Structure Dose Volume Histograms Research Articles

Combination of a Big Data Analytics Resource System With an Artificial Intelligence Algorithm to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients

PurposeWe combined clinical practice changes, standardizations, and technology to automate aggregation, integration, and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). We then developed novel artificial intelligence algorithms, coupled with the BDARS, to identify structure dose volume histograms (DVH) metrics associated with dysphagia. Methods and MaterialsFrom the BDARS harmonized data of ≥22,000 patients, we identified 132 patients recently treated for head and neck cancer who also demonstrated dysphagia scores that worsened from base line to a maximum grade ≥2. We developed a method that used both physical and biologically corrected (α/β = 2.5) DVH curves to test both absolute and percentage volume based DVH metrics. Combining a statistical categorization algorithm with machine learning (SCA-ML) provided more extensive detailing of response threshold evidence than either approach alone. A sensitivity guided, minimum input, machine learning (ML) model was iteratively constructed to identify the key structure DVH metric thresholds. ResultsSeven swallowing structures producing 738 candidate DVH metrics were ranked for association with dysphagia using SCA-ML scoring. Structures included superior pharyngeal constrictor (SPC), inferior pharyngeal constrictor (IPC), larynx, and esophagus. Bilateral parotid and submandibular gland (SG) structures were categorized by relative mean dose (eg, SG_high, SG_low) as a dose versus tumor centric analog to contra and ipsilateral designations. Structure DVH metrics with high SCA-ML scores included the following: SPC: D20% (equivalent dose [EQD2] Gy) ≥47.7; SPC: D25% (Gy) ≥50.4; IPC: D35% (Gy) ≥61.7; parotid_low: D60% (Gy) ≥13.2; and SG_high: D35% (Gy) ≥61.7. Larynx: D25% (Gy) ≥21.2 and SG_low: D45% ≥28.2 had high SCA-ML scores but were segmented on less than 90% of plans. A model based on SPC: D20% (EQD2 Gy) alone had sensitivity and area under the curve of 0.88 ± 0.13 and 0.74 ± 0.17, respectively. ConclusionsThis study provides practical demonstration of combining big data with artificial intelligence to increase volume of evidence in clinical learning paradigms.

Incorporating big data into treatment plan evaluation: Development of statistical DVH metrics and visualization dashboards

PurposeTo develop statistical dose-volume histogram (DVH)–based metrics and a visualization method to quantify the comparison of treatment plans with historical experience and among different institutions. Methods and materialsThe descriptive statistical summary (ie, median, first and third quartiles, and 95% confidence intervals) of volume-normalized DVH curve sets of past experiences was visualized through the creation of statistical DVH plots. Detailed distribution parameters were calculated and stored in JavaScript Object Notation files to facilitate management, including transfer and potential multi-institutional comparisons. In the treatment plan evaluation, structure DVH curves were scored against computed statistical DVHs and weighted experience scores (WESs). Individual, clinically used, DVH-based metrics were integrated into a generalized evaluation metric (GEM) as a priority-weighted sum of normalized incomplete gamma functions. Historical treatment plans for 351 patients with head and neck cancer, 104 with prostate cancer who were treated with conventional fractionation, and 94 with liver cancer who were treated with stereotactic body radiation therapy were analyzed to demonstrate the usage of statistical DVH, WES, and GEM in a plan evaluation. A shareable dashboard plugin was created to display statistical DVHs and integrate GEM and WES scores into a clinical plan evaluation within the treatment planning system. Benchmarking with normal tissue complication probability scores was carried out to compare the behavior of GEM and WES scores. ResultsDVH curves from historical treatment plans were characterized and presented, with difficult-to-spare structures (ie, frequently compromised organs at risk) identified. Quantitative evaluations by GEM and/or WES compared favorably with the normal tissue complication probability Lyman-Kutcher-Burman model, transforming a set of discrete threshold-priority limits into a continuous model reflecting physician objectives and historical experience. ConclusionsStatistical DVH offers an easy-to-read, detailed, and comprehensive way to visualize the quantitative comparison with historical experiences and among institutions. WES and GEM metrics offer a flexible means of incorporating discrete threshold-prioritizations and historic context into a set of standardized scoring metrics. Together, they provide a practical approach for incorporating big data into clinical practice for treatment plan evaluations.

