Benchmarking the Radiation Oncology Alternative Payment Model: Changes in Medicare Reimbursement for 16 Common Radiation Therapy Treatment Courses.

Radiation oncology (RO) has seen declines in Medicare reimbursement (MCR) in the past decade under the current fee-for-service model. Although studies have explored decline in reimbursement at a per-code level, to our knowledge there are no recent studies analyzing changes in MCR over time for common RO treatment courses. By analyzing changes in MCR for common treatment courses, our study had 3 objectives: (1) to provide practitioners and policymakers with estimates of recent reimbursement changes for common treatment courses; (2) to provide an estimate of how reimbursement will change in the future under the current fee-for-service model if current trends continue; and (3) to provide a baseline for treatment episodes in the event that the episode-based Radiation Oncology Alternative Payment Model is eventually implemented. Specifically, we quantified inflation- and utilization-adjusted changes in reimbursement for 16 common radiation therapy (RT) treatment courses from 2010 to 2020. Centers for Medicare & Medicaid Services Physician/Supplier Procedure Summary databases were used to obtain reimbursement for all RO procedures in 2010, 2015, and 2020 for free-standing facilities. Inflation-adjusted average reimbursement (AR) per billing instance was calculated for each Healthcare Common Procedure Coding System code using 2020 dollars. For each year, the billing frequency of each code was multiplied by the AR per code. Results were summed per RT course per year, and AR for RT courses were compared. Sixteen common RO courses for head and neck, breast, prostate, lung, and palliative RT were analyzed. AR decreased for all 16 courses from 2010 to 2020. From 2015 to 2020, the only course that increased in AR was palliative 2-dimensional 10-fraction 30 Gy, which increased by 0.4%. Courses using intensity modulated RT saw the largest AR decline from 2010 to 2020, ranging from 38% to 39%. We report significant declines in reimbursement from 2010 to 2020 for common RO courses, with the largest declines for intensity modulated RT. Policymakers should consider the significant cuts to reimbursement that have already occurred when considering future reimbursement adjustment under the current fee-for-service model or when considering mandatory adoption of a new payment system with further cuts and the negative effect of such cuts on quality and access to care.

A component method to delineate surgical spine implants for proton Monte Carlo dose calculation.

PURPOSE: Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images. METHODS: The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours. RESULTS: For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm3 for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm3 , respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm3 for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt-chromium alloys by different vendors. CONCLUSIONS: A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof-of-concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head-and-neck patients where tight margins are required to spare organs at risk.

A potential revolution in cancer treatment: A topical review of FLASH radiotherapy.

FLASH radiotherapy (RT) is a novel technique in which the ultrahigh dose rate (UHDR) (≥40 Gy/s) is delivered to the entire treatment volume. Recent outcomes of in vivo studies show that the UHDR RT has the potential to spare normal tissue without sacrificing tumor control. There is a growing interest in the application of FLASH RT, and the ultrahigh dose irradiation delivery has been achieved by a few experimental and modified linear accelerators. The underlying mechanism of FLASH effect is yet to be fully understood, but the oxygen depletion in normal tissue providing extra protection during FLASH irradiation is a hypothesis that attracts most attention currently. Monte Carlo simulation is playing an important role in FLASH, enabling the understanding of its dosimetry calculations and hardware design. More advanced Monte Carlo simulation tools are under development to fulfill the challenge of reproducing the radiolysis and radiobiology processes in FLASH irradiation. FLASH RT may become one of standard treatment modalities for tumor treatment in the future. This paper presents the history and status of FLASH RT studies with a focus on FLASH irradiation delivery modalities, underlying mechanism of FLASH effect, in vivo and vitro experiments, and simulation studies. Existing challenges and prospects of this novel technique are discussed in this manuscript.

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.

