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Ionizing radiation acoustic imaging (iRAI) allows online monitoring of radiation’s interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We describe an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The method relies on a two-dimensional matrix array transducer and a matching multi-channel preamplifier board. The feasibility of imaging temporal 3D dose accumulation was first validated in a tissue-mimicking phantom. Next, semiquantitative iRAI relative dose measurements were verified in vivo in a rabbit model. Finally, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment.

Purpose Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. Methods and materials A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient. Results PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p

Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients.We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors.The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity.The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

PURPOSE: To develop and implement an efficient and accurate commissioning procedure for small field static beam animal irradiation studies on an MV research linear accelerator (Linatron-M9) using radiochromic gel dosimetry. MATERIALS: The research linear accelerator (Linatron-M9) is a 9 MV linac with a static fixed collimator opening of 5.08 cm diameter. Lead collimators were manually placed to create smaller fields of 2x2 cm(2), 1x1 cm(2) and 0.5x0.5 cm(2). Relative dosimetry measurements were performed, including profiles, percent depth dose (PDD) curves, beam divergence, and relative output factors using various dosimetry tools, including a small volume ionization chamber (A14), GAFCHROMIC™ EBT3 film, and Clearview gel dosimeters. The gel dosimeter was used to provide a 3D volumetric reference of the irradiated fields. The Linatron profiles and relative output factors were extracted at a reference depth of 2 cm with the output factor measured relative to the 2x2 cm(2) reference field. Absolute dosimetry was performed using A-14 ionization chamber measurements, which were verified using a national standards laboratory remote dosimetry service. RESULTS: Absolute dosimetry measurements were confirmed within 1.4% (k = 2, 95% confidence = 5%). The relative output factor of the small fields measured with films and gels agreed with a maximum relative percent error difference between the two methods of 1.1 % for the 1x1 cm(2) field and 4.3 % for the 0.5x0.5 cm(2) field. These relative errors were primarily due to the variability in the collimator positioning. The measured beam profiles demonstrated excellent agreement for beam size (measured as FWHM), within approximately 0.8 mm (or less). Film measurements were more accurate in the penumbra region due to the film’s finer resolution compared with the gel dosimeter. Following the van Dyk criteria, the PDD values of the film and gel measurements agree within 11% in the buildup region starting from 0.5 cm depth and within 2.6 % beyond maximum dose and into the fall-off region for depths up to 5 cm. The 2D beam profile isodose lines agree within 0.5 mm in all regions for the 0.5x0.5 cm(2) and the 1x1 cm(2) fields and within 1 mm for the larger field of 2x2 cm(2). The 2D PDD curves agree within approximately 2% of the maximum in the typical therapy region (1–4 cm) for the 1x1 cm(2) and 2x2 cm(2) and within 5% for the 0.5x0.5 cm(2) field. CONCLUSION: This work provides a commissioning process to measure the beam characteristics of a fixed beam MV accelerator with detailed dosimetric evaluation for its implementation in megavoltage small animal irradiation studies. Radiochromic gel dosimeters are efficient small field relative dosimetry tools providing 3D dose measurements allowing for full representation of dose, dosimeter misalignment corrections and high reproducibility with low inter-dosimeter variability. Overall, radiochromic gels are valuable for fast, full relative dosimetry commissioning in comparison to films for application in high energy small-field animal irradiation studies.

Purpose Previous reports of stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC) suggest unacceptably high rates of toxicity in patients with Child-Turcotte-Pugh (CTP) B liver disease. We hypothesized that an individualized adaptive treatment approach based on midtreatment liver function would maintain good local control while limiting toxicity in this population. Methods and Materials Patients with CTP-B liver disease and HCC were treated on prospective trials of individualized adaptive SBRT between 2006 and 2018. Patients underwent pre- and midtreatment liver function assessments using indocyanine green. Treatment-related toxicity was defined as a ≥2-point increase in CTP score from pretreatment within 6 months of treatment. In addition, we performed analyses with a longitudinal model to assess changes in CTP score over 12 months after SBRT. Results Eighty patients with CTP-B (median tumor size, 2.5 cm) were treated: 37 patients were CTP-B-7, 28 were CTP-B-8, and 15 were CTP-B-9. The median treatment dose was 36 Gy in 3 fractions. One-year local control was 92%. In a multivariate model controlling for tumor size, treatment dose, and baseline CTP score, higher treatment dose was associated with improved freedom from local progression (hazard ratio: 0.97; 95% confidence interval, 0.94-1.00; P = .04). Eighteen patients (24%) had a ≥2-point increase in CTP score within 6 months of SBRT. In a longitudinal model assessing changes in CTP score over 12 months after SBRT, controlling for baseline CTP and tumor size, increasing mean liver dose was associated with larger increases in CTP score (P = .04). Conclusions An individualized adaptive treatment approach allows for acceptable toxicity and effective local control in patients with HCC and CTP-B liver disease. Because increasing dose may increase both local control and toxicity, further work is needed to optimize treatment in patients with compromised liver function.

