Radiotherapy Treatment Plan Quality Research Articles

Three-dimensional conformal radiotherapy breast cancer planning: An evaluation study comparing two techniques using homemade phantom

Anthropomorphic phantoms, which can provide equivalent human tissue densities, are one of the best solutions for verifying the quality of radiotherapy treatment plans produced by treatment planning systems. The goal of this work was to develop and fabricate a breast phantom to estimate radiation doses to the breast, lung, and surface using radiochromic films (EBT3) for basically two techniques of three-dimensional conformal radiotherapy (3D-CRT) treatment planning. Thirty-two acrylic slices were used to construct the phantom. Cork and Teflon were used to mimic the lung and bone, respectively. Four slots were drilled for dosimetry purposes to allow access to the areas of ionization chamber installation. Both wedged and open of two tangential beams techniques were applied. With a mean deviation of 1.02 ± 1.1, the variation between estimated point doses and measurements using the three ionization chambers ranged from 2.9 to 1.4%. Using 0, 5 and 10 mm boluses, the mean percentage doses on the target surface were 54.7, 88.6 and 91.7% of the prescribed dose (PD), respectively. The homemade phantom was appropriate for conducting quality control (QC) tests for 3D-CRT planning techniques for breast radiotherapy.

TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan

Properly selected beam angles contribute to the quality of radiotherapy treatment plans. However, the beam angle optimization (BAO) problem is difficult to solve to optimality due to its non-convex discrete nature with many local minima. In this study, we propose TBS-BAO, a novel approach for solving the BAO problem, and test it for non-coplanar robotic CyberKnife radiotherapy for prostate cancer. First, an ideal Pareto-optimal reference dose distribution is automatically generated using a priori multi-criterial fluence map optimization (FMO) to generate a plan that includes all candidate beams (total-beam-space, TBS). Then, this ideal dose distribution is reproduced as closely as possible in a subsequent segmentation/beam angle optimization step (SEG/BAO), while limiting the number of allowed beams to a user-selectable preset value. SEG/BAO aims at a close reproduction of the ideal dose distribution. For each of 33 prostate SBRT patients, 18 treatment plans with different pre-set numbers of allowed beams were automatically generated with the proposed TBS-BAO. For each patient, the TBS-BAO plans were then compared to a plan that was automatically generated with an alternative BAO method (Erasmus-iCycle) and to a high-quality manually generated plan. TBS-BAO was able to automatically generate plans with clinically feasible numbers of beams (∼25), with a quality highly similar to corresponding 91-beam ideal reference plans. Compared to the alternative Erasmus-iCycle BAO approach, similar plan quality was obtained for 25-beam segmented plans, while computation times were reduced from 10.7 hours to 4.8/1.5 hours, depending on the applied pencil-beam resolution in TBS-BAO. 25-beam TBS-BAO plans had similar quality as manually generated plans with on average 48 beams, while delivery times reduced from 22.3 to 18.4/18.1 min. TBS reference plans could effectively steer the discrete non-convex BAO.

Knowledge-Based Models for NRG HN001 Proton Plan Quality Analysis

<h3>Purpose/Objective(s)</h3> Radiotherapy treatment plan quality is essential to the success of clinical trials. Knowledge-based models have been used extensively for photon-based plan quality evaluation. With an increasing number of intensive modulated proton therapy (IMPT) plans submitted to clinical trials, we built knowledge-based proton planning (KBP) models for nasopharyngeal cancer cases. We report on the testing and the implementation of the models for the review of the quality of NRG-HN001 proton plans. <h3>Materials/Methods</h3> IMPT plans were manually created for 50 cases submitted to NRG-HN001. These plans were used to train a KBP model. Iterations of KBP-based re-optimization of model plans were performed to enhance the model performance. The final model was then used for generating 20 IMPT plans on the original proton cohort of patients to compare with submitted plans for quality evaluation. <h3>Results</h3> Among the 20 IMPT plans to date submitted to HN001, 11 plans showed deviation unacceptable for target coverage and 7 plans for OARs sparing. Compared with submitted plans, all KBP plans meet per protocol or variation acceptable values for both target and OAR volumes. The 20-plan average target volume VRxGy[%] increased 8% (<i>P</i> = 0.00371), meanwhile, brain stem D0.03 cc [Gy] decreased 8 Gy (<i>P</i> < 0.00001), optic nerves D0.03 cc [Gy] decreased 11 Gy (<i>P</i> = 0.0002), chiasm D0.03 cc [Gy] decreased 12 Gy (<i>P</i> = 0.0006), parotids mean dose decreased 4Gy (<i>P</i> = 0.002). Doses to other OARs (spinal cord, mandible, TM joint, temporal lobes, eyes, lens, larynx, cochlea, and esophagus) showed no statistically significant changes. <h3>Conclusion</h3> This is the first successful application of KBP for clinical trial IMPT plan quality assurance. KBP-aided IMPT plans demonstrate better target coverage with reduced OAR doses compared with submitted plans from multi-institutions, showing improvement opportunities for IMPT planning. It is feasible to use these KBP models for current and future nasopharyngeal cancer clinical trials to improve proton radiotherapy plan quality.