Objective. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization models.Approach.The proposed framework includes two experiments to validatein vivodose and water equivalent thickness (WET) distributions using anthropomorphic and porcine phantoms. Phantoms were irradiated using anteroposterior proton beams, and the exit doses and residual ranges were measured by MatriXX PT and a multi-layer strip ionization chamber. Two pre-trained conventional and physics-informed residual networks (RN/PRN) were used for mass density inference from DECT. Additional two heuristic material conversion models using single-energy CT (SECT) and DECT were implemented for comparisons. The gamma index was used for dose comparisons with criteria of 3%/3 mm (10% dose threshold).Main results. The phantom study showed that MCDC with PRN achieved mean gamma passing rates of 95.9% and 97.8% for the anthropomorphic and porcine phantoms. The rates were 86.0% and 79.7% for MCDC with the empirical DECT model. WET analyses indicated that the mean WET variations between measurement and simulation were -1.66 mm, -2.48 mm, and -0.06 mm for MCDC using a Hounsfield look-up table with SECT and empirical and PRN models with DECT. Validation experiments indicated that MCDC with PRN achieved consistent dose and WET distributions with measurement.Significance. The proposed framework can be used to identify the optimal CT-based material characterization model for MCDC to improve proton range uncertainty. The framework can systematically verify the accuracy of proton treatment planning, and it can potentially be implemented in the treatment room to be instrumental in online adaptive treatment planning.

Synchronized high-speed scintillation imaging of proton beams, generated by a gantry-mounted synchrocyclotron, on a pulse-by-pulse basis.

BACKGROUND: With the emergence of more complex and novel proton delivery techniques, there is a need for quality assurance tools with high spatiotemporal resolution to conveniently measure the spatial and temporal properties of the beam. In this context, scintillation-based dosimeters, if synchronized with the radiation beam and corrected for ionization quenching, are appealing. PURPOSE: To develop a synchronized high-speed scintillation imaging system for characterization and verification of the proton therapy beams on a pulse-by-pulse basis. MATERIALS AND METHODS: A 30 cm × 30 cm × 5 cm block of BC-408 plastic scintillator placed in a light-tight housing was irradiated by proton beams generated by a Mevion S250 proton therapy synchrocyclotron. A high-speed camera system, placed perpendicular to the beam direction and facing the scintillator, was synchronized to the accelerator's pulses to capture images. Opening and closing of the camera's shutter was controlled by setting a proper time delay and exposure time, respectively. The scintillation signal was recorded as a set of two-dimensional (2D) images. Empirical correction factors were applied to the images to correct for the nonuniformity of the pixel sensitivity and quenching of the scintillator. Proton range and modulation were obtained from the corrected images. RESULTS: The camera system was able to capture all data on a pulse-by-pulse basis at a rate of ∼504 frames per second. The applied empirical correction method for ionization quenching was effective and the corrected composite image provided a 2D map of dose distribution. The measured range (depth of distal 90%) through scintillation imaging agreed within 1.2 mm with that obtained from ionization chamber measurement. CONCLUSION: A high-speed camera system capable of capturing scintillation signals from individual proton pulses was developed. The scintillation imaging system is promising for rapid proton beam characterization and verification.

Decreases in Radiation Oncology Medicare Reimbursement Over Time: Analysis by Billing Code.

PURPOSE: Radiation oncology (RO) has seen declines in Medicare reimbursement (MCR). However, there are no recent studies analyzing the contributions of specific billing codes to overall RO reimbursement. We compared total MCR for specific Healthcare Common Procedure Coding System (HCPCS) codes in 2019 with MCR for those codes in 2010 and 2015, corrected for inflation, to see how the same basket of RO services in 2019 would have been reimbursed in 2010 and 2015 (adjusted MCR). METHODS AND MATERIALS: The Centers for Medicare & Medicaid Services Physician/Supplier Procedure Summary database was used to obtain MCR data for RO HCPCS codes in 2010, 2015, and 2019. For each code, the total allowed charge was divided by the number of submitted claims to calculate the average MCR per claim in 2010, 2015, and 2019. The 2019 billing frequency for each code was then multiplied by the inflation-adjusted average MCR for those codes in 2010 and 2015 to determine what the MCR would have been in 2010 and 2015 using 2019 dollars and utilization rates. Results were compared with actual 2019 MCR to calculate the projected difference. RESULTS: Total inflation-adjusted RO MCR was $2281 million (M), $1991 M, and $1848 M in 2010, 2015, and 2019 respectively. This represents a cut of $433 M (19%) and $143 M (7%) from 2010 and 2015, respectively, to 2019. After utilization adjustment, total reimbursement was $2534 M, $2034 M, and $1848 M for 2010, 2015, and 2019, respectively, representing a cut of $686 M (27%) and $186 M (9%) from 2010 and 2015, respectively, to 2019. Intensity modulated radiation therapy (IMRT) treatment delivery and planning accounted for $917 M (36%), $670 M (33%), and $573 M (31%) of the adjusted MCR in 2010, 2015, and 2019, respectively. CONCLUSIONS: Medicare reimbursement decreased substantially from 2010 to 2019. A decline in IMRT treatment reimbursement was the primary driver of MCR decline. When considering further cuts, policymakers should consider these trends and their consequences for health care quality and access.

