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Radiotherapy plan evaluation indices: A dosimetrical suitability check


1 Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
2 Department of Radiotherapy, Balco Medical Center, Naya Raipur, Chhattisgarh, India
3 Department of Radiation Oncology, RMLIMS, Lucknow, Uttar Pradesh, India

Date of Submission15-Jun-2019
Date of Decision02-Sep-2019
Date of Acceptance01-Dec-2019
Date of Web Publication13-Oct-2020

Correspondence Address:
Abhijit Mandal,
Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_417_19

 > Abstract 


Purpose: To classify the available plan evaluation indices and compare the dosimetric suitability of these indices.
Materials and Methods: Available published plan evaluation indices were categorized. Conformity index (CI) into two groups, one group contains those CI formulas which do not consider critical structure and other group contains those CI formulas which consider planning target volume (PTV) coverage, normal tissue and critical structure sparing simultaneously. Various homogeneity index (HI) formulas extracted from literature. Structure data sets of 25 patients were taken under consideration comprising of various sites. For each patient, two plans were created using Volumetric Arc Therapy technique. First type of plan (Plan-A) were generated considering all tissue objectives for targets and Organ at Risks (OARs) whereas second type of plan (Plan-B) were generated considering only targets tissue objectives and excluding OARs tissue objectives during plan optimization and dose calculation. Planning evaluation parameters were compared between Plan-A and Plan-B.
Results: CI calculated by various formulas in two different scenarios presented <2% variation. Any commonly used CI formula failed to differentiate the two different planning situations. On comparison between HI of two different scenario, it is observed that there are four formulas of HI which showed negligible variation but two formulae: S-index and HI (D) showed marginal variation. It is also observed that when OARs are removed from optimization dose homogeneity improved which is specifically pointed by sigma index formula.
Conclusion: CI, which has assimilated the presence of OAR in their formulation, shows more reliability in plan evaluation. Sigma index was found to be more efficient formula while evaluating homogeneity of a treatment plan.

Keywords: Conformity index, homogeneity index, planning target volume



How to cite this URL:
Patel G, Mandal A, Shende R, Bharati A. Radiotherapy plan evaluation indices: A dosimetrical suitability check. J Can Res Ther [Epub ahead of print] [cited 2020 Oct 28]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=298075




 > Introduction Top


Plan evaluation is a key component in the planning and radiation treatment process. Most of the time, we relies on the conventional method of plan evaluation such as slice by slice visual verification of prescription isodose line conforming to planning target volume (PTV) and dose volume histogram (DVH). Routinely, for each patient a number of treatment plans can be generated which differs from each other in terms of dose distribution. A best plan of the lot is selected and approved for treatment on the basis of merits of the plan. In earlier days this plan selection process was on the basis of subjective evaluation, which was purely dependent on evaluator skill and knowledge. However, with the introduction of newer sophisticated treatment techniques (such as intensity-modulated radiotherapy, image-guided radiotherapy, and stereotactic surgery/radiotherapy), the plan evaluation process becomes more complex and needs special care to get better clinical treatment outcome. To resolve the evaluator variability and increase the objectivity of plan evaluation process, radiotherapy oncology group (RTOG) in 1993 introduce conformity index (CI) and homogeneity index (HI) to analyze DVH.[1] Since the inception of CI and HI improvisation is in progress.

Number of authors [2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12],[13],[14],[15],[16],[17] defined CI with new ideas but most of them could not identifies many issues such as role of cold/hot spot in PTV, role of spatial dose information and different targets with different dose prescription etc., Some new indices were also defined to address the flaws of earlier indices but mostly were personalized and created using special software such as MATLAB, C-language, and Visual basic. Hence, their application is limited and cannot be generalized.

Second important parameter in plan evaluation is a HI, which accounts for nonuniform dose distribution inside the PTV. HI is influenced by many factors such as target volume, location of target, and prescribed dose, and this is validated by various authors, still there are some factors need to be unveil.[18],[19],[20],[21]

Hence, there is a need to categorize the available published conformity indices and homogeneity indices and find the suitability of these indices in various clinical situations. In this present study an attempt has been made to classify the published plan evaluation indices and a dosimetrical suitability test between various indices was conducted.