SU-F-T-353: Effect of Planning Parameters On Tomotherapy Plan Quality and Treatment Time

Purpose: Aim of this study is to evaluate the effect of planning parameters such as pitch & field width on tomotherapy treatment plan quality and treatment time. Methods: We chose three treatment plans from different sites for this study. Combination of three different field widths (1 cm, 2.5 cm & 5 cm) and three different pitch values (0.215, 0.287 & 0.430) are used to generate plans. For a given patient all plans were optimized with same objectives, constrains, number of iterations and modulation factor. Plan quality was compared using clinically significant Dose Volume Histogram (DVH) indices (D98%, D95%, D50%, Dmean, D2, Dmax) and Homogeniety Index (HI). Also treatment times for all plans were compared to evaluate the delivery efficiency Results: DVH indices of target (D98%, D95%, D50%, Dmean, D2) showed increasing trend with the increase in the pitch as well as field width. The difference in DVH indices was less than 1.5%. Similarly critical structure DVH indices (Dmax, Dmean) also had increasing trend with the pitch value field width. The difference in DVH indices is upto 10% for the pitch and upto 50% increase in dose for 5cm collimator. The variation in homogeneity index with reference to 1 cm field width was 1–4% and 2.2 – 5% for 2.5cm and 5cm field widths respectively. For a given field width increase in pitch reduces the treatment time upto 30%. Increase in field width from 1cm to 5 cm reduces the treatment time upto 75%. Conclusion: Effects of planning parameters on tomotherapy plan quality & treatment time were studied. The effect of pitch is found to be less than the field width on the target, critical structures dose as well as treatment time. Optimal selection of planning parameter will improve the treatment delivery efficiency with appropriate dose to target and critical structure.

A Clinical Objective IMRT QA Method Based on Portal Dosimetry and Electronic Portal Imager Device (EPID) Measurement

Clinical objective IMRT QA is a new approach to conduct patient specific IMRT QA by evaluating the QA result based on metrics like the structure dose volume histograms (DVH) on the planning CT image. Different from traditional 2D planar dose based IMRT QA in which all the measurements and evaluations were based on a phantom, clinical objective IMRT QA reveals the direct clinical impact of any measured variation based on the predicted delivered dose to the patient target volumes and critical organs. In this study, we proposed a method to implement the clinical objective IMRT QA procedure by using generally available dosimetry equipment in a radiation oncology clinic. The proposed procedure was tested and validated on five IMRT plans by using a solid water phantom, which served as a test patient, along with total 30 ion chamber measurements. The result showed that the proposed QA procedure successfully achieved the goal of clinical objective IMRT QA. Based on the ion chamber measurements, the QA procedure predicted that the average variations between the planned and the delivered doses were within 1.9%. This method may hold potentials to help radiation oncology clinics to move from the traditional IMRT QA to the clinical objective IMRT QA.

3D Analysis of Intensity-Modulated Radiation Therapy Quality Assurance Measurement using a 2D Diode Array

Intensity-modulated radiation therapy (IMRT) quality assurance (QA) is often performed using a 2D device and compares measured and computed fluence maps to determine if a field passes or fails certain dose and position criteria. The effects of a measured deviation to the 3D patient spatial dosimetry and dose volume histogram (DVH) are largely unknown because they cannot be analyzed using commercial 2D array IMRT QA systems. We report an in-house treatment planning system (TPS) PLanUNC based 3D IMRT QA analysis approach that has been used in our institution for the past ten years when 2D fluence map IMRT QA failed. In this approach the measured 2D fluence maps are imported back to PLanUNC and used to re-compute 3D patient dosimetry including DVHs. The 2D fluence map IMRT QA criteria is that the measured dose for 95% of the detectors is within 5% of the planned dose, and that the distance-to-agreement be within 4mm (5%/4mm). 22 IMRT plans that had at least one field fail initial QA using MapCHECK 2 are examined using our 3D QA approach. The DVH analysis shows that 19/22 plans that failed initial QA were within 2% of the planned target and critical structure DVHs. 3/22 IMRT plans were found to have DVH difference greater than 2%. The 3D analysis of 2D IMRT QA result shows that when a fluence map QA fails for a single field, often it is clinically insignificant in terms of patient 3D dosimetry