To study the dosimetric risk factors for radiation-induced proximal bronchial tree (PBT) toxicity in patients treated with radiation therapy for non-small cell lung cancer (NSCLC).Patients with medically inoperable or unresectable NSCLC treated with conventionally fractionated 3-dimensional conformal radiation therapy (3DCRT) in prospective clinical trials were eligible for this study. Proximal bronchial tree (PBT) and PBT wall were contoured consistently per RTOG 1106 OAR-Atlas. The dose-volume histograms (DVHs) of physical prescription dose (DVHp) and biological effective dose (α/β = 2.5; DVH2.5) were generated, respectively. The primary endpoint was PBT toxicities, defined by CTCAE 4.0 under the terminology of bronchial stricture/atelectasis.Of 100 patients enrolled, with a median follow-up of 64 months (95% confidence interval [CI], 50-78), 73% received 70 Gy or greater and 17% developed PBT toxicity (grade 1, 8%; grade 2, 6%; grade 3, 0%; and grade 4, 3%). The median time interval between RT initiation and onset of PBT toxicity was 8.4 months (95% CI, 4.7-44.1). The combined DVHs showed that no patient with a PBT maximum physical dose65 Gy developed any PBT toxicity. Cox proportional hazards analysis and receiver operating characteristic analysis demonstrated that V75 of PBT was the most significant dosimetric parameter for both grade 1+ (P = .035) and grade 2+ (P = .037) PBT toxicities. The dosimetric thresholds for V75 of PBT were 6.8% and 11.9% for grade 1+ and grade 2+ PBT toxicity, respectively.V75 of PBT appeared be the most significant dosimetric parameter for PBT toxicity after conventionally fractionated thoracic 3DCRT. Constraining V75 of PBT can limit clinically significant PBT toxicity.

Medical physics residents (MPRs) will define and shape the future of physics in medicine. We sought to better understand the residency experience, as related to resilience and well-being, through the lens of current MPRs and medical physicists (MPs) working with residents.From February-May 2019, we conducted 32, 1-h, confidential, semi-structured interviews with MPs either currently enrolled in an accredited residency (n = 16) or currently employed by a department with an accredited residency (n = 16). Interviews centered on the topics of mentorship, work/life integration, and discrimination. Qualitative analysis methods were used to derive key themes from the interview transcripts.With regard to the medical physics residency experience, four key themes emerged during qualitative analysis: the demanding nature of medical physics residencies, the negative impacts of residency on MPRs during training and beyond, strategies MPRs use to cope with residency stress, and the role of professional societies in addressing residency-related change.Residency training is a stress-inducing time in the path to becoming a board-certified MP. By uncovering several sources of this stress, we have identified opportunities to support the resiliency and well-being of MPs in training through recommendations by professional societies, programmatic changes, and interventions at the department and residency program director level for residency programs, as well as strategies that MPRs themselves can use to support well-being on their career journey.

PURPOSE: Electron-based ultra-high dose rate radiation therapy (UHDR-RT), also known as Flash-RT, has shown the ability to improve the therapeutic index in comparison to conventional radiotherapy (CONV-RT) through increased sparing of normal tissue. However, the extremely high-dose rates in UHDR-RT have raised the need for accurate real-time dosimetry tools. This work aims to demonstrate the potential of the emerging technology of Ionized Radiation Acoustic Imaging (iRAI) through simulation studies and investigate its characteristics as a promising relative in vivo dosimetric tool for UHDR-RT. METHODS: The detection of induced acoustic waves following a single UHDR pulse of a modified 6 MeV 21EX Varian Clinac in a uniform porcine gelatin phantom that is brain-tissue equivalent was simulated for an ideal ultrasound transducer. The full 3D dose distributions in the phantom for a 1x1 cm(2) field were simulated using EGSnrc (BEAMnrc\DOSXYZnrc) Monte Carlo (MC) codes. The relative dosimetry simulations were verified with dose experimental measurements using Gafchromic films. The spatial dose distribution was converted into an initial pressure source spatial distribution using the medium dependent dose-pressure relation. The MATLAB based toolbox k-Wave was then used to model the propagation of acoustic waves through the phantom and perform time-reversal (TR) based imaging reconstruction. The effect of the various linear accelerator (linac) operating parameters, including linac pulse duration and pulse repetition rate (frequency), were investigated as well. RESULTS: The Monte Carlo dose simulation results agreed with the film measurement results, specifically at the central beam region up to 80% dose within approximately 5% relative error for the central profile region and a local relative error of < 6 % for percentage dose depth. IRAI-based FWHM of the radiation beam was within approximately 3 mm relative to the MC simulated beam FWHM at the beam entrance. The real time pressure signal change agreed with the dose changes proving the capability of the iRAI for predicting the beam position. IRAI was tested through 3D simulations of its response to be based on the temporal changes in the linac operating parameters on a dose per pulse basis as expected theoretically from the pressure-dose proportionality. The pressure signal amplitude obtained through 2D simulations was proportional to the dose per pulse. The instantaneous pressure signal amplitude decreases as the linac pulse duration increases, as predicted from the pressure wave generation equations, such that the shorter the linac pulse the higher the signal and the better the temporal (spatial) resolutions of iRAI. The effect of the longer linac pulse duration on the spatial resolution of the 3D constructed iRAI images was corrected for through linac pulse deconvolution. This correction has improved the passing rate of the 1%/1mm gamma test criteria, between the pressure-constructed and dosimetric beam characteristic, to as high as 98%. CONCLUSIONS: A full simulation workflow was developed for testing the effectiveness of iRAI as a promising relative dosimetry tool for UHDR-RT radiation therapy. IRAI has shown the advantage of 3D dose mapping through the dose signal linearity and hence has the potential to be a useful dosimeter at depth dose measurement and beam localization and hence potentially for in vivo dosimetry in UHDR-RT.