 > Materials and Methods Top


Multiple indices proposed in literature were categorized into two groups: Group-A and Group-B. Group-A contains those CI formulas which does not consider critical structure sparing while using them for evaluation but includes normal tissue and PTV coverage [Table 1]. Group-B contains those CI formulas which consider PTV coverage, normal tissue and critical structure sparing simultaneously while using them for plan evaluation [Table 2]. The intention behind forming two groups is to enhance clear understanding to reader regarding various CI definitions published in literature. Various HI formulas extracted from literature are presented in [Table 3].
Table 1: Group-A containing definitions of conformity index which do not take into account organ at risk sparing

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Table 2: Group-B containing definitions of conformity index which take into account organ at risk sparing

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Table 3: Various formulas of homogeneity indices available in literature

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Structure data set of twenty five patients were taken under consideration comprising five glioblastoma multiforme cases of brain site, five carcinoma of larynx of head and neck site, five carcinoma of esophagus of thorax site, five carcinoma of lung of thorax, and five carcinoma of cervix of pelvis site. For each patients, two plans were created using Volumetric Arc Therapy technique in Eclipse treatment planning system version 11.3, Varian Medical system Inc., Palo Alto USA. Dose calculation grid size was set 3 mm for all planned cases. Dose volume optimizer algorithm had been employed for optimization and Anisotropic Analytical Algorithm for dose calculation. First type of plan (Plan-A) were generated considering all tissue objectives for targets and Organ at Risks (OARs) whereas second type of plan (Plan-B) were generated considering only targets tissue objectives and excluding OARs tissue objectives during plan optimization. In Plan-B, we have removed all organs at risk from optimization process in order to search effect on various parameters of plan evaluation indices like CI and HI.

Plan comparison criteria

Plan comparison is performed between Plan-A and Plan-B in view of various formulas of CI. For target coverage, 95% of prescription dose must cover 100% of PTV. Different organ at risk receiving dose in Plan-A and Plan-B were also recorded in this study for comparison. Results obtained in this study cannot consider absolute because new plan created in this study can affected by various factors, e.g., optimization algorithm, normal tissue objective setting and planner's way of planning.


 > Results Top


It is observed that CI calculated by various formulas in two different scenario presented [Table 4] <3% variation (Range: 1.07%–2.3%). The percentage variation OAR doses in two different plan were recorded in [Table 6], [Table 7], [Table 8], [Table 9], [Table 10]. It is observed that, when the OAR are situated in close proximity to the target, such as esophagus [Table 7] and cervix [Table 9], there is a marginal increase of OAR doses in Plan-B than Plan-A. Whereas, a significant decrease of OAR doses in Plan-B than Plan-A was observed when the OARs situated at sufficiently farther from target, such as head and neck [Table 6], brain [Table 8] and lung [Table 10]. In esophagus cases, the variation was least for heart (5.1%) and highest for right lung (8.9%) whereas in cervix cases, it was least for right femur (2.0%) and highest for left femur (4.5%). In head and neck cases the variation was least for right parotid (17.0%) and highest for brainstem (51.2%). Similarly in brain cases the variation was least in left optic nerve (9.4%) and was highest for right optic nerve (23%), whereas in lung cases it was least for heart (28%) and highest for contralateral lung (31.6%).
Table 4: Conformity index belong to Group-A and target coverage evaluation in two different plans Plan-A and Plan-B

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Table 6: Organ at risk mean and maximum doses for Head and Neck treatment site

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Table 7: Organ at risk mean and maximum doses for Esophagus treatment site

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Table 8: Organ at risk mean and maximum doses for Brain treatment site

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Table 9: Organ at risk mean and maximum doses for Cervix treatment site

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Table 10: Organ at risk mean and maximum doses for Lung treatment site

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Target coverage is slightly improved in Plan-B (1.17%). This showed that by relaxing OAR won't help to improve conformity. CI evaluated in our study which belongs to group-A do not present true picture of real situation. This may also depends on the optimization algorithm employed in commercial treatment planning system.

By following the same methodology of comparison, HI of two different scenarios was evaluated using seven formulas mentioned in [Table 3]. Out of seven formulas four formulas with serial number 3, 4, 6 and 8 of [Table 3], showed marginal percentage variation − 24.04%, −24.84%, −26.1% and − 27.94% respectively [Table 5]. It is analyzed that when OAR are removed from optimization, dose homogeneity improved which is specifically pointed by these four formulas. Sigma index was found to be more efficient formula while evaluating HI of a treatment plan.
Table 5: Homogeneity index evaluation in two different plans Plan-A and Plan-B

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 > Discussion Top


Dose spillage both low and high outside PTV is a major concern during plan evaluation; surprisingly, neither definitions of CI available in literature addressed this issue. Existing formulas of CI unable to distinguish between two competitive plans having dose spillage outside the planning target volume. Therefore clinicians forced to adopt slice by slice visual verification of dose distribution obtained in treatment plans.