SU‐GG‐T‐490: Analysis of Clinical Data Sets by Comparative DVH Analysis

Analysis of large clinical dosimetry data sets has been limited to evaluation of single number dosimetric quantifiers (like V20 or D90). The quantifiers are linked with clinical outcomes and conclusions drawn based on comparative statistical tests. These quantifiers are often a matter of guesswork, at best chosen by recommendation of investigators who have performed similar analyses. A tool for comparative analysis of the entire dose volume histogram for large data sets has been developed and is described. This tool has been used as an interface between several treatment planning systems, external beam and brachytherapy. The downloaded structure dose volume histograms and data set analysis and comparison are performed within the tool. As an example of this process, a series of implants, performed at multiple institutions, has been analyzed following import from Variseed (Varian Medical Systems). Ten patients who had RTOG Grade 2 rectal complications following the implant were compared to twenty‐three patients who did not experience any rectal complications. While a similar analysis has been performed previously, this work was limited to analyzing rectal dosimetry in terms of 2 or 3 rectal dose quantifiers. While the previous effort was able to show statistical significance with R100, it is apparent from the present work that the critical doses for rectal complications are much more likely associated with doses to the rectum which are much lower than the prescription dose, in the range of 40–50 Gy. This is verified by improved significance of the correlation between the two arms of the cohort at these dose levels. The monotherapy dataset demonstrates the utility of the tool and the associated process. The tool has also been used to analyze external beam data sets to demonstrate the dosimetric improvement of inverse planning over conventional planning for external beam radiotherapy.

WE‐D‐T‐6E‐02: A Clinical Planning Tool for Optimization of Intensity Modulated Radiotherapy Parameters

Purpose: To develop a clinical tool that incorporates novel strategies to optimize all involved parameters in IMRT treatment planning: (a) the number of beams, (b) beam orientations, and (c) fluence maps. Method and Materials: An IMRT planning tool was developed in the MATLAB (The MathWorks, Natick, Ma.) environment with the capability of interfacing to a commercial treatment planning system. It consists of three components: image segmentation, parameter optimization, and isodose and dose‐volume histogram (DVH) display. The parameter optimization tool starts with a large number of user input feasible beam orientations and sequentially eliminates beams. Beam elimination is terminated when unacceptable deterioration of the critical structure DVH is encountered. The algorithm uses the novel strategy of forcing the sequential dose distributions to imitate, as best as possible, the dose distribution with all beam orientations. This not only ensures a clinically acceptable final dose distribution but also greatly speeds up the computation time by pre‐knowledge of the expected dose at every point in the patient body. This process is illustrated in a typical prostate cancer IMRT plan with prescription doses to prostate and seminal vesicles expanded by 1cm, and more stringent than usual constraints to rectum, bladder, and femoral heads. Results: For the case illustrated, the parameter optimization tool sequentially reduced the number of selected beams from a user input 36 to 7 beams. Reducing the number of selected beams below 7 resulted in abrupt deterioration of the critical structure DVHs. In the tested cases, the resulting dose distributions are clinically excellent owing to the novel strategy of imitating the dose distribution with all beams. Conclusions: The clinical tool presented here serves an important purpose by promoting the extraction of the full benefit of IMRT by optimizing all involved parameters. It can interface with commercial treatment planning systems to enhance their functionality.

