Population-based Model Research Articles

The societal economic value of COVID-19 vaccines in the United States

Aims The COVID-19 pandemic has claimed the lives of more than 800,000 people in the United States (US) and has been estimated to carry a societal cost of $16 trillion over the next decade. The availability of COVID-19 vaccines has had a profound effect on the trajectory of the pandemic, with wide-ranging benefits. We aimed to estimate the total societal economic value generated in the US from COVID-19 vaccines. Methods We developed a population-based economic model informed by existing data and literature to estimate the total societal value generated from COVID-19 vaccines by avoiding COVID-19 infections as well as resuming social and economic activity more quickly. To do this, we separately estimated the value generated from life years saved, healthcare costs avoided, quality of life gained, and US gross domestic product (GDP) gained under a range of plausible assumptions. Results Findings from our base case analysis suggest that from their launch in December 2020, COVID-19 vaccines were projected to generate $5.0 trillion in societal economic value for the US from avoided COVID-19 infections and resuming unrestricted social and economic activity more quickly. Our scenario analyses suggest that the value could range between $1.8 and $9.9 trillion. Our model indicates that the most substantial sources of value are derived from reduction in prevalence of depression ($1.9 trillion), gains to US GDP ($1.4 trillion), and lives saved from fewer COVID-19 infections ($1.0 trillion). Limitations Constructed as a projection from December 2020, our model does not account for the Delta or future variants, nor does it account for improvements in COVID-19 treatment. Conclusions The magnitude of economic benefit from vaccination highlights the need for coordinated policy decisions to support continued widespread vaccine uptake in the US.

The non-fatal burden of cancer in Belgium, 2004\u20132019: a nationwide registry-based study

BackgroundThe importance of assessing and monitoring the health status of a population has grown in the last decades. Consistent and high quality data on the morbidity and mortality impact of a disease represent the key element for this assessment. Being increasingly used in global and national burden of diseases (BoD) studies, the Disability-Adjusted Life Year (DALY) is an indicator that combines healthy life years lost due to living with disease (Years Lived with Disability; YLD) and due to dying prematurely (Years of Life Lost; YLL). As a step towards a comprehensive national burden of disease study, this study aims to estimate the non-fatal burden of cancer in Belgium using national data.MethodsWe estimated the Belgian cancer burden from 2004 to 2019 in terms of YLD, using national population-based cancer registry data and international disease models. We developed a microsimulation model to translate incidence- into prevalence-based estimates, and used expert elicitation to integrate the long-term impact of increased disability due to surgical treatment.ResultsThe age-standardized non-fatal burden of cancer increased from 2004 to 2019 by 6 and 3% respectively for incidence- and prevalence-based YLDs. In 2019, in Belgium, breast cancer had the highest morbidity impact among women, followed by colorectal and non-melanoma skin cancer. Among men, prostate cancer had the highest morbidity impact, followed by colorectal and non-melanoma skin cancer. Between 2004 and 2019, non-melanoma skin cancer significantly increased for both sexes in terms of age-standardized incidence-based YLD per 100,000, from 49 to 111 for men and from 15 to 44 for women. Important decreases were seen for colorectal cancer for both sexes in terms of age-standardized incidence-based YLD per 100,000, from 105 to 84 for men and from 66 to 58 for women.ConclusionsBreast and prostate cancers represent the greatest proportion of cancer morbidity, while for both sexes the morbidity burden of skin cancer has shown an important increase from 2004 onwards. Integrating the current study in the Belgian national burden of disease study will allow monitoring of the burden of cancer over time, highlighting new trends and assessing the impact of public health policies.

Identifying optimal candidates for tumor resection among borderline and locally advanced pancreatic cancer: A population-based predictive model

BackgroundWhether patients with borderline resectable and locally advanced pancreatic cancer (BR/LAPC) benefit from resection of the primary cancer is controversial. We developed a nomogram to screen who would benefit from surgery for the primary tumor. MethodsWe identified patients from the Surveillance, Epidemiology, and End Results (SEER) database and then divided them into surgical and non-surgical groups. A 1:1 propensity score matching (PSM) was used to mitigate the bias. We hypothesized that patients who underwent surgery would benefit from surgery by having a longer median overall survival (OS) than patients who did not undergo surgery. Univariate and multivariate logistic regression analyses were used to determine the variables affecting surgical outcomes, and a nomogram was created based on the multivariate logistic results. Finally, we verified the discrimination and calibration of the nomogram with receiver operator characteristic (ROC) curve and calibration plots. ResultsA total of 518 pairs of surgical and non-surgical pancreatic cancer patients were matched after PSM. Survival curves showed longer OS in the surgical group than in the non-surgical group, median survival times were 14 months versus 8 months. In the surgical group, 340 (65.63%) patients have a longer survival time than 8 months (beneficial group). Multifactorial logit regression results showed that including age, tumor size, degree of differentiation, and chemotherapy were significant influences on the benefit of surgery for primary tumors and were used as predictors to construct a nomogram. The area under the ROC curve (AUC) reached 0.747 and 0.706 in the training and validation sets. ConclusionWe developed a practical predictive model to support clinical decision-making that can be used to help clinicians determine if there is a benefit to surgical resection of the primary tumor in patients with BR/LAPC.