Purpose To generate an understanding of the primary concerns facing medical physicists regarding integration of a demanding technical career with their personal lives. Methods and Materials In 2019, we recruited 32 medical physics residents, faculty, and staff via emails to US medical physics residency program directors to participate in a 1-hour, semistructured interview that elicited their thoughts on several topics, including work–life integration. Standard techniques of qualitative thematic analysis were used to generate the research findings. Results Of the participants, 50% were women and 69% were non-Hispanic White individuals, with a mean (SD) age of 37.5 (7.4) years. They were evenly split between residents and faculty or staff. Participant responses centered around 5 primary themes: the gendered distribution of household responsibilities, the effect of career or work on home and family life, the effect of family on career or work, support and strategies for reconciling work–life conflicts, and the role of professional societies in addressing work–life integration. Participants expressed concern about the effect of heavy workloads on home life, with female respondents more likely to report carrying the majority of the household burden. Conclusions Medical physicists experience challenges in managing work–life conflict amid a diverse array of personal and professional responsibilities. Further investigations are needed to quantitatively assess the division of work and household labor by gender in medical physics, particularly after the outbreak of the COVID-19 pandemic, but this study's qualitative findings suggest that the profession should consider ways to address root causes of work–life conflict to promote the future success and well-being of all medical physicists, and perhaps women in particular.

The purpose of this study is to demonstrate that a Bayesian network (BN) approach can explore hierarchical biophysical relationships that influence tumor response and predict tumor local control (LC) in non-small-cell lung cancer (NSCLC) patients before and during radiotherapy from a large-scale dataset. Our BN building approach has two steps. First, relevant biophysical predictors influencing LC before and during the treatment are selected through an extended Markov blanket (eMB) method. From this eMB process, the most robust BN structure for LC prediction was found via a wrapper-based approach. Sixty-eight patients with complete feature information were used to identify a full BN model for LC prediction before and during the treatment. Fifty more recent patients with some missing information were reserved for independent testing of the developed pre- and during-therapy BNs. A nested cross-validation (N-CV) was developed to evaluate the performance of the two-step BN approach. An ensemble BN model is generated from the N-CV sampling process to assess its similarity with the corresponding full BN model, and thus evaluate the sensitivity of our BN approach. Our results show that the proposed BN development approach is a stable and robust approach to identify hierarchical relationships among biophysical features for LC prediction. Furthermore, BN predictions can be improved by incorporating during treatment information.

Purpose/objective(s) To develop improved NTCP models of liver function using pre-/during-treatment DCE-MRI for adaptation of radiation therapy (RT) in hepatocellular cancer (HCC) patients who receive SBRT. The liver function map based on Gadoxetate uptake rate (k1) is compared to portal venous perfusion for HCC NTCP models. Materials/methods 194 HCC patients who received SBRT were evaluated. Subcohorts of patients (n = 23, each) with either portal venous perfusion or Gadoxetate uptake rate k1 liver function maps calculated from DCE MRI using respective Gd-DTPA or Gadoxetic-Acid were analyzed. In addition, patients with pre-RT Child-Pugh (C-P) score > 8 for the Gadoxetate map subgroup were excluded and the remaining patients (n = 18) analyzed to explore the effect of pre-RT liver condition to the models. Median prescription doses of 50 Gy were delivered in 3 fractions or 5 fractions (split course with a 1-month break). Physical doses were converted into EQD2 for analysis. A logistic dose-response model was used to estimate the fraction of liver functional loss (change of k1 between pre-/during-RT) as a function of subregional liver dose. NTCP was estimated using the cumulative functional reserve model for change in C-P scores (2-point change in the score). Model parameters were calculated using maximum-likelihood estimations. The logistic local dose-response model coupled with the cumulative functional reserve NTCP model were compared between k1 and perfusion portal venous liver function maps. Results In the logistic model, dose that causes 50% reduction of function is much lower for k1 map, D50 of 10.2 Gy (all C-P) /14.8 Gy (pre-RT C-P Conclusion Functional maps showed steeper and lower dose for 50% complication compared with previous perfusion results. Considering its superiority to perfusion maps in representing liver "function", this may indicate a lower limit for normal liver dose or better dose distribution to preserve more liver function. The preliminary findings will be validated in a larger and independent cohort.

