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Comparison of Dose Statistics of Intensity-Modulated Radiation Therapy Plan from Varian Eclipse Treatment Planning System with Novel Python-Based Indigenously Developed Software
Progress in Medical Physics 2022;33(3):25-35
Published online September 30, 2022
© 2022 Korean Society of Medical Physics.

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
Introduction

There is a clinical need for studies of radiobiological effects due to the risks of side effects and delayed reactions after irradiation with 3-dimensional radiotherapy (3D-RT), intensity-modulated radiotherapy (IMRT), and RapidArc radiotherapy. Current linear accelerator suppliers, including Varian (USA), Elekta (Sweden), ACCURAY (USA), Philips (Netherlands), GE Healthcare (UK), Toshiba (Japan), Mitsubishi Heavy Industries (Japan), and Shinva (China), have integrated biological modeling into treatment planning systems (TPSs) to evaluate tumor control probability (TCP) and normal tissue complication probability (NTCP). The basic requirement for various radiobiological models is the creation of a dose-volume histogram (DVH) for treatment planning. However, the use of TPS in studies evaluating radiobiological plans is currently limited due to unaffordability related to the high licensing costs. Accordingly, the generation of DVH during TPS is only possible within institutions due to the need for institutional approval. A number of biological assessment software programs, such as SABER (Spatial and Biological Assessment of Radiation Therapy) [1], CERR (Computer Environment for Radiation Therapy Research) [2], HART (Histogram Analysis in Radiation Therapy) [3], and DRESS (dose-response Explorer system) [4], have been developed in MATLAB. Further software programs, such as Bioplan (biological plan evaluation software) [5] and IsoBED [6], have been developed in Microsoft Visual Basic. However, all of these are commercial software programs. The use of MATLAB software is limited by high purchasing costs due to the proprietary nature of the algorithms, difficulties with code visibility, and relatively low portability. The assessment software programs mentioned above do not provide indices of hotness and coldness for treatment planning. However, use of the freely available high-end software program, Python, can overcome the limitations of the software programs mentioned above. We therefore internally developed software in the high-end programming language, Python, to calculate index of hotness (IOH) and index of coldness (IOC) values for sequential boost (SeqB) treatment plans. Simultaneously integrated boost (SIB) allows the simultaneous administration of different dose levels to different target volumes within a single treatment. The software can store data in Oracle databases for future reports.

Materials and Methods

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.

3. Advantages of Python

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.

Results

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

Patient Primary site Staging Organ at risk Minimum dose (cGy) Maximum dose (cGy) Mean dose (cGy)