ACO Investment Model Produced Savings, But The Majority Of Participants Exited When Faced With Downside Risk.

Medicare's Accountable Care Organization (ACO) Investment Model (AIM) provided up-front funding to forty-one small, rurally located ACOs to encourage their participation in the Medicare Shared Savings Program. We estimate net savings to Medicare of $381.5 million over three years, driven by utilization reductions in inpatient and other institutional care and by the absence of shared risk for potential increases in Medicare spending incurred by participants. These savings suggest that population-based payment models can enable providers to better meet the needs of rural populations through greater flexibility in care delivery. However, nearly two-thirds of AIM ACOs exited the Medicare Shared Savings Program when faced with the requirement to assume downside financial risk, starting in year four of participation. As the Centers for Medicare and Medicaid Services builds on AIM and rural hospital global payment models, our findings suggest that new payment models can support more efficient use of resources to meet the health care needs of rural populations. However, the findings also caution against the vigorous pursuit of savings as a primary goal of payment models in traditionally underserved communities.

An Improved Artificial Bee Colony Algorithm for Model-Free Active Noise Control: Algorithm and Implementation

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this paper, artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate and improve the noise reduction performance. Compared with some closely related population-based model free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

Four-Dimensional Computed Tomography-Based Correlation of Respiratory Motion of Lung Tumors With Implanted Fiducials and an External Surrogate

PurposeOur purpose was to assess the suitability of airway-implanted internal fiducial markers and an external surrogate of respiratory motion for motion management during radiation therapy of lung tumors. Methods and MaterialsWe analyzed 4-dimensional computed tomography scans acquired during radiation therapy simulation for 28 patients with lung tumors who had anchored fiducial markers bronchoscopically implanted inside small airways in or near the tumor in a prospective trial. We used a linear mixed model to build population-based correlative models of tumor and surrogate motion. The first 24 of the 28 patients were used to build correlative models, and 4 of the 28 consecutive patients were excluded and used as an internal validation cohort. Of the 24 patients from the model building cohort, all were used for the models based on the internal fiducial. The external surrogate was completely visualized in 11 patients from the model building cohort, so only those were used for the models based on the external surrogate. Furthermore, we determined the predicted residual error sum of squares for our correlative models, which may serve as benchmarks for future research. ResultsThe motion of the internal fiducials was significantly associated with the tumor motion in the anterior-posterior (P < .0001) and superior-inferior (SI) directions (P < .0001). We also observed a strong correlation of the external surrogate anterior-posterior motion to the tumor dominant SI motion (P < .0001). In the validation cohort, the internal fiducial SI motion was the only reliable predictor of lung tumor motion. ConclusionsThe internal fiducials appear to be more reliable predictors of lung tumor motion than the external surrogate. The suitability of such airway-implanted internal fiducial markers for advanced motion management techniques should be further investigated. Although the external surrogate seems to be less reliable, its wide availability and noninvasive application support its clinical utility, albeit the greater uncertainty will need to be compensated for.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2.

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2—the receptor of SARS-CoV-2 in human cells—and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8–3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

A population-based method to determine the time-integrated activity in molecular radiotherapy