There is always a probability of hot spot and cold spots within target; they are unavoidable but where they created inside PTV is objectionable. Hot spot inside gross tumor volume (GTV) increases tumor control probability (TCP) and cold spot inside PTV decreases TCP.[22],[23],[24] Hot spot at the border of PTV margin but close to serial organ cannot be accepted where as cold spot at the border of PTV margin and adjacent to serial organ is acceptable.

Different targets with different dose prescriptions known as simultaneously integrated boost plans remained a major concern for almost all definitions of CI available in literature. Most of indices definitions provide satisfactory CI value for higher dose target but fail to satisfy other targets in simultaneously integrated boost (SIB) treatment plans. Only planning quality index developed by Leung et al.[11] addressed this issue satisfactorily. As we know that clinicians prefer to go for SIB plans over sequential plans because of its distinct clinical advantages and SIB plans are becoming routine practice for clinicians.[25],[26],[27]

It has been observed that proximity of OAR to target perturbs plan outcome. When OAR has strict constrained and there is marginal dose variation between OAR and target then it is a possibility that either target coverage compromise or OAR sparing. It is a planner who has to set balance between them, it points out that proximity of OAR affects target coverage, conformity and dose distribution inside target. Therefore, a definition of CI which does not take into account the presence of OAR merely provides incomplete information of dose conformity to target.

As we know that different parts of the body possess varying degree of heterogeneity. Brain possesses least heterogeneity in terms of density difference as compare to head and neck, thorax, abdomen and pelvis. Head and neck carry highest degree of density difference because of structures like oral cavity, nasal cavity, high density bone, high density teeth, tongue and sometimes dental implants which affects dose distribution significantly inside the target volume. It has been observed that treatment plans of brain cases presents more homogeneous dose distribution inside PTV except stereotactic radiosurgery/stereotactic radiation therapy (SRS/SRT) treatment plans where dose heterogeneity is desirable as compare to other treatment site plans. Head and neck treatment plans, especially SIB plans are found to have highest degree of heterogeneity or say poor value of HI if calculated individually for differential target volumes. One more useful finding is that HI index also get affected by proximity of OAR, extent of their overlapping with PTV and their respective tolerance doses. To identify the presence of hot spots and cold spots which is a measure of underdose and overdose in PTV is a crucial step in plan evaluation. Ideally, HI should take care of this, but existing formulas of HI cannot satisfactorily express it and therefore slice by slice verification of dose distribution is always a primary choice of clinicians. Because many times presence of hot spot in GTV or clinical target volume and cold spot adjacent to OAR but within PTV is acceptable while plan evaluation. It has been clinically accepted that presence of hot spot in GTV provides radiobiological advantage in terms of TCP.[28] Existing formulas of HI cannot reveal location of multiple hot spot and cold spot within PTV and merely provides degree of heterogeneity.

In the beginning gradient index (GI) was introduced for SRS/SRT treatment techniques only because, brain is such a sensitive area where sparing tiny volume of it make a marginal difference in treatment outcome.[29] For small volume targets high dose gradient can be easily achievable which results in improved CI and GI. In case of larger volume targets, GI shows poor value; still, it a good choice to consider while plan evaluation. It is well understood that SRS/SRT treatment plans produces significant non-uniform dose distribution, hence HI may not be considered as important plan evaluation parameter in addition with CI and GI. Molecular imaging confirmed that all targets do not have homogeneous cell density; hence, concept of homogeneous dose distribution inside PTV is dissolving. New theory of biological target-based planning is evolving, and with advancement in the field of molecular imaging, biological target-based planning will be the right choice. Hence, HI may be discontinuing using an effective or objective tool in plan evaluation instead GI is a good choice in addition with CI.


 > Conclusion Top


Conformity indices, which have assimilated the presence of OAR in their formulation, show more reliability as a plan evaluation tool. Further innovations and research is required to define ideal, quantitative plan evaluation indices. Sigma index was found to be more efficient formula while evaluating homogeneity of a treatment plan.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10]



 

 
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