Purpose/Objective(s) Head and neck cancer (HNC) RT typically results in high doses to organs-at-risk (OARs), contributing to decreased quality-of-life (QOL) seen in patient reported outcomes (PRO). Current RT planning methods are driven by singular dosimetric trade-offs that may be inconsistent with improving a patient's overall predicted total toxicity burden (TTB). We developed an aggregate toxicity assessment over time including PROs that represents a paradigm shift over informal summary of individual toxicity. Here we introduce a treatment planning approach to directly optimize and reduce the predicted TTB-PRO of a patient's RT plan. Materials/Methods We implemented a prioritized approach to TTB optimization using an in-house optimization plugin linked to a commercial treatment planning system. Prioritized TTB (P-TTB) plans were optimized using a two-stage hierarchal method for inverse IMRT planning. In the first stage, TTB, defined as the weighted-sum of NTCPs, is minimized subject to high-priority OAR dose limits, target prescription, and target coverage requirements. Weights for the toxicities directly represent the relative undesirability of toxicities. In the second stage, a weighted-sum dose-metric objective is minimized, similar to current clinical practice. This stage is subject to the same constraints as the first stage, but an additional constraint for TTB is incorporated based on the optimal solution of the first stage. Using this approach, we retrospectively generated plans for 5 HNC patients and compared the resulting plans to IMRT plans generated without the added TTB constraint. Both plans consisted of 9 equally spaced beams with the same OAR dose limits and dose-metric objective. Target dose requirements were based on simultaneous integrated boost prescriptions of 70 and 56 Gy delivered in 35 fractions. TTB was calculated using previously published NTCP models for dysphagia and xerostomia. Dysphagia was based on increase in aspiration or HNQOL score > 81 as a function of pharyngeal constrictor (PC) mean dose. Xerostomia was based on parotid flow ratio 73 as a function of parotid gland (PG) mean dose. Results Our optimization engine successfully generated P-TTB plans for all five patients while maintaining similar target coverage to the standard dosimetrically optimized plans. P-TTB plans resulted in average absolute NTCP reductions of 15.5% (range: 0.2 - 53.4) for dysphagia and 20.5% (1.9 - 68.5) for xerostomia. These changes correlate to average mean dose decreases of 6.9 Gy (0.2 - 22.7) for PC and 10.2 Gy (1.2 - 33.5) for PGs. P-TTB planning resulted in greater fluence modulation and higher doses to OARs not included in the calculation of TTB. Conclusion The P-TTB planning method offers an intuitive optimization approach to balancing trade-offs between various treatment outcomes and has the ability to directly reduce and redistribute predicted toxicity which may aid in improving QOL for HNC patients receiving RT.

PURPOSE: Most existing computed tomography (CT)-ventilation imaging techniques are based on deformable image registration (DIR) of different respiratory phases of a four-dimensonal CT (4DCT) scan of the lung, followed by the quantification of local breathing-induced changes in Hounsfield Units (HU) or volume. To date, only moderate correlations have been reported between these CT-ventilation metrics and standard ventilation imaging modalities for adaptive lung radiation therapy. This study evaluates the use of stress maps derived from biomechanical model-based DIR as an alternative CT-ventilation metric. MATERIALS AND METHODS: Six patients treated for lung cancer with conventional radiation therapy were retrospectively analyzed. For each patient, a 4DCT scan and Tc-99m SPECT-V image acquired for treatment planning were collected. Biomechanical model-based DIR was applied between the inhale and exhale phase of the 4DCT scans and stress maps were calculated. The voxel-wise correlation between the reference SPECT-V image and map of the maximum principal stress was measured with a Spearman correlation coefficient. The overlap between high (above the 75th percentile) and low (below the 25th percentile) functioning volumes extracted from the reference SPECT-V and the stress maps was measured with Dice similarity coefficients (DSC). The results were compared to those obtained when using two classical CT-ventilation metrics: the change in HU and Jacobian determinant. RESULTS: The mean Spearman correlation coefficients were: 0.37 ± 18 and 0.39 ± 13 and 0.59 ± 0.13 considering the changes in HU, Jacobian and maximum principal stress, respectively. The corresponding mean DSC coefficients were 0.38 ± 0.09, 0.37 ± 0.07 and 0.52 ± 0.07 for the high ventilation function volumes and 0.48 ± 0.13, 0.44 ± 0.09 and 0.52 ± 0.07 for the low ventilation function volumes. CONCLUSION: For presenting a significantly stronger and more consistent correlation with standard SPECT-V images than previously proposed CT-ventilation metrics, stress maps derived with the proposed method appear to be a promising tool for incorporation into functional lung avoidance strategies.