BackgroundThe calculation of time-integrated activities (TIAs) for tumours and organs is required for dosimetry in molecular radiotherapy. The accuracy of the calculated TIAs is highly dependent on the chosen fit function. Selection of an adequate function is therefore of high importance. However, model (i.e. function) selection works more accurately when more biokinetic data are available than are usually obtained in a single patient. In this retrospective analysis, we therefore developed a method for population-based model selection that can be used for the determination of individual time-integrated activities (TIAs). The method is demonstrated at an example of [177Lu]Lu-PSMA-I&T kidneys biokinetics. It is based on population fitting and is specifically advantageous for cases with a low number of available biokinetic data per patient.MethodsRenal biokinetics of [177Lu]Lu-PSMA-I&T from thirteen patients with metastatic castration-resistant prostate cancer acquired by planar imaging were used. Twenty exponential functions were derived from various parameterizations of mono- and bi-exponential functions. The parameters of the functions were fitted (with different combinations of shared and individual parameters) to the biokinetic data of all patients. The goodness of fits were assumed as acceptable based on visual inspection of the fitted curves and coefficients of variation CVs < 50%. The Akaike weight (based on the corrected Akaike Information Criterion) was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit.ResultsThe function A_{1} { }beta { }e^{{ - left( {lambda_{1} + lambda_{{{text{phys}}}} } right)t}} + A_{1} { }left( {1 - beta } right){ }e^{{ - left( {lambda_{{{text{phys}}}} } right)t}} with shared parameter beta was selected as the function most supported by the data with an Akaike weight of 97%. Parameters A_{1} and lambda_{1} were fitted individually for every patient while parameter beta { } was fitted as a shared parameter in the population yielding a value of 0.9632 ± 0.0037.ConclusionsThe presented population-based model selection allows for a higher number of parameters of investigated fit functions which leads to better fits. It also reduces the uncertainty of the obtained Akaike weights and the selected best fit function based on them. The use of the population-determined shared parameter for future patients allows the fitting of more appropriate functions also for patients for whom only a low number of individual data are available.

Gastric deformation models for adaptive radiotherapy: Personalized vs population-based strategy

Background and purposeTo create a library of plans (LoP) for gastric cancer adaptive radiotherapy, accurate predictions of shape changes due to filling variations are essential. The ability of two strategies (personalized and population-based) to predict stomach shape based on filling was evaluated for volunteer and patient data to explore the potential for use in a LoP. Materials and methodsFor 19 healthy volunteers, stomachs were delineated on MRIs with empty (ES), half-full (HFS) and full stomach (FS). For the personalized strategy, a deformation vector field from HFS to corresponding ES was acquired and extrapolated to predict FS. For the population-based strategy, the average deformation vectors from HFS to FS of 18 volunteers were applied to the HFS of the remaining volunteer to predict FS (leave-one-out principle); thus, predictions were made for each volunteer. Reversed processes were performed to predict ES. To validate, for seven gastric cancer patients, the volunteer population-based model was applied to their pre-treatment CT to predict stomach shape on 2–3 repeat CTs. For all predictions, volume was made equal to true stomach volume. ResultsFS predictions were satisfactory, with median Dice similarity coefficient (mDSC) of 0.91 (population-based) and 0.89 (personalized). ES predictions were poorer: mDSC = 0.82 for population-based; personalized strategy yielded unachievable volumes. Population-based shape predictions (both ES and FS) were comparable between patients (mDSC = 0.87) and volunteers (0.88). ConclusionThe population-based model outperformed the personalized model and demonstrated its ability in predicting filling-dependent stomach shape changes and, therefore, its potential for use in a gastric cancer LoP.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart’s left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m^{2}). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, − 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from − 2946 to − 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

The merits of administrative benchmarks for population-based payment programs.

The different approaches to setting benchmarks for population-based payment models (empirical, bidding based, and administratively set) have unique advantages and challenges.

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer.

Purpose: To test a short 2-[18F]Fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET dynamic acquisition protocol to calculate Ki using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).Methods: 24 patients with NSCLC who underwent standard dynamic 2-[18F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), Ki was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain Ki,s using IDIFFitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIFFitted curves were modeled to assume the value of 2-[18F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between Ki and Ki,s. Finally, Ki,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min−1. The LOOCV error was 0.4%. In patients of group 2, the mean values of Ki and Ki,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R2 = 0.9970). The Passing–Bablok regression for comparison between Ki and Ki,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[18F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional Ki estimation.

Graph, Spectra, Control and Epidemics: An Example with a SEIR Model

Networks and graphs offer a suitable and powerful framework for studying the spread of infection in human and animal populations. In the case of a heterogeneous population, the social contact network has a pivotal role in the analysis of directly transmitted infectious diseases. The literature presents several works where network-based models encompass realistic features (such as contacts networks or host–pathogen biological data), but analytical results are nonetheless scarce. As a significant example, in this paper, we develop a multi-group version of the epidemiological SEIR population-based model. Each group can represent a social subpopulation with the same habits or a group of geographically localized people. We consider also heterogeneity in the weighting of contacts between two groups. As a simple application, we propose a simple control algorithm in which we optimize the connection weights in order to minimize the combination between an economic cost and a social cost. Some numerical simulations are also provided.