Radiotherapy treatment plan quality metrics for breast cancer patients using conformal planning techniques

Background: The purpose of this study is to evaluate and compare treatment plan quality metrics for postmastectomy breast cancer patients using 3Dimensional conformal radiotherapy (3DCRT) and intensity modulated radiotherapy (IMRT) planning techniques. Materials and Methods: The current study included 50 postmastectomy breast cancer patients out of which 24 were planned with 3DCRT and 26 with IMRT technique. Treatment plan quality metrics namely homogeneity index (HI), conformity index (CI), conformation number (CN), uniformity index (UI) and spillage index (R50), volume receiving 110% and 95% of prescribed dose (V110% and V95%) were calculated and compared for the two planning techniques. Results: IMRT plans have better conformity, homogeneity indices and low V110% than 3DCRT plans with almost similar R50% and V95%. Conclusions: Quantitative values of radiotherapy treatment plan quality metrics for the target are found in favour of IMRT technique than 3DCRT. Implementation of these five parameters is helpful for evaluating treatment plans along with slice by slice and DVH analysis. Keywords: Carcinoma of Breast; conformal planning techniques; radiotherapy.

PO-1798: Quality of radiotherapy treatment plans for locally advanced sinonasal tumors in a phase II trial

PO-1798: Quality of radiotherapy treatment plans for locally advanced sinonasal tumors in a phase II trial

Investigation of Target Minimum and Maximum Dosimetric Criteria for the Evaluation of Standardized Radiotherapy Plan &amp;lt;br/&amp;gt;—Target Minimum and Maximum Evaluation

Purpose: Standardization of tumor dosimetric coverage is essential for the evaluation of radiotherapy treatment plan quality. National clinical trials network RTOG protocols include tumor target dosimetric criteria that specify the prescription dose and minimum and maximum dose (Dmin and Dmax) coverages. This study investigated the impact of various minimum and maximum dose definitions using tumor control probability (TCP) models. Methods and Materials: Three disease sites (head and neck, lung, and prostate) were studied using target volume dosimetric criteria from the RTOG 0920, 1308, and 0938 protocols. Simulated target dose-volume histograms (DVHs) of Dmin and Dmax were modeled using the protocol specifications. Published TCP models for the three disease sites were applied to the DVH curves. The effects of various dose definitions on TCP were studied. Results: While the prescription dose coverage was maintained, a -3.7% TCP difference was observed for head and neck cancer when the target doses varied by 3.5% of the tumor volume from the point dose. For prostate and lung cancers, -3.3% and -2.2% TCP differences were observed, respectively. The TCPs for head and neck and prostate cancers were more negatively affected by deviations in the Dmin than the TCP for lung cancer. The lung TCP increased to a greater extent with a change in the Dmax compared with the head and neck and prostate TCPs. Conclusions: These results can be used to evaluate plan quality when the target dose only slightly deviates from the dosimetric criteria. When the overall target prescription dose coverage is maintained, the Dmax is recommended to be within 3% of the target volume: 98% (for head and neck and prostate) and 97% (for lung) of the target volume, satisfying the Dmin needed to maintain TCP variations at less than 2.1%. Using 0.03 cc instead of a point dose for Dmin and Dmax criteria minimally impacts TCPs.

Knowledge-Based Model for the Evaluation of NRG-HN001 Radiotherapy Treatment Plan Quality

Knowledge-Based Model for the Evaluation of NRG-HN001 Radiotherapy Treatment Plan Quality

Characterization of a Bayesian network-based radiotherapy plan verification model.