Purpose Due to a gap in published guidance, we describe our robust cycle of in-house clinical software development and implementation, which has been used for years to facilitate the safe treatment of all patients in our clinics. Methods and Materials Our software development and implementation cycle requires clarity in communication, clearly defined roles, thorough commissioning, and regular feedback. Cycle phases include design requirements and use cases, development, physics evaluation testing, clinical evaluation testing, and full clinical release. Software requirements, release notes, test suites, and a commissioning report are created and independently reviewed before clinical use. Software deemed to be high-risk, such as those that are writable to a database, incorporate the use of a formal, team-based hazard analysis. Incident learning is used to both guide initial development and improvements as well as to monitor the safe use of the software. Results Our standard process builds in transparency and establishes high expectations in the development and use of custom software to support patient care. Since moving to a commercial planning system platform in 2013, we have applied our team-based software release process to 16 programs related to scripting in the treatment planning system for the clinic. Conclusions The principles and methodology described here can be implemented in a range of practice settings regardless of whether or not dedicated resources are available for software development. In addition to teamwork with defined roles, documentation, and use of incident learning, we strongly recommend having a written policy on the process, using phased testing, and incorporating independent oversight and approval before use for patient care. This rigorous process ensures continuous monitoring for and mitigatation of any high risk hazards.

Purpose Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning. Methods A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance. Results Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively. Conclusions MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.

PURPOSE: The purpose of this study is to identify dosimetric variables that best predict for acute esophagitis in patients treated for locally advanced non–small cell lung cancer in a prospectively accrued statewide consortium. METHODS AND MATERIALS: Patients receiving definitive radiation therapy for stage II-III non–small cell lung cancer within the Michigan Radiation Oncology Quality Consortium were included in the analysis. Dose-volume histogram data were analyzed to determine absolute volumes (cc) receiving doses from 10 to 60 Gy (V10, V20, V30, V40, V50, and V60), as well as maximum dose to 2 cc (D2cc), mean dose (MD), and generalized equivalent uniform dose (gEUD). Logistic regression models were used to characterize the risk of toxicity as a function of dose and other covariatcs. The ability of each variable to predict esophagitis, individually or in a multivariate model, was quantified by receiver operating characteristic analysis. RESULTS: There were 533 patients who met study criteria and were included; 437 (81.9%) developed any grade of esophagitis. Significant variables on univariate analysis for grade ≥2 esophagitis were concurrent chemotherapy, V20, V30, V40, V50, V60, MD, D2cc, and gEUD. For grade ≥3 esophagitis, the predictive variables were: V30, V40, V50, V60, MD, D2cc, and gEUD. In multivariable modeling, gEUD was the most significant predictor of both grade ≥2 and grade ≥3 esophagitis. When gEUD was excluded from the model, D2cc was selected as the most predictive variable for grade ≥3 esophagitis. For an estimated risk of grade ≥3 esophagitis of 5%, the threshold values for gEUD and D2cc were 59.3 Gy and 68 Gy, respectively. CONCLUSIONS: In this study, we report the novel finding that gEUD and D2cc, rather than MD, were the most predictive dose metrics for severe esophagitis. To limit the estimated risk of grade ≥3 esophagitis to

Purpose To use knowledge-based planning (KBP) as a method of producing high-quality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG) 0631. Methods and Materials An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues. Results The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3% ± 3.2% to 98.3% ± 1.4% (P = .01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cm3 for manual plans to 1.8 ± 0.6 cm3 with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledge-based planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target. Conclusion Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard.

A substantial barrier to the single-and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, doseevolume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials. (C) 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background and purpose To study whether cytokine markers may improve predictive accuracy of radiation esophagitis (RE) in non-small cell lung cancer (NSCLC) patients. Materials and methods A total of 129 patients with stage I-III NSCLC treated with radiotherapy (RT) from prospective studies were included. Thirty inflammatory cytokines were measured in platelet-poor plasma samples. Logistic regression was performed to evaluate the risk factors of RE. Stepwise Akaike information criterion (AIC) and likelihood ratio test were used to assess model predictions. Results Forty-nine of 129 patients (38.0%) developed grade ≥2 RE. Univariate analysis showed that age, stage, concurrent chemotherapy, and eight dosimetric parameters were significantly associated with grade ≥2 RE (p < 0.05). IL-4, IL-5, IL-8, IL-13, IL-15, IL-1α, TGFα and eotaxin were also associated with grade ≥2 RE (p

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non–small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.