Beam orientation selection for intensity-modulated radiation therapy based on target equivalent uniform dose maximization

Purpose: To develop an automated beam-orientation selection procedure for intensity-modulated radiotherapy (IMRT), and to determine if a small number of beams picked by this automated procedure can yield results comparable to a large number of manually placed orientations. Methods and Materials: The automated beam selection procedure maximizes an unconstrained objective function composed of target equivalent uniform dose (EUD) and critical structure dose-volume histogram (DVH) constraints. Beam orientations are selected from a large feasible set of directions through a series of alternating fluence optimization and orientation alteration steps, until convergence to a stable orientation set. The fluence optimization step adjusts fluences to maximize the objective function. The orientation alteration step substitutes beams in the orientation set currently under consideration with beams of the parent set in the immediate angular vicinity; the altered orientation set is deemed current if it produces a higher objective function value in the fluence optimization step. Results and Conclusions: It is demonstrated, for prostate IMRT planning, that a modest number of appropriately selected beam orientations (3 or 5) can provide dose distributions as satisfactory as those produced by a large number of unselected equispaced orientations. Such selected beam orientations can reduce overall treatment time, thus making IMRT more clinically practical.

Optimal dose/volume constraints of sensitive structures in inverse planning for nasopharyngeal carcinoma

Purpose: The key to inverse planning is to provide a set of achievable and safe dose/volume constraints that spare the sensitive structures but still allows delivery of a high dose to tumor targets. A standard set of such constraints are not available for nasopharyngeal cancers. As a result, an optimal inverse plan involves numerous iterations of changing dose/volume constraints. The purpose of this study is to develop dose constraints for sensitive structures of the head/neck region that provide optimal intensity modulated radiation therapy (IMRT) plans while minimizing the treatment planning time. Materials and methods: Twenty-five nasopharyngeal carcinoma patients consecutively treated at our institution with IMRT were reviewed. Of these, 9 were T1/T2 and 16 were T3/T4 tuumors. The average dose given to the Gross Tumor Volume (GTV) and Planning Tumor Volume (PTV) was 74 Gy and 65.7 Gy respectively. The percentage of the GTV and PTV receiving <95% of the prescribed dose was 3.8% and 1.8%. The sensitive structure dose volume histograms (DVHs) were also reviewed. Different parameters were evaluated for structures organized in series versus those in parallel. Serial functional subunit parameters were maximum point dose and maximum dose to 5%, 10% of its volume. Parallel functional subunit parameters were mean dose and maximum dose to 50%, 80% of the volume. Because the clinical outcomes for these patients were excellent, these dose constraints can be used as a guideline. Results: The average doses with standard deviations to normal structures are summarized in the table below. When these average dose/volume parameters were applied to the optimization process as constraints, the number of iterations were significantly reduced. Conclusions: The efficacy of computer optimized inverse planning is dependent on a set of dose constraints that must be both clinically safe and achievable. Through our experience of the 25 nasopharyngeal carcinoma patients, we have established a series of normal tissue dose/volume constraints which provided high doses to target volumes as well as safe doses to surrounding sensitive structures. Significant reductions in planning time was also achieved using these dose constraints. Tabled 1Serial StructuresMean Maximum Point Dose (SD)Mean Maximum Dose to 5% VolumeMean Maximum Dose to 10% VolumeBrain Stem53.8 Gy (6.3)41.9 Gy (8.5)38.9 Gy (9.5)Spinal Cord40.5 Gy (10.6)32.0 Gy (12.3)26.0 Gy (16.5)Chiasm36.8 Gy (13.4)30.6 Gy (12.5)28.7 Gy (11.3)Optic Nerve34.8 Gy (17.0)29.5 Gy (14.4)26.4 Gy (14.2)Globe29.3 Gy (15.4)18.3 Gy (11.6)15.7 Gy (10.9)Parallel StructuresMean Average Dose (SD)Mean Maximum Dose to 50% VolumeMean Maximum Dose to 80% VolumeTMJ36.2 Gy (6.8)34.2 Gy (7.5)30 Gy (6.0)Middle/Inner Ear46.8 Gy (11.2)45.2 Gy (13.3)38.6 Gy (13.2)Parotid Gland27.2 Gy (5.8)24.7 Gy (5.6)18.2 Gy (6.0) Open table in a new tab