The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time-consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof-of-principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. We obtained 51540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de-identified Elekta oncology information system from the years 2010-2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29-node, 40-edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2-yr, 3-yr 4-yr and 5-yr windows. With a 4-yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1-yr steps. Compared to the 4-yr window moved back by 4yrs (2010-2013 vs 2014-2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4-yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

29. Ultra-hypofractionated prostate cancer radiotherapy: A global Unified Dosimetry Index (gUDI) for treatment plans evaluation (AIRC, grant: IG-13218)

Purpose Formulation of a global Unified Dosimetry Index (gUDI) for the evaluation of ultra-hypofractionated prostate Volumetric Modulated Arc Therapy (VMAT by RapidArc) treatment plans. Methods Radiotherapy treatment plan quality can be assessed in terms of dose coverage, conformity, homogeneity and dose gradient index. All these parameters could be included in the Unified Dosimetry Index (UDI). We developed a global UDI (gUDI) to evaluate treatment plans containing volumes irradiated with different dose prescriptions: Intensity Modulated Radiation Therapy with simultaneous integrated boost IMRT-SIB with 2 dose levels (36.25 Gy/5 fz for the whole prostate gland and 37.5 Gy/5 fz for Dominant Intraprostatic Lesion (DIL)). gUDI score equals to one for an ideal plan. To validate gUDI scoring system, 65 prostate cancer patients treated with ultra-hypofractionated IMRT-SIB using VMAT technique were evaluated. The computation of gUDI index was performed separately for plans with single or double DIL. Mean ( μ ) and standard deviations ( σ ) were calculated for all dosimetry indices and gUDI. Furthermore, gUDI μ and σ were analyzed to compare and classify treatment plans: under this ranking system, plans can be ranked as “excellent”, “good”, “average” or “poor”. Results Dose Coverage and Homogeneity index scores did not get away from the ideal values, both for the prostate and DIL. No statistical differences were founded for plans with single or double DIL. The gUDI index classification showed only 6 cases in the “poor” region and most of the plans scored as “average” and “good”. Conclusions gUDI score can be a useful tool to quantify treatment plans quality also when different dose prescriptions volumes are treated. Furthermore, gUDI allows to establish a standard on which dosimetry plans can be ranked.

Considerations for using data envelopment analysis for the assessment of radiotherapy treatment plan quality

PurposeThe operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.Design/methodology/approachIn total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.FindingsThe input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.Research limitations/implicationsThis work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.Practical implicationsDEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.Social implicationsDEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.Originality/valueTo the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

Knowledge Engineering-Based Quality Evaluation of NRG Oncology RTOG 0522 Treatment Plans

To evaluate radiotherapy treatment plan quality of IMRT plans submitted for RTOG 0522 clinical trial (A Randomized Phase III Trial of Concurrent Accelerated Radiation and Cisplatin versus Concurrent Accelerated Radiation, Cisplatin, and Cetuximab (C225) [Followed by Surgery for Selected Patients] for Stage III and IV Head and Neck Carcinomas). To use knowledge engineering based models from RapidPlan (built from HN002) for the quality study, and to re-optimize the plans by the model with the aim of further sparing the organs at risk and improve the quality of these plans in general. A head and neck RapidPlan model was initially built in Varian Eclipse treatment planning system (Version 13.6.15) using treatment plans from the NRGHN002 clinical trial (A Randomized Phase II Trial for patients with p16 positive, non-smoking-associated, locoregionally advanced oropharyngeal cancer). The model was duplicated and tuned to be used for QA and re-optimizing treatment plans from RTOG 0522. The modified model was then used to QA and re-optimize plans with deviation unacceptable scores (i.e., failed to pass the dosimetric compliance criteria of RTOG 0522). Initial quality review of 748 IMRT plans submitted to RTOG0522 show that 21 plans did not meet the spinal cord dose constraint (D0.03 cc <= 48 Gy), and 323 plans did not meet the dose constraint for parotid glands (one of the gland has mean dose less than 26 Gy). All the 21 plans that did not meet spinal cord constraint were pick for further analysis using the HN002 model, the results show that 15 of them has submitted plan dose volume histogram (DVH) fell above the predicted band. All the 21 plans were further optimized using model generated line objectives for organ at risks (OARs) (spinal cord, parotid glans, larynx, and NonPTV). All of plans achieved better scores after re-optimization (per-protocol or deviation acceptable). Average spinal cord Dmax [Gy] was decreased from 48.9 ± 2 Gy to 41.4 ± 4.8 Gy. Average parotids gland Dmean [Gy] were decreased from 34.1 ± 12 Gy to 29.5 ± 12 Gy. PTV coverage for re-optimized plans is comparable with original plans. PTV_7000 V65 Gy[%], D95%[Gy] and V80 Gy[%] for original plans are 98.7 ± 2.7%, 70 ± 2.3 Gy, 1.2 ± 4.5 Gy; and those for the re-optimized plans are 99.9 ± 0.1%, 70.8 ± 0.6 Gy, and 0.05 ± 0.1%, respectively. Knowledge-engineering based models predicted IMRT plans of better target coverage and OAR sparing as compared with submitted 0522 cases. All of IMRT plans with the worst score of deviation unacceptable from RTOG 0522 were improved to scores of variation acceptable or better with Knowledge based planning (KBP) re-optimization. These models will be used as part of the clinical trial quality assurance program to improve the quality of treatment plans submitted for clinical trials.