Purpose To develop normal tissue complications (NTCP) models for hepatocellular cancer (HCC) patients who undergo liver radiation therapy (RT) and to evaluate the potential role of functional imaging and measurement of blood-based circulating biological markers before and during RT to improve the performance of these models. Methods and Materials The data from 192 HCC patients who had undergone RT from 2005 to 2014 were evaluated. Of the 192 patients, 146 had received stereotactic body RT (SBRT) and 46 had received conventional RT to a median physical tumor dose of 49.8 Gy and 50.4 Gy, respectively. The physical doses were converted into 2-Gy equivalents for analysis. Two approaches were investigated for modeling NTCP: (1) a generalized Lyman-Kutcher-Burman model; and (2) a generalization of the parallel architecture model. Three clinical endpoints were considered: the change in albumin-bilirubin (ALBI), change in Child-Pugh (C-P) score, and grade ≥3 liver enzymatic changes. Local dynamic contrast-enhanced magnetic resonance imaging portal venous perfusion information was used as an imaging biomarker for local liver function. Four candidate inflammatory cytokines were considered as biological markers. The imaging findings and cytokine levels were incorporated into NTCP modeling, and their role was evaluated using goodness-of-fit metrics. Results Using dosimetric information only, the Lyman-Kutcher-Burman model for the ALBI/C-P change had a steeper response curve compared with grade ≥3 enzymatic changes. Incorporating portal venous perfusion imaging information into the parallel architecture model to represent functional reserve resulted in relatively steeper dose-response curves compared with dose-only models. A larger loss of perfusion function was needed for enzymatic changes compared with ALBI/C-P changes. Increased transforming growth factor-β1 and eotaxin expression increased the trend of expected risk in both NTCP modeling approaches but did not reach statistical significance. Conclusions The incorporation of imaging findings and biological markers into NTCP modeling of liver toxicity improved the estimates of expected NTCP risk compared with using dose-only models. In addition, such generalized NTCP models should contribute to a better understanding of the normal tissue response in HCC SBRT patients and facilitate personalized treatment.

Objective. Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on pre-learned patterns from a well-defined patient population. Approach. Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new joint framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance characteristics of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores. Main results. The MRNN framework had slightly better performance than the QRNN framework, with MRNN (QRNN) validation AUC scores of 0.742 ± 0.021 (0.675 ± 0.036), 0.709 ± 0.026 (0.656 ± 0.021), 0.724 ± 0.036 (0.652 ± 0.044), and 0.698 ± 0.016 (0.605 ± 0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, only the results from the two higher order states had statistically significant differences ( p < 0.05 ) . A similar trend was also observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN (QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively. Significance. These results suggest that both stochastic models have potential value in predicting patient changes during the course of adaptive radiotherapy.

Purpose/objective(s) Cardiac radiation exposure is associated with an increased rate of adverse cardiac events in patients receiving radiation therapy for locally advanced non-small cell lung carcinoma (NSCLC). Previous analysis of practice patterns within the statewide Michigan Radiation Oncology Quality Consortium (MROQC) revealed 1 in 4 patients received a mean heart dose > 20 Gy and significant heterogeneity existed among treatment centers in using cardiac dose constraints. The purpose of this study is to analyze the effect of education and initiation of standardized cardiac dose constraints on heart dose across a statewide consortium. Materials/methods From 2012 to 2020, 1604 patients from 27 academic and community centers who received radiation therapy for locally advanced NSCLC were included in this analysis. Dosimetric endpoints including mean heart dose (MHD), mean lung dose, and mean esophagus dose were calculated using data from dose-volume histograms. These dose metrics were grouped by year of treatment initiation for all patients. Education regarding data for cardiac dose constraints was discussed in small lung cancer working group meetings and consortium-wide starting in 2016. This was followed in 2018 by implementation of a quality metric requiring mean heart dose Results Following education and implementation of the heart dose performance metric, mean MHD declined from an average of 12.2 Gy pre-intervention to 10.4 Gy post-intervention, and the percentage of patients receiving MHD > 20 Gy reduced by half. (Table). Mean lung dose and mean esophagus dose did not increase, and tumor coverage remained unchanged. Conclusion Education and implementation of a standardized cardiac dose quality measure across a statewide consortium was associated with a reduction of mean heart dose in patients receiving radiation therapy for locally advanced NSCLC. These dose reductions were achieved without sacrificing tumor coverage, increasing mean lung dose or mean esophagus dose. Analysis of the clinical ramifications of the reduction in cardiac doses is ongoing.