Beam orientation selection for intensity modulated radiation therapy based on target equivalent uniform dose maximization

Purpose: Manual beam selection for Intensity Modulated Radiotherapy (IMRT) can be difficult due to the non-intuitive dose contribution of each beam to the target and critical structures. Selecting a large number of different beam orientations may produce a satisfactory treatment plan, but would also greatly increase treatment time. An appropriately chosen small number of beam orientations can help reduce treatment time, while producing a satisfactory dose distribution. This work presents an automated scheme which selects a set of beam orientations that maximizes target Equivalent Uniform Dose (EUD) while satisfying user inputs of critical structure dose-volume histogram (DVH) constraints, minimum entry angle between beams, and number of orientations.

Removing the effect of statistical uncertainty on dose-volumehistograms from Monte Carlo dose calculations

Dose-volume histograms (DVHs) of the dose distributions calculated by theMonte Carlo method contain statistical uncertainties. The Monte Carlo DVH canbe considered as blurred from the noiseless DVH by the statisticaluncertainty. The focus of the present work is on the removal of thestatistical uncertainty effect on the Monte Carlo DVHs and the reconstructionof the noiseless DVHs. We first study the effect of statistical uncertainty.It is found that the steeper the DVH, the more significant the effect. Fortypical critical structure DVHs the effect is usually negligible. For thetarget DVHs the effect could be clinically significant, depending on thevalue of uncertainty and the slope of the DVH. We then propose an iterativereconstruction algorithm. Using the DVHs and statistical uncertainties fromthe Monte Carlo simulations, we are able to reconstruct the noiseless DVHs. Ahypothetical example and a number of clinical cases have been used to test theproposed algorithm. For each clinical case, two Monte Carlo simulations(denoted A and B) were performed. Simulation A has very large statisticaluncertainties (about 10% of dose in the target volume) while simulation B hasvery small uncertainties (about 1%). DVHs from simulation B were used toapproximate the noiseless DVHs. Using the proposed algorithm, the effect ofstatistical uncertainty can be removed from the DVHs of simulation A. Thereconstructed DVHs were in good agreement with the DVHs from simulation B. Theproposed approach is expected to be useful in removing the blurring effect ona quickly calculated Monte Carlo DVH when performing the iterative forwardtreatment planning.

The effect of dose calculation uncertainty on the evaluation of radiotherapy plans.

Monte Carlo dose calculations will potentially reduce systematic errors that may be present in currently used dose calculation algorithms. However, Monte Carlo calculations inherently contain random errors, or statistical uncertainty, the level of which decreases inversely with the square root of computation time. Our purpose in this study was to determine the level of uncertainty at which a lung treatment plan is clinically acceptable. The evaluation methods to decide acceptability were visual examination of both isodose lines on CT scans and dose volume histograms (DVHs), and reviewing calculated biological indices. To study the effect of systematic and/or random errors on treatment plan evaluation, a simulated "error-free" reference plan was used as a benchmark. The relationship between Monte Carlo statistical uncertainty and dose was found to be approximately proportional to the square root of the dose. Random and systematic errors were applied to a calculated lung plan, creating dose distributions with statistical uncertainties of between 0% and 16% (1 s.d.) at the maximum dose point and also distributions with systematic errors of -16% to 16% at the maximum dose point. Critical structure DVHs and biological indices are less sensitive to calculation uncertainty than those of the target. Systematic errors affect plan evaluation accuracy significantly more than random errors, suggesting that Monte Carlo dose calculation will improve outcomes in radiotherapy. A statistical uncertainty of 2% or less does not significantly affect isodose lines, DVHs, or biological indices.