A longitudinal evaluation of improvements in radiotherapy treatment plan quality for head and neck cancer patients

PurposeTo investigate changes in head-and-neck cancer (HNC) plan quality following the introduction of new technologies and planning techniques in the last decade. Methods and materialsThirty plans were selected from each of four successive periods (P). P1: 7-field static intensity-modulated radiotherapy (IMRT) with parotid gland sparing; P2: dual-arc volumetric-modulated arc therapy (VMAT, similar to P3-P4), including submandibular gland sparing; P3: inclusion of individual swallowing muscles and attempts to further reduce parotid and oral cavity doses through manual interactive optimization; P4: containing the same organs-at-risk (OARs) as P3, but automatically interactively optimized. Plan benchmarking included mean salivary gland/swallowing muscle/oral cavity (Dsal/Dswal/Doc) doses. Differences in mean doses between the periods were analyzed by an ANCOVA, taking geometric differences across periods into account. ResultsCompared to P1, P2 plans improved Dsal by 3.4Gy on average. P3 improved Dsal/Dswal/Doc by 6.9/11.5/7.2Gy over P2, showing that Dswal and Dsal could be improved simultaneously. In P4, Doc/Dswal slightly improved over P3 by 1.7/3.8Gy. Improved OAR sparing in P3/P4 did not come at the cost of increased dose deposition elsewhere and planning target volume (PTV) dose homogeneity was similar. ConclusionsNew technologies and planning techniques were successfully implemented into routine clinical care and resulting in improved HNC plan quality.

SU‐E‐T‐578: On Definition of Minimum and Maximum Dose for Target Volume

Purpose:This study aims to investigate the impact of different minimum and maximum dose definitions in radiotherapy treatment plan quality evaluation criteria by using tumor control probability (TCP) models.Methods:Dosimetric criteria used in RTOG 1308 protocol are used in the investigation. RTOG 1308 is a phase III randomized trial comparing overall survival after photon versus proton chemoradiotherapy for inoperable stage II‐IIIB NSCLC. The prescription dose for planning target volume (PTV) is 70Gy. Maximum dose (Dmax) should not exceed 84Gy and minimum dose (Dmin) should not go below 59.5Gy in order for the plan to be “per protocol” (satisfactory).A mathematical model that simulates the characteristics of PTV dose volume histogram (DVH) curve with normalized volume is built. The Dmax and Dmin are noted as percentage volumes Dη% and D(100‐δ)%, with η and d ranging from 0 to 3.5. The model includes three straight line sections and goes through four points: D95%= 70Gy, Dη%= 84Gy, D(100‐δ)%= 59.5 Gy, and D100%= 0Gy. For each set of η and δ, the TCP value is calculated using the inhomogeneously irradiated tumor logistic model with D50= 74.5Gy and γ50=3.52.Results:TCP varies within 0.9% with η; and δ values between 0 and 1. With η and η varies between 0 and 2, TCP change was up to 2.4%. With η and δ variations from 0 to 3.5, maximum of 8.3% TCP difference is seen.Conclusion:When defined maximum and minimum volume varied more than 2%, significant TCP variations were seen. It is recommended less than 2% volume used in definition of Dmax or Dmin for target dosimetric evaluation criteria.This project was supported by NIH grants U10CA180868, U10CA180822, U24CA180803, U24CA12014 and PA CURE Grant.