Prognostic value of clinicians' predictions of neonatal outcomes in counseling at the margin of gestational viability

Within antenatal counseling sessions at the margin of gestational viability, clinicians frequently to use population-based outcome data and statistical models to guide the decision-making process. These tools often utilize non-modifiable prenatal factors to estimate outcomes based on population averages. However, most parents prefer individualized predictions, which cannot be supported by these models. Additionally, prognostic accuracy is limited by institutional practices surrounding active management of infants at the margin of viability. Throughout the literature, parental perspectives emphasize the importance of communicating subjective information, such as providing hope and supporting personal values, over the importance of accurate prognostic information from the clinician. In this review we aim to describe the value of clinician prognoses in the decision-making process at the margin of gestational viability and emphasize the importance of addressing parental values during the counseling process, regardless of the expected outcome.

Chronic Lymphocytic Leukemia (CLL) Clonal Growth Rate Is Slower Following Venetoclax-Rituximab (VenR): Results from a Minimal Residual Disease (MRD) Model from the Randomized Phase 3 Murano Trial

Chronic Lymphocytic Leukemia (CLL) Clonal Growth Rate Is Slower Following Venetoclax-Rituximab (VenR): Results from a Minimal Residual Disease (MRD) Model from the Randomized Phase 3 Murano Trial

Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study

ObjectivesTo develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of...

Minimal Residual Disease Dynamics after Venetoclax-Obinutuzumab Treatment: Extended Off-Treatment Follow-up From the Randomized CLL14 Study.

The CLL14 study has established one-year fixed-duration treatment of venetoclax and obinutuzumab (Ven-Obi) for patients with previously untreated chronic lymphocytic leukemia. With all patients off treatment for at least three years, we report a detailed analysis of minimal residual disease (MRD) kinetics and long-term outcome of patients treated in the CLL14 study. Patients were randomly assigned to receive six cycles of obinutuzumab with 12 cycles of venetoclax or 12 cycles of chlorambucil (Clb-Obi). Progression-free survival (PFS) was the primary end point. Key secondary end points included rates of undetectable MRD and overall survival. To analyze MRD kinetics, a population-based growth model with nonlinear mixed effects approach was developed. Of 432 patients, 216 were assigned to Ven-Obi and 216 to Clb-Obi. Three months after treatment completion, 40% of patients in the Ven-Obi arm (7% in the Clb-Obi arm) had undetectable MRD levels < 10-6 by next-generation sequencing in peripheral blood. Median MRD doubling time was longer after Ven-Obi than Clb-Obi therapy (median 80 v 69 days). At a median follow-up of 52.4 months, a sustained significant PFS improvement was observed in the Ven-Obi arm compared with Clb-Obi (median not reached v 36.4 months; hazard ratio 0.33; 95% CI, 0.25 to 0.45; P < .0001). The estimated 4-year PFS rate was 74.0% in the Ven-Obi and 35.4% in the Clb-Obi arm. No difference in overall survival was observed (hazard ratio 0.85; 95% CI, 0.54 to 1.35; P = .49). No new safety signals occurred. Appearance of MRD after Ven-Obi is significantly slower than that after Clb-Obi with more effective MRD reduction. These findings translate into a superior long-term efficacy with the majority of Ven-Obi-treated patients remaining in remission.

The cost-effectiveness of a school-based intervention for bullying prevention: An Australian case study

BackgroundBullying has been recognised as a leading risk factor for mental disorders. The current study aims to evaluate the population cost-effectiveness of the Friendly Schools program (FSP), a school-based intervention for bullying prevention in Australia. MethodA population-based Markov model which simulated how the population cohort moves between two health states (being bullied and not bullied) over 10 years was developed to estimate the cost per quality-adjusted life year gained and return on investment (ROI) ratio of the FSP intervention relative to no intervention and current practice. The target population comprised primary school students in grades 4 and 6. Transition probabilities were sourced from trial data which evaluated FSP. The cost analysis included intervention costs (from the societal perspective), cost savings associated with reduced health service use and school absence days. ResultsThe current study demonstrated that FSP was cost saving and produced an ROI ratio of 1.56 to 2.22 depending on the presence of bullying strategies currently in schools. The results were robust to changes made in the one-way sensitivity analysis and the probabilistic sensitivity analysis indicated that the intervention was 100% likely to be cost-effective. Successful implementation of the FSP program will involve additional support for school staff to accommodate the responsibilities of intervention delivery alongside their existing workload. ConclusionThis study found that the FSP intervention was cost saving and had a positive ROI. These findings strongly support primary schools to consider implementing FSP. Further evidence would strengthen the economic case for implementation.

