were calculated and tabulated in Table 2. The results are displayed on the same application screen at the bottom of the window. For all indices, 1 indicates a perfect match between the prescribed and planned dose, while values further from 1 indicate greater dissimilarity. It should be noted that the IOC has values equal to or less than 1, representing under dosing of the target volume, while the IOH has values equal to or greater than 1.

Hot and cold indices calculated in the Python software program from SIB-IMRT treatment plans for patients with head and neck cancers.

PatientPrimary siteStageTarget volume 70Target volume 63Target volume 60Target volume 59.4Target volume 56Target volume 54Target volume 50

HotColdHotColdHotColdHotColdHotColdHotColdHotCold
1Ca hypopharynx4A1.0540.9461.0950.905
2Ca tongue4A1.0050.9951.0390.961
3Ca cricopharynx4A1.0080.9921.0580.9421.090.91
4Ca tongue4A1.0060.9941.0780.9221.110.89
5Ca pyriform fossae31.0040.9961.040.961.0780.922
6Ca pyriform fossae31.0070.9931.0410.9591.080.92
7Ca pyriform fossae4B1.0080.9921.0570.9431.0920.908
8Ca supraglottis31.0040.9961.0580.9421.090.91
9Ca supraglottis31.0080.9921.0690.941.0920.908

Deviations from the hotness and coldness indices were analyzed and presented graphically in Fig. 7. The resultant graph shows that higher target volumes reduce deviation when the dose-volume constraint is prioritized in the optimization window of the Eclipse TPS.

Figure 7. Indices of hotness and coldness from simultaneously integrated boost-intensity-modulated radiotherapy (SIB-IMRT) treatment plans for the entire target volume. PTV, planning target volume.

Discussion

We believe this to be the first biology-based treatment plan evaluation software written in the high-end Python programming language with DVH text files generated from the Varian Eclipse commercial planning system. Generated outputs can be displayed in Tkinter frames and simultaneously stored in the Oracle database for further use [15].

We termed this software program the Relative Dose System (RDS) as relative DVH in text files are imported as inputs by the software. The software extracts data points from the text file and stores them in NumPy arrays allowing reconstruction of DVH and analysis of biological and physical parameters. We demonstrate the data points of the generated DVH chart were in excellent agreement with the Eclipse TPS. Clinically, the RDS software has greater utility in examining DVH statistics from SIB-IMRT plans, a newly developed approach for the application of different radiation doses to different areas in a single session [16].

This software was developed using the high-end Python programming language, which compares Eclipse TPS dose statistics with reconstructed DVH in the Python program [17]. This preliminary and pilot study demonstrates dose statistics including mean, minimum, and maximum doses for tumors or targets and OAR can be obtained from graphical DVH reconstructed from text files generated from planning DVH [18].

We compared dose statistics calculated from DVH generated by Eclipse and DVH reconstructed in Python and found that the mean deviation in maximum dose values was less than 0.1% and the mean deviation in mean dose values was less than 0.3%. The mean deviation in minimum doses values was approximately 1% as there were fewer data points in the bins of the relative DVH than absolute data points. Evaluating the minimum dose is not essential when creating a biological model as the dose tolerance of normal structures is based on the maximum and mean dose.

The results were randomly cross-checked with the existing ETPS and found to be in exact agreement with the proprietary Python software program [19] by checking the volume, mean, and maximum dose for the DVH structure.

Evaluation methods in plan comparison studies remain controversial as treatment plans do not always correlate with clinical outcomes such as side effects and toxicity. Accordingly, there is often disagreement between physicians and physicists regarding the impact of treatment plans on patient outcomes. Accordingly, the software developed in-house during the present study may allow clinical and radiobiological optimization of treatment plans, thereby providing new directions for radiobiological plan assessment studies.

The present study demonstrates the development of internally developed software that uses DVH in all perspective dimensions to calculate physical indices [20], such as conformity index and homogeneity index of target volumes, and biological indices, such as TCP and NTCP. We intend to conduct further studies to develop software for the calculation of uncomplicated TCP (UTCP) [21] for effective OAR volumes with homogeneous and inhomogeneous irradiation together with plan evaluations using the APM, QUANTEC, EMAMI (Dr. B. Emami), and RTOG protocols [22].