AutoLock: a semiautomated system for radiotherapy treatment plan quality control.

A semiautomated system for radiotherapy treatment plan quality control (QC), named AutoLock, is presented. AutoLock is designed to augment treatment plan QC by automatically checking aspects of treatment plans that are well suited to computational evaluation, whilst summarizing more subjective aspects in the form of a checklist. The treatment plan must pass all automated checks and all checklist items must be acknowledged by the planner as correct before the plan is finalized. Thus AutoLock uniquely integrates automated treatment plan QC, an electronic checklist, and plan finalization. In addition to reducing the potential for the propagation of errors, the integration of AutoLock into the plan finalization workflow has improved efficiency at our center. Detailed audit data are presented, demonstrating that the treatment plan QC rejection rate fell by around a third following the clinical introduction of AutoLock.PACS number: 87.55.Qr

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

Introduction: the quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans.Materials and method: one hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test.Results: a voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set.Conclusion: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

Retrospective evaluation of dosimetric quality for prostate carcinomas treated with 3D conformal, intensity modulated and volumetric modulated arc radiotherapy.

IntroductionThis study examines and compares the dosimetric quality of radiotherapy treatment plans for prostate carcinoma across a cohort of 163 patients treated across five centres: 83 treated with three-dimensional conformal radiotherapy (3DCRT), 33 treated with intensity modulated radiotherapy (IMRT) and 47 treated with volumetric modulated arc therapy (VMAT).MethodsTreatment plan quality was evaluated in terms of target dose homogeneity and organs at risk (OAR), through the use of a set of dose metrics. These included the mean, maximum and minimum doses; the homogeneity and conformity indices for the target volumes; and a selection of dose coverage values that were relevant to each OAR. Statistical significance was evaluated using two-tailed Welch's T-tests. The Monte Carlo DICOM ToolKit software was adapted to permit the evaluation of dose metrics from DICOM data exported from a commercial radiotherapy treatment planning system.ResultsThe 3DCRT treatment plans offered greater planning target volume dose homogeneity than the other two treatment modalities. The IMRT and VMAT plans offered greater dose reduction in the OAR: with increased compliance with recommended OAR dose constraints, compared to conventional 3DCRT treatments. When compared to each other, IMRT and VMAT did not provide significantly different treatment plan quality for like-sized tumour volumes.ConclusionsThis study indicates that IMRT and VMAT have provided similar dosimetric quality, which is superior to the dosimetric quality achieved with 3DCRT.

Factors Influencing Conformity Index in Radiotherapy for Non–Small Cell Lung Cancer

The radiotherapy conformity index (CI) is a useful tool to quantitatively assess the quality of radiotherapy treatment plans, and represents the relationship between isodose distributions and target volume. A conformity index of unity implies high planning target volume (PTV) coverage and minimal unnecessary irradiation of surrounding tissues. We performed this analysis to describe the CI for lung cancer 3-dimensional conformal radiotherapy (3DCRT) and to identify clinical and technical determinants of CI, as it is not known which factors are associated with good quality 3D conformal radiotherapy treatment planning. Radiotherapy treatment plans from a database of 52 patients with inoperable Stage 1 to 3b lung cancer, on a hypofractionated 3DCRT trial were evaluated. A CI was calculated for all plans using the definition of the ICRU 62:CI = (TV/PTV), which is the quotient of the treated volume (TV) and the PTV. Data on patient, tumor, and planning variables, which could influence CI, were recorded and analyzed. Mean CI was 2.01 (range = 1.06-3.8). On univariate analysis, PTV ( p = 0.023), number of beams ( p = 0.036), medial vs. lateral tumor location ( p = 0.016), and increasing tumor stage ( p = 0.041) were associated with improved conformity. On multiple regression analysis, factors found to be associated with CI included central vs. peripheral tumor location ( p = 0.041) and PTV size ( p = 0.058). The term 3DCRT is used routinely in the literature, without any indication of the degree of conformality. We recommend routine reporting of conformity indices. Conformity indices may be affected by both planning variables and tumor factors.