NRG Oncology Survey of Monte Carlo Dose Calculation Use in US Proton Therapy Centers.

PURPOSE/OBJECTIVES: Monte Carlo (MC) dose calculation has appeared in primary commercial treatment-planning systems and various in-house platforms. Dual-energy computed tomography (DECT) and metal artifact reduction (MAR) techniques complement MC capabilities. However, no publications have yet reported how proton therapy centers implement these new technologies, and a national survey is required to determine the feasibility of including MC and companion techniques in cooperative group clinical trials. MATERIALS/METHODS: A 9-question survey was designed to query key clinical parameters: scope of MC utilization, validation methods for heterogeneities, clinical site-specific imaging guidance, proton range uncertainties, and how implants are handled. A national survey was distributed to all 29 operational US proton therapy centers on 13 May 2019. RESULTS: We received responses from 25 centers (86% participation). Commercial MC was most commonly used for primary plan optimization (16 centers) or primary dose evaluation (18 centers), while in-house MC was used more frequently for secondary dose evaluation (7 centers). Based on the survey, MC was used infrequently for gastrointestinal, genitourinary, gynecology and extremity compared with other more heterogeneous disease sites (P < .007). Although many centers had published DECT research, only 3/25 centers had implemented DECT clinically, either in the treatment-planning system or to override implant materials. Most centers (64%) treated patients with metal implants on a case-by-case basis, with a variety of methods reported. Twenty-four centers (96%) used MAR images and overrode the surrounding tissue artifacts; however, there was no consensus on how to determine metal dimension, materials density, or stopping powers. CONCLUSION: The use of MC for primary dose calculation and optimization was prevalent and, therefore, likely feasible for clinical trials. There was consensus to use MAR and override tissues surrounding metals but no consensus about how to use DECT and MAR for human tissues and implants. Development and standardization of these advanced technologies are strongly encouraged for vendors and clinical physicists.

Treatment utilization and outcomes in elderly patients with locally advanced esophageal carcinoma: a review of the National Cancer Database.

For elderly patients with locally advanced esophageal cancer, therapeutic approaches and outcomes in a modern cohort are not well characterized. Patients ≥70 years old with clinical stage II and III esophageal cancer diagnosed between 1998 and 2012 were identified from the National Cancer Database and stratified based on treatment type. Variables associated with treatment utilization were evaluated using logistic regression and survival evaluated using Cox proportional hazards analysis. Propensity matching (1:1) was performed to help account for selection bias. A total of 21,593 patients were identified. Median and maximum ages were 77 and 90, respectively. Treatment included palliative therapy (24.3%), chemoradiation (37.1%), trimodality therapy (10.0%), esophagectomy alone (5.6%), or no therapy (12.9%). Age ≥80 (OR 0.73), female gender (OR 0.81), Charlson-Deyo comorbidity score ≥2 (OR 0.82), and high-volume centers (OR 0.83) were associated with a decreased likelihood of palliative therapy versus no treatment. Age ≥80 (OR 0.79) and Clinical Stage III (OR 0.33) were associated with a decreased likelihood, while adenocarcinoma histology (OR 1.33) and nonacademic cancer centers (OR 3.9), an increased likelihood of esophagectomy alone compared to definitive chemoradiation. Age ≥80 (OR 0.15), female gender (OR 0.80), and non-Caucasian race (OR 0.63) were associated with a decreased likelihood, while adenocarcinoma histology (OR 2.10) and high-volume centers (OR 2.34), an increased likelihood of trimodality therapy compared to definitive chemoradiation. Each treatment type demonstrated improved survival compared to no therapy: palliative treatment (HR 0.49) to trimodality therapy (HR 0.25) with significance between all groups. Any therapy, including palliative care, was associated with improved survival; however, subsets of elderly patients with locally advanced esophageal cancer are less likely to receive aggressive therapy. Care should be taken to not unnecessarily deprive these individuals of treatment that may improve survival.

Consensus Statement on Proton Therapy in Early-Stage and Locally Advanced Non-Small Cell Lung Cancer.