Purpose/objective(s) Functional lung avoidance (FLA) radiotherapy (RT) aims to minimize post-RT respiratory function loss. Current FLA approaches spatially map lung function and preferentially avoid dose to high-functioning lung (HFL). However, current FLA methods do not adequately consider the conducting architecture responsible for gas exchange (airways/vasculature). Previously, we showed that excessive toxicity to airways results in airway collapse, leading to functional loss within downstream lung sub-volumes. We proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL. Here, we propose a novel method that combines both FLA and FWAS to minimize post-RT functional loss. Materials/methods We estimated post-RT decline in regional airflow and ventilation as a combined measure of functional loss. High-res breath-hold CTs (BHCTs) and simulation 4DCTs from seven lung SAbR patients were analyzed retrospectively. Three functional avoidance IMRT strategies were compared to conventional IMRT planning: (i) FLA - a 4DCT-based ventilation map was used to delineate the HFL, defined as the functional regions of the lung above the mean intensity in the map. FLA plans were optimized such that mean dose and V13.5 in HFL were as low as reasonably achievable while meeting clinical dose constraints. (ii) FWAS - a virtual bronchoscopy software was used to autosegment 12 airway generations on the BHCT. Each terminal airway was connected to a lung sub-volume and assigned a functional weight based on the relative ventilation contribution of the sub-volume based on the ventilation map. For non-terminal airways, the weight was calculated in terms of the cumulative ventilation supported by corresponding downstream airways. These weights were used in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse. (iii) FLA+FWAS - the two strategies were combined to reduce the dose to both HFL and corresponding upstream airways. Results Overall, FLA+FWAS provided the highest ventilation preservations with an average improvement, compared to conventional plans, of 12%, followed by FWAS (8%), and FLA (3%). In all cases, clinical objectives for planning target volume and organs at risk were met. Generalized estimated equations with identity link and normal distribution and exchangeable correlation matrix showed that all these improvements were statistically significant (P Conclusion These initial results suggest that it is crucial to consider the serial as well as the parallel nature of the lung, being airway sparing likely more critical to minimize post-RT functional loss.

PURPOSE/OBJECTIVE(S) Regional collaborative quality initiatives that incent participation through direct payment and streamlined reimbursement for high-performing sites have been shown to drive improvements in surgical outcomes. Numerous quality measures have been proposed in radiation oncology, and research has identified readily measurable indicators like dose to critical structures that predict for subsequent toxicity in patients. A decade ago, we initiated a voluntary statewide collaboration for quality improvement in radiation oncology and now describe its impact on care delivery. MATERIALS/METHODS Following SQUIRE guidelines, we describe the approach and measures that the program has implemented. To evaluate impact, we describe compliance at baseline and now with active measures among participating practices. RESULTS Since beginning data collection in 2012, radiation oncologists, physicists, data abstractors, and practice administrators from sites in one state (currently numbering 27) have convened thrice yearly. At these meetings, experts have spoken regarding trends within the field and inspired discussions regarding potential targets for quality improvement within the consortium. Blinded data on practices at various sites have also been regularly presented, and the group has iteratively developed new initiatives and consensus-based benchmarks to improve radiation oncology care delivery, patient experiences, and outcomes. An observational dataset with detailed information from over 20,000 patients has been assembled to evaluate quality. Compliance with select measures is described in the table, including use of guideline-concordant hypofractionated radiotherapy, motion management, doses to targets/normal tissues, and consistency in delineating and naming contoured structures (a precondition for quality evaluation). CONCLUSION Although observational analysis cannot fully exclude secular trends, contextual data revealing slow uptake of best practices elsewhere in the US suggests that this initiative has improved the consistency, efficiency, and quality of radiation oncology care in its member practices and may be a model for other regions.

Purpose Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Methods and Materials Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Results Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P

Background and purpose Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. Materials and methods Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4 weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. Results In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were −28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to −27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. Conclusions Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.

Purpose Advanced radiotherapy delivery systems designed for high‐dose, high‐precision treatments often come equipped with high‐definition multi‐leaf collimators (HD‐MLC) aimed at more finely shaping radiation dose to the target. In this work, we study the effect of a high definition MLC on spine stereotactic body radiation therapy (SBRT) treatment plan quality and plan deliverability. Methods and Materials Seventeen spine SBRT cases were planned with VMAT using a standard definition MLC (M120), HD‐MLC, and HD‐MLC with an added objective to reduce monitor units (MU). M120 plans were converted into plans deliverable on an HD‐MLC using in‐house software. Plan quality and plan deliverability as measured by portal dosimetry were compared among the three types of plans. Results Only minor differences were noted in plan quality between the M120 and HD‐MLC plans. Plans generated with the HD‐MLC tended to have better spinal cord sparing (3% reduction in maximum cord dose). HD‐MLC plans on average had 12% more MU and 55% greater modulation complexity as defined by an in‐house metric. HD‐MLC plans also had significantly degraded deliverability. Of the VMAT arcs measured, 94% had lower gamma passing metrics when using the HD‐MLC. Conclusion Modest improvements in plan quality were noted when switching from M120 to HD‐MLC at the expense of significantly less accurate deliverability in some cases.