Conclusions

Dose statistics computed from Eclipse-generated DVH and Python-reconstructed DVH were compared, demonstrating a mean deviation in maximum dose values of less than 0.1% and a mean deviation in minimum dose values of approximately 1%. The mean deviation in mean dose values was less than 0.3%, possibly due to fewer data points in relative DVH bins compared to absolute data points. The results were compared with an existing TPS demonstrating the dose statistics calculated using our novel method were in exact agreement with the TPS. Based on the consistency and integrity of the program, treatment plans with indices of hotness and coldness were calculated as the speed of the program depends on the NumPy module implemented in the Python program. Our in-house software has been tested and found to have utility in creating DVH graphs from text files derived from IMRT plans and is able to reconstruct 16 DVH graphs in less than one second.

Acknowledgments

The research was supported by Thangam Cancer Hospital, Namakkal, Tamilnadu, India.

Conflicts of Interest

The authors have nothing to disclose.

Availability of Data and Materials

The data that support the findings of this study are available on request from the corresponding author.

Author Contributions

Conceptualization: Sougoumarane Dashnamoorthy, Karthick Rajamanickam, and Ebenezar Jeyasingh. Data curation: Sougoumarane Dashnamoorthy. Formal analysis: Sougoumarane Dashnamoorthy, Karthick Rajamanickam, and Imtiaz Ahmed. Funding acquisition: Karthick Rajamanickam. Investigation: Sougoumarane Dashnamoorthy, Ebenezar Jeyasingh, and Vindhyavasini Prasad Pandey. Methodology: Ebenezar Jeyasingh. Project administration: Karthick Rajamanickam. Resources: Karthick Rajamanickam and Kathiresan Nachimuthu. Software: Sougoumarane Dashnamoorthy. Supervision: Ebenezar Jeyasingh. Validation: Sougoumarane Dashnamoorthy and Ebenezar Jeyasingh. Visualization: Vindhyavasini Prasad Pandey and Pitchaikannu Venkatraman. Writing – original draft: Sougoumarane Dashnamoorthy. Writing – review & editing: Karthick Rajamanickam, Ebenezar Jeyasingh, and Vindhyavasini Prasad Pandey.

Fig 1.

Figure 1.Energy fluence of treatment fields after treatment plan optimization.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 2.

Figure 2.Dose-volume histograms with target volumes for organs at risk in Eclipse treatment planning system.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 3.

Figure 3.Minimum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Minimum dose statistics of brain stem. (b) Minimum dose statistics of spinal cord. (c) Minimum dose statistics of parotid right. (d) Minimum dose statistics of parotid left.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 4.

Figure 4.Maximum dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Maximum dose statistics of brain stem. (b) Maximum dose statistics of spinal cord. (c) Maximum dose statistics of parotid right. (d) Maximum dose statistics of parotid left.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 5.

Figure 5.Mean dose statistics of the organ at risk calculated with Eclipse and Python software. (a) Mean dose statistics of brain stem. (b) Mean dose statistics of spinal cord. (c) Mean dose statistics of parotid right. (d) Mean dose statistics of parotid left.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 6.

Figure 6.Screenshot of the application program with dose statistics and hot and cold indices.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Fig 7.

Figure 7.Indices of hotness and coldness from simultaneously integrated boost-intensity-modulated radiotherapy (SIB-IMRT) treatment plans for the entire target volume. PTV, planning target volume.
Progress in Medical Physics 2022; 33: 25-35https://doi.org/10.14316/pmp.2022.33.3.25

Table 1 Dose statistics calculated in Eclipse or Python from DVH graphs in patients with IMRT plans for head and neck cancers

PatientPrimary siteStagingOrgan at riskMinimum dose (cGy)Maximum dose (cGy)Mean dose (cGy)