Radiation dose escalation has been shown to improve local control and survival in patients with non-small cell lung cancer in some studies, but randomized data have not supported this premise, possibly owing to adverse effects. Because of the physical characteristics of the Bragg peak, proton therapy (PT) delivers minimal exit dose distal to the target volume, resulting in better sparing of normal tissues in comparison to photon-based radiation therapy. This is particularly important for lung cancer given the proximity of the lung, heart, esophagus, major airways, large blood vessels, and spinal cord. However, PT is associated with more uncertainty because of the finite range of the proton beam and motion for thoracic cancers. PT is more costly than traditional photon therapy but may reduce side effects and toxicity-related hospitalization, which has its own associated cost. The cost of PT is decreasing over time because of reduced prices for the building, machine, maintenance, and overhead, as well as newer, shorter treatment programs. PT is improving rapidly as more research is performed particularly with the implementation of 4-dimensional computed tomography-based motion management and intensity modulated PT. Given these controversies, there is much debate in the oncology community about which patients with lung cancer benefit significantly from PT. The Particle Therapy Co-operative Group (PTCOG) Thoracic Subcommittee task group intends to address the issues of PT indications, advantages and limitations, cost-effectiveness, technology improvement, clinical trials, and future research directions. This consensus report can be used to guide clinical practice and indications for PT, insurance approval, and clinical or translational research directions.

Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk.

PURPOSE: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose-volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems' biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. METHODS: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller-Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes' rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. RESULTS: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0.77), mean heart dose (0.69), and a pre-to-midtreatment change in ACE (0.66). When RP prediction was made only with pretreatment information, the AUC ranged from 0.76 to 0.81 depending on the ensemble size. Bootstrap validation of graph features in the ensemble quantified confidence of association between variables in the graphs where ten interactions were statistically significant. CONCLUSIONS: The presented BN methodology provides the flexibility to model hierarchical interactions between RP covariates, which is applied to probabilistic inference on RP. The authors' preliminary results demonstrate that such framework combined with an ensemble method can possibly improve prediction of RP under real-life clinical circumstances such as missing data or treatment plan adaptation.

A comparison of surgical intervention and stereotactic body radiation therapy for stage I lung cancer in high-risk patients: a decision analysis.

OBJECTIVE: We sought to compare the relative cost-effectiveness of surgical intervention and stereotactic body radiation therapy in high risk patients with clinical stage I lung cancer (non-small cell lung cancer). METHODS: We compared patients chosen for surgical intervention or SBRT for clinical stage I non-small cell lung cancer. Propensity score matching was used to adjust estimated treatment hazard ratios for the confounding effects of age, comorbidity index, and clinical stage. We assumed that Medicare-allowable charges were $15,034 for surgical intervention and $13,964 for stereotactic body radiation therapy. The incremental cost-effectiveness ratio was estimated as the cost per life year gained over the patient's remaining lifetime by using a decision model. RESULTS: Fifty-seven patients in each arm were selected by means of propensity score matching. Median survival with surgical intervention was 4.1 years, and 4-year survival was 51.4%. With stereotactic body radiation therapy, median survival was 2.9 years, and 4-year survival was 30.1%. Cause-specific survival was identical between the 2 groups, and the difference in overall survival was not statistically significant. For decision modeling, stereotactic body radiation therapy was estimated to have a mean expected survival of 2.94 years at a cost of $14,153 and mean expected survival with surgical intervention was 3.39 years at a cost of $17,629, for an incremental cost-effectiveness ratio of $7753. CONCLUSIONS: In our analysis stereotactic body radiation therapy appears to be less costly than surgical intervention in high-risk patients with early stage non-small cell lung cancer. However, surgical intervention appears to meet the standards for cost-effectiveness because of a longer expected overall survival. Should this advantage not be confirmed in other studies, the cost-effectiveness decision would be likely to change. Prospective randomized studies are necessary to strengthen confidence in these results.

A Bayesian network approach for modeling local failure in lung cancer.

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

Heart irradiation as a risk factor for radiation pneumonitis.

PURPOSE: to investigate the potential role of incidental heart irradiation on the risk of radiation pneumonitis (RP) for patients receiving definitive radiation therapy for non-small-cell lung cancer (NSCLC). MATERIAL AND METHODS: two hundred and nine patient datasets were available for this study. Heart and lung dose-volume parameters were extracted for modeling, based on Monte Carlo-based heterogeneity corrected dose distributions. Clinical variables tested included age, gender, chemotherapy, pre-treatment weight-loss, performance status, and smoking history. The risk of RP was modeled using logistic regression. RESULTS: the most significant univariate variables were heart related, such as heart heart V65 (percent volume receiving at least 65 Gy) (Spearman Rs = 0.245, p < 0.001). The best-performing logistic regression model included heart D10 (minimum dose to the hottest 10% of the heart), lung D35, and maximum lung dose (Spearman Rs = 0.268, p < 0.0001). When classified by predicted risk, the RP incidence ratio between the most and least risky 1/3 of treatments was 4.8. The improvement in risk modeling using lung and heart variables was better than using lung variables alone. CONCLUSIONS: these results suggest a previously unsuspected role of heart irradiation in many cases of RP.