Varian Eclipse Python software % deviation Varian Eclipse Python software % deviation Varian Eclipse Python software % deviation
1 Ca hypopharynx 4A Brain_stem 60.7 58 4.44 2,014 2,009 0.24 180.5 182 −0.83
Spinal cord 12.7 11 13.38 3,250.2 3,241 0.28 1,651.1 1,652 −0.05
Parotid_left 230.6 224 2.86 5,464.7 5,460 0.08 2,332.1 2,338 −0.25
Parotid_right 266.5 266 0.18 5,496.9 5,467 0.54 2,581.3 2,583 −0.06
2 Ca tongue 4A Brain_stem 48.3 48 0.62 737 732 0.67 119.5 120 −0.41
Spinal cord 625.3 624 0.20 3,281.2 3,276 0.15 2,272.3 2,274 −0.07
Parotid_left 172.4 168 2.55 6,230.4 6,228 0.03 2,468.2 2,472 −0.15
Parotid_right 80.2 78 2.74 1,289.1 1,284 0.39 402.7 408 −1.31
3 Ca tongue 4A Brain_stem 109.1 106 2.84 2,567 2,560.5 0.25 323.2 328.8 −1.73
Spinal cord 299.8 293.8 2.00 3,566.3 3,560.9 0.15 2,591.5 2,595.5 −0.15
Parotid_left 240 237 1.25 5,923.6 5,918.6 0.08 2,628.6 2,630.5 −0.07
Parotid_right 270.2 265.8 1.62 5,876 5,876.6 −0.01 2,588 2,588.4 −0.01
4 Ca pyriform fossae 4B Brain_stem 112.6 111.9 0.62 2,465.7 2,462.6 0.12 294.4 300.8 −2.17
Spinal cord 42 41.9 0.23 2,977.4 2,973.3 0.13 1,934.7 1,937.9 −0.16
Parotid_left 387.7 384.8 0.74 6,131.3 6,135.5 −0.06 3,356.6 3,358.1 −0.04
Parotid_right 439.3 433.7 1.27 6,194.3 6,191.5 0.04 3,277.2 3,281.1 −0.11
5 Ca cricopharynx 4A Brain_stem 135.5 132.9 1.91 3,134.9 3,134.2 0.02 829 832.5 −0.42
Spinal cord 100.3 97.94 2.35 3,086.5 3,085.2 0.04 1,759.9 1,763 −0.17
Parotid_left 1,140.5 1,140.3 0.01 6,922.9 6,919 0.05 3,418.1 3,421 −0.08
Parotid_right 638.8 636.6 0.34 6,923.7 6,919 0.06 3,448.7 3,456 −0.211
6 Ca supraglottis 3 Brain_stem 49.1 47.0 4.2 349.9 348.1 0.5 113.8 112.2 1.4
Spinal cord 128.9 126 2.249806 3,503.8 3,500 0.10845 2,209.3 2,212 −0.122211
Parotid_left 193 189 2.072539 6,063.4 6,062 0.02309 1,280.7 1,288 −0.570001
Parotid_right 179.4 175 2.45262 6,179.3 6,174 0.08577 1,330.2 1,330 0.0150353
7 Ca pyriform fossae 3 Brain_stem 110.8 109 1.624549 4,668.7 4,662.8 0.12637 1,034.2 1,036 −0.174048
Spinal cord 25.4 23 9.448819 4,592.5 4,592 0.01089 2,467.1 2,471 −0.15808
Parotid_left 312.8 308 1.534527 5,708 5,705 0.05256 3,103.5 3,108 −0.144998
Parotid_right 283.1 280 1.095019 7,046.1 7,042 0.05819 3,371 3,374 −0.088994
8 Ca pyriform fossae 3 Brain_stem 90.4 84 7.079646 3,413.8 3,409 0.14061 2,134.8 2,142 −0.337268
Spinal cord 267 266 0.374532 6,555.5 6,552 0.05339 2,419.8 2,422 −0.090917
Parotid_left 271.6 266 2.061856 6,401.4 6,398 0.05311 2,441 2,450 −0.368701
Parotid_right 1,316 1,321 0.379939 6,909 6,913.1 −0.0593 3,605 3,604.5 0.0138696
9 Ca supraglottis 3 Brain_stem 53.4 52 2.621723 556.1 549.3 1.2228 118.7 122.1 −2.864364
Spinal cord 105.3 101.7 3.418803 3,372.1 3,371.7 0.01186 1,727.9 1,729.9 −0.115747
Parotid_left 201 196.7 2.139303 5,867.9 5,861.4 0.11077 1,827.4 1,831.7 −0.235307
Parotid_right 201 196.7 2.139303 5,919.3 5,915.6 0.06251 2,069.7 2,075.9 −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.

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

Fig. 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).

Fig. 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.

Fig. 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.

Fig. 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].

Fig. 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

Patient Primary site Stage Target volume 70 Target volume 63 Target volume 60 Target volume 59.4 Target volume 56 Target volume 54 Target volume 50







Hot Cold Hot Cold Hot Cold Hot Cold Hot Cold Hot Cold Hot Cold
1 Ca hypopharynx 4A 1.054 0.946 1.095 0.905
2 Ca tongue 4A 1.005 0.995 1.039 0.961
3 Ca cricopharynx 4A 1.008 0.992 1.058 0.942 1.09 0.91
4 Ca tongue 4A 1.006 0.994 1.078 0.922 1.11 0.89
5 Ca pyriform fossae 3 1.004 0.996 1.04 0.96 1.078 0.922
6 Ca pyriform fossae 3 1.007 0.993 1.041 0.959 1.08 0.92
7 Ca pyriform fossae 4B 1.008 0.992 1.057 0.943 1.092 0.908
8 Ca supraglottis 3 1.004 0.996 1.058 0.942 1.09 0.91
9 Ca supraglottis 3 1.008 0.992 1.069 0.94 1.092 0.908

SIB-IMRT, simultaneously integrated boost-intensity-modulated radiotherapy.



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.

Fig. 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.

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