Varian EclipsePython software% deviationVarian EclipsePython software% deviationVarian EclipsePython software% deviation
1Ca hypopharynx4ABrain_stem60.7584.442,0142,0090.24180.5182−0.83
Spinal cord12.71113.383,250.23,2410.281,651.11,652−0.05
Parotid_left230.62242.865,464.75,4600.082,332.12,338−0.25
Parotid_right266.52660.185,496.95,4670.542,581.32,583−0.06
2Ca tongue4ABrain_stem48.3480.627377320.67119.5120−0.41
Spinal cord625.36240.203,281.23,2760.152,272.32,274−0.07
Parotid_left172.41682.556,230.46,2280.032,468.22,472−0.15
Parotid_right80.2782.741,289.11,2840.39402.7408−1.31
3Ca tongue4ABrain_stem109.11062.842,5672,560.50.25323.2328.8−1.73
Spinal cord299.8293.82.003,566.33,560.90.152,591.52,595.5−0.15
Parotid_left2402371.255,923.65,918.60.082,628.62,630.5−0.07
Parotid_right270.2265.81.625,8765,876.6−0.012,5882,588.4−0.01
4Ca pyriform fossae4BBrain_stem112.6111.90.622,465.72,462.60.12294.4300.8−2.17
Spinal cord4241.90.232,977.42,973.30.131,934.71,937.9−0.16
Parotid_left387.7384.80.746,131.36,135.5−0.063,356.63,358.1−0.04
Parotid_right439.3433.71.276,194.36,191.50.043,277.23,281.1−0.11
5Ca cricopharynx4ABrain_stem135.5132.91.913,134.93,134.20.02829832.5−0.42
Spinal cord100.397.942.353,086.53,085.20.041,759.91,763−0.17
Parotid_left1,140.51,140.30.016,922.96,9190.053,418.13,421−0.08
Parotid_right638.8636.60.346,923.76,9190.063,448.73,456−0.211
6Ca supraglottis3Brain_stem49.147.04.2349.9348.10.5113.8112.21.4
Spinal cord128.91262.2498063,503.83,5000.108452,209.32,212−0.122211
Parotid_left1931892.0725396,063.46,0620.023091,280.71,288−0.570001
Parotid_right179.41752.452626,179.36,1740.085771,330.21,3300.0150353
7Ca pyriform fossae3Brain_stem110.81091.6245494,668.74,662.80.126371,034.21,036−0.174048
Spinal cord25.4239.4488194,592.54,5920.010892,467.12,471−0.15808
Parotid_left312.83081.5345275,7085,7050.052563,103.53,108−0.144998
Parotid_right283.12801.0950197,046.17,0420.058193,3713,374−0.088994
8Ca pyriform fossae3Brain_stem90.4847.0796463,413.83,4090.140612,134.82,142−0.337268
Spinal cord2672660.3745326,555.56,5520.053392,419.82,422−0.090917
Parotid_left271.62662.0618566,401.46,3980.053112,4412,450−0.368701
Parotid_right1,3161,3210.3799396,9096,913.1−0.05933,6053,604.50.0138696
9Ca supraglottis3Brain_stem53.4522.621723556.1549.31.2228118.7122.1−2.864364
Spinal cord105.3101.73.4188033,372.13,371.70.011861,727.91,729.9−0.115747
Parotid_left201196.72.1393035,867.95,861.40.110771,827.41,831.7−0.235307
Parotid_right201196.72.1393035,919.35,915.60.062512,069.72,075.9−0.29956

DVH, dose-volume histogram; IMRT, intensity-modulated radiotherapy.

Table 2 Hot and cold indices calculated in the Python software program from SIB-IMRT treatment plans for patients with head and neck cancers

PatientPrimary siteStageTarget volume 70Target volume 63Target volume 60Target volume 59.4Target volume 56Target volume 54Target volume 50

HotColdHotColdHotColdHotColdHotColdHotColdHotCold
1Ca hypopharynx4A1.0540.9461.0950.905
2Ca tongue4A1.0050.9951.0390.961
3Ca cricopharynx4A1.0080.9921.0580.9421.090.91
4Ca tongue4A1.0060.9941.0780.9221.110.89
5Ca pyriform fossae31.0040.9961.040.961.0780.922
6Ca pyriform fossae31.0070.9931.0410.9591.080.92
7Ca pyriform fossae4B1.0080.9921.0570.9431.0920.908
8Ca supraglottis31.0040.9961.0580.9421.090.91
9Ca supraglottis31.0080.9921.0690.941.0920.908

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Vol.33 No.4 December, 2022

pISSN 2508-4445
eISSN 2508-4453
Formerly ISSN 1226-5829

Frequency: Quarterly