Assessment of intrafraction mediastinal and hilar lymph node movement and comparison to lung tumor motion using four-dimensional CT.

PURPOSE: To quantify the amount of free-breathing motion measured using Four-dimensional (4D) CT scans of mediastinal and hilar lymph nodes and to compare this motion to the primary lung tumor motion. METHODS AND MATERIALS: Twenty patients with primary lung cancer, radiographically positive lymph nodes, and prior 4D CT scans were retrospectively analyzed. The 4D CT data sets were divided into four respiratory phases, and the primary tumor and radiographically positive nodes were contoured. Geometric and volumetric analysis was performed to analyze the motion of the primary tumors and the lymph nodes. RESULTS: The mean lymph node motion was 2.6 mm in the mediolateral direction, 2.5 mm in the anterior-posterior direction, and 5.2 mm in the cranial-caudal direction with a maximum of 14.4 mm. All lymph nodes were found to move inferiorly during inspiration, with 12.5% of nodes moving more than 1 cm. Lymph nodes located below the carina showed significantly more motion than those above the carina (p = 0.01). In comparing the primary tumor motion to the lymph node motion, no correlation was identified. CONCLUSIONS: Four-dimensional CT scans can be used to measure the motion of the primary lung tumor and pathologic lymph nodes encountered during the respiratory cycle. Both the primary lung tumor and the lymph node must to be examined to assess their individual degree of motion. This study demonstrates the need for individualized plans to assess the heterogeneous motion encountered in both primary lung tumors and among lymph node stations.

Retrospective monte carlo dose calculations with limited beam weight information.

An important unresolved issue in outcomes analysis for lung complications is the effect of poor or completely lacking heterogeneity corrections in previously archived treatment plans. To estimate this effect, we developed a novel method based on Monte Carlo (MC) dose calculations which can be applied retrospectively to RTOG/AAPM-style archived treatment plans (ATP). We applied this method to 218 archived nonsmall cell lung cancer lung treatment plans that were originally calculated either without heterogeneity corrections or with primitive corrections. To retrospectively specify beam weights and wedges, beams were broken into Monte Carlo-generated beamlets, simulated using the VMC++ code, and mathematical optimization was used to match the archived water-based dose distributions. The derived beam weights (and any wedge effects) were then applied to Monte Carlo beamlets regenerated based on the patient computed tomography densities. Validation of the process was performed against five comparable lung treatment plans generated using a commercial convolution/superposition implementation. For the application here (normal lung, esophagus, and planning target volume dose distributions), the agreement was very good. Resulting MC and convolution/superposition values were similar when dose distributions without heterogeneity corrections or dose distributions with corrections were compared. When applied to the archived plans (218), the average absolute percent difference between water-based MC and water-based ATPs, for doses above 2.5% of the maximum dose was 1.8+/-0.6%. The average absolute percent difference between heterogeneity-corrected MC and water-based ATPs increased to 3.1+/-0.9%. The average absolute percent difference between the MC heterogeneity-corrected and the ATP heterogeneity-corrected dose distributions was 3.8+/-1.6% (available in 132/218 archives). The entire dose-volume-histograms for lung, tumor, and esophagus from the different calculation methods, as well as specific dose metrics, were compared. The average difference in maximum lung dose between water-based ATPs and heterogeneity-corrected MC dose distributions was -1.0+/-2.1 Gy. Potential errors in relying on primitive heterogeneity corrections are most evident from a comparison of maximum lung doses, for which the average MC heterogeneity-corrected values were 5.3+/-2.8 Gy less than the ATP heterogeneity-corrected values. We have demonstrated that recalculation of archived dose distributions, without explicit information about beam weights or wedges, is feasible using beamlet-based optimization methods. The method provides heterogeneity-corrected dose data consistent with convolution-superposition calculations and is one feasible approach for improving dosimetric data for outcomes analyses.