Deploying a Single-Energy O-Ring Linac as the Sole Treatment Machine in a Community Setting: A Feasibility Study

A novel O-ring Linac (ORL) offers a fully enclosed gantry with a single energy (6 MV) flattening filter-free photon beam, improved kV CBCT, a maximum treatment length of 36 cm (dual-isocenter setup), and a translation-only 3DOF couch for IGRT. The purpose of this study was to evaluate the viability of this ORL for use in a single-room clinic setting and to assess implementation challenges.Treatment statistics of the single-energy ORL over a 1-year period from our main campus (5-linac lineup) were used to develop a ORL machine utilization model; 2 years of treatment statistics from 3 single-room locations with multi-energy C-arm Linac were analyzed to generate a population-based disease distribution model for a typical single-room center. A percentage frequency distribution of key anatomical sites is summarized in Table 1. The ORL utilization model was then extended to the network disease distribution model to assess the percentage of patients in network that can be treated using the ORL.Table 1 shows the 9 anatomical sites accounting for ∼96% of network cases, all of which have corresponding active treatment protocols using the ORL at our main campus. Some 2D/3D treatments required modifications to planning workflow based on technical limitations of the ORL. Less than 1% of treatments at network locations required field sizes exceeding 36 cm, which is the ORL's limit. Electron treatments constitute ∼10% of network cases, of which 74% were for breast boost treatments. Considering 80% of breast boost treatments at our main campus were delivered on ORLs, over half of network electron cases can be safely treated on an ORL with photons. kV-CBCT is required for all ORL daily treatments. Despite the lack of 6DOF couch, ∼80% of ORL treatment fractions did not require manual repositioning for rotational corrections. Averaged appointment durations were reduced from 20 min on C-Arm Linac to 14 min on ORL.Our model indicated that approximately 95% of the patient population at a single-Linac clinic can be treated using the ORL photon-only system. Streamlined IGRT workflow improved the ORL's overall efficiency compared to C-Arm Linac. Although some modifications to planning workflows are required, the ORL improves overall efficiency and is in general suitable for a single-room clinic.

Small Non-Rigid Variations Can Assist Robust IMPT Plan Selection for Head and Neck Patients

Interfraction anatomical variability increases dosimetric discrepancy during delivery of intensity-modulated proton therapy (IMPT). Small non-rigid variations (sNRVs) such as neck extension, spinal flexion, jaw and shoulder position occur during IMPT for head & neck cancer. We assess dosimetric discrepancies caused by sNRVs and suggest a population-based model to incorporate sNRVs into robust evaluation of IMPT plans.A total of 20 nasopharynx and 15 oropharynx radiotherapy patients were retrospectively analyzed, each with weekly CT scans. Anatomical variations during the first week of treatment are predominately due to sNRV, when progressive changes (weight-loss, tumor shrinkage) are less significant. Using deformable image registration, we obtained deformations between week 0/week 1 of treatment for all 35 patients, creating a distribution of possible sNRVs. The deformations from this distribution are then applied to the planning CT of two patients. Patient 1 has a laterally located gross tumor volume (GTV), patient 2 has a central GTV, both with bilateral low-risk clinical target volume (CTV). For both patients, IMPT plans using 3-field, 4-field, and 5-field beam arrangements were generated (nominal dose) and dose was forward calculated on the 35 deformed CT images representing the possible sNRV dose (n = 210) to assess dose degradation. Dose-volume histogram (DVH) bandwidths for sNRV doses were calculated per patient and beam arrangement to evaluate the robustness of the nominal plans with respect to the sNRVs.Among both patients (210 scenarios), we observe a maximum difference to the nominal dose of: 7% degradation of CTV D95, increase in parotid Dmean 10.6 Gy (RBE), increase in brainstem Dmax 14.8 Gy (RBE), increase in chiasm Dmax 18 Gy (RBE), increase in larynx Dmean15 Gy (RBE). For both patients, the 4-field plan shows the narrowest bandwidth and thus best robustness against sNRV in terms of CTV coverage (tab 1). For patient 1, the 4-field plan appears most robust in terms of organ at risk dose. For patient 2, the 3-field plan shows the best parotid sparing while dose metrics for brainstem and chiasm are similar for all beam arrangements.We demonstrate that sNRVs can substantially degrade the dose to the target volume and increase OAR doses. Including the influence of sNRVs during robust evaluation may assist in plan selection, providing information on clinical uncertainties additional to the conventional rigid isocenter shift and CT number uncertainty.