Purpose To 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 materials The 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. Results DVH 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. Conclusions Statistical 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.

// Jing Zhao 1, 2, * , Regina M. Day 3, * , Jian-Yue Jin 2, 4 , Leslie Quint 5 , Hadyn Williams 6 , Catherine Ferguson 2 , Li Yan 2 , Maurice King 2 , Ahmad Albsheer 2 , Martha Matuszak 7 and Feng-Ming (Spring) Kong 2, 8 1 Department of Oncology, Tongji Hospital, Tongji Medial College, Huazhong University of Science and Technology, Wuhan, Hubei, China 2 Department of Radiation Oncology, Medical College of Georgia, Augusta University, Augusta, GA, USA 3 Department of Pharmacology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA 4 Department of Radiation Oncology, Radiation Physics, Indiana University School of Medicine, Indianapolis, IN, USA 5 Department of Radiology, University of Michigan, Ann Arbor, MI, USA 6 Department of Radiology, Medical College of Georgia, Augusta University, Augusta, GA, USA 7 Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA 8 Department of Radiation Oncology, IU Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA * These authors contributed equally to this work Correspondence to: Feng-Ming (Spring) Kong, email: fskong@iupui.edu Keywords: lung cancer, thoracic radiotherapy, radiation induced pleural effusion, risk factors, overall survival Received: May 07, 2017 Accepted: June 08, 2017 Published: June 29, 2017 ABSTRACT The risk factors and potential practice implications of radiation-induced pleural effusion (RIPE) are undefined. This study examined lung cancer patients treated with thoracic radiation therapy (TRT) having follow-up computed tomography (CT) or 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. Increased volumes of pleural effusion after TRT without evidence of tumor progression was considered RIPE. Parameters of lung dose-volume histogram including percent volumes irradiated with 5-55 Gy (V5-V55) and mean lung dose (MLD) were analyzed by receiver operating characteristic analysis. Clinical and treatment-related risk factors were detected by univariate and multivariate analyses. 175 out of 806 patients receiving TRT with post-treatment imaging were included. 51 patients (24.9%) developed RIPE; 40 had symptomatic RIPE including chest pain (47.1%), cough (23.5%) and dyspnea (35.3%). Female (OR = 0.380, 95% CI: 0.156–0.926, p = 0.033) and Caucasian race (OR = 3.519, 95% CI: 1.327–9.336, p = 0.011) were significantly associated with lower risk of RIPE. Stage and concurrent chemotherapy had borderline significance (OR = 1.665, p = 0.069 and OR = 2.580, p = 0.080, respectively) for RIPE. Patients with RIPE had significantly higher whole lung V5-V40, V50 and MLD. V5 remained as a significant predictive factor for RIPE and symptomatic RIPE ( p = 0.007 and 0.022) after adjusting for race, gender and histology. To include, the incidence of RIPE is notable. Whole lung V5 appeared to be the most significant independent risk factor for symptomatic RIPE.

Introduction Most patients with lung cancer are elderly and poorly represented in randomized clinical trials. They are often undertreated because of concerns about their ability to tolerate aggressive treatment. We tested the hypothesis that elderly patients undergoing definitive lung radiation might tolerate treatment differently than younger patients. Methods A total of 125 patients who underwent definitive lung radiotherapy were identified from a prospective institutional database (University of Michigan cohort). Logistic regression modeling was performed to assess the impact of age on esophagitis grade 2 or higher or grade 2 or higher and pneumonitis grade 3 or higher or grade 2 or higher, with adjustment for esophageal and lung dose, respectively, as well as for chemotherapy utilization, smoking status, and performance status. The analysis was validated in a large cohort of 691 patients from the Michigan Radiation Oncology Quality Consortium registry, an independent statewide prospective database. Results In the University of Michigan cohort, multivariable regression models revealed a significant inverse correlation between age and rate of esophagitis for both toxicity levels, (adjusted OR = 0.93 for both models and 95% confidence intervals of 0.88–0.98 and 0.87–0.99), with areas under the curve of 0.747 and 0.721, respectively, demonstrating good fit. This same association was noted in the Michigan Radiation Oncology Quality Consortium cohort. There was no significant association between age and pneumonitis. Conclusions There is a lower incidence of esophagitis with increasing age even after adjustment for use of chemotherapy. This is a novel finding in thoracic oncology. No age dependence was noted for pulmonary toxicity. The elderly are able to tolerate definitive thoracic radiation well and should be offered this option when clinically warranted.