A utility approach to individualized optimal dose selection using biomarkers

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non‐negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154301/1/bimj2068.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154301/2/bimj2068_am.pd

Prediction of Therapy Tumor-Absorbed Dose Estimates in I-131 Radioimmunotherapy Using Tracer Data Via a Mixed-Model Fit to Time Activity

Abstract Background: For individualized treatment planning in radioimmunotherapy (RIT), correlations must be established between tracer-predicted and therapy-delivered absorbed doses. The focus of this work was to investigate this correlation for tumors. Methods: The study analyzed 57 tumors in 19 follicular lymphoma patients treated with I-131 tositumomab and imaged with SPECT/CT multiple times after tracer and therapy administrations. Instead of the typical least-squares fit to a single tumor's measured time-activity data, estimation was accomplished via a biexponential mixed model in which the curves from multiple subjects were jointly estimated. The tumor-absorbed dose estimates were determined by patient-specific Monte Carlo calculation. Results: The mixed model gave realistic tumor time-activity fits that showed the expected uptake and clearance phases even with noisy data or missing time points. Correlation between tracer and therapy tumor-residence times (r=0.98; p<0.0001) and correlation between tracer-predicted and therapy-delivered mean tumor-absorbed doses (r=0.86; p<0.0001) were very high. The predicted and delivered absorbed doses were within±25% (or within±75 cGy) for 80% of tumors. Conclusions: The mixed-model approach is feasible for fitting tumor time-activity data in RIT treatment planning when individual least-squares fitting is not possible due to inadequate sampling points. The good correlation between predicted and delivered tumor doses demonstrates the potential of using a pretherapy tracer study for tumor dosimetry-based treatment planning in RIT.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98438/1/cbr%2E2011%2E1053.pd

Biological-Effect Modeling of Radioimmunotherapy for Non-Hodgkins Lymphoma: Determination of Model Parameters

Treatment with Tositumomab and 131I tositumomab anti-CD20 radioimmunotherapy (Bexxar) yields a nonradioactive antibody antitumor response (the so-called cold effect) and a radiation response. Numerical parameter determination by least-squares (LS) fitting was implemented for more accurate parameter estimates in equivalent biological-effect calculations. Methods: One hundred thirty-two tumors in 37 patients were followed using five or six SPECT/CT studies per patient, three each (typical) post-tracer (0.2 GBq) and post-therapy (?3 GBq) injections. The SPECT/CT data were used to calculate position- and time-dependent dose rates and antibody concentrations for each tumor. CT-defined tumor volumes were used to track tumor volume changes. Combined biological-effect and cell-clearance models were fit to tumor volume changes. Optimized parameter values determined using LS fitting were compared to previous fitted values that were determined by matching calculated to measured tumor volume changes using visual assessment. Absorbed dose sensitivity (α) and cold-effect sensitivity (?p) parameters were the primary fitted parameters, yielding equivalent biological-effect (E) values. Results: Individual parameter uncertainties were approximately 10% and 30% for α and ?p, respectively. LS versus previously fit parameter values were highly correlated, although the averaged α value decreased and the averaged ?p value increased for the LS fits compared to the previous fits. Correlation of E with 2-month tumor shrinkage data was similar for the two fitting techniques. The LS fitting yielded improved fit quality and likely improved parameter estimation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140326/1/cbr.2012.1467.pd

Stereotactic Body Radiation Therapy for Primary and Metastatic Liver Tumors

AbstractOBJECTIVES: The full potential of stereotactic body radiation therapy (SBRT), in the treatment of unresectable intrahepatic malignancies, has yet to be realized as our experience is still limited. Thus, we evaluated SBRT outcomes for primary and metastatic liver tumors, with the goal of identifying factors that may aid in optimization of therapy. METHODS: From2005 to 2010, 62 patients with 106 primary and metastatic liver tumors were treated with SBRT to a median biologic effective dose (BED) of 100 Gy (42.6-180). The majority of patients received either three (47%) or five fractions (48%). Median gross tumor volume (GTV) was 8.8 cm3 (0.2-222.4). RESULTS: With a median followup of 18 months (0.46-46.8), freedom from local progression (FFLP) was observed in 97 of 106 treated tumors, with 1- and 2-year FFLP rates of 93% and 82%. Median overall survival (OS) for all patients was 25.2 months, with 1- and 2-year OS of 81%and 52%. Neither BED nor GTV significantly predicted for FFLP. Local failure was associated with a higher risk of death [hazard ratio (HR) = 5.1, P = .0007]. One Child-Pugh Class B patient developed radiationinduced liver disease. There were no other significant toxicities. CONCLUSIONS: SBRT provides excellent local control for both primary and metastatic liver lesions with minimal toxicity. Future studies should focus on appropriate selection of patients and on careful assessment of liver function to maximize both the safety and efficacy of treatment

Evaluation of dual energy quantitative CT for determining the spatial distributions of red marrow and bone for dosimetry in internal emitter radiation therapy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135113/1/mp0378.pd

Nitrate Removal Performance of Denitrifying Woodchip Bioreactors in Tropical Climates

In Australia, declining water quality in the Great Barrier Reef (GBR) is a threat to its marine ecosystems and nitrate (NO3−) from sugar cane-dominated agricultural areas in the coastal catchments of North Queensland is a key pollutant of concern. Woodchip bioreactors have been identified as a potential low-cost remediation technology to reduce the NO3− runoff from sugar cane farms. This study aimed to trial different designs of bioreactors (denitrification walls and beds) to quantify their NO3− removal performance in the distinct tropical climates and hydrological regimes that characterize sugarcane farms in North Queensland. One denitrification wall and two denitrification beds were installed to treat groundwater and subsurface tile-drainage water in wet tropics catchments, where sugar cane farming relies only on rainfall for crop growth. Two denitrification beds were installed in the dry tropics to assess their performance in treating irrigation tailwater from sugarcane. All trialled bioreactors were effective at removing NO3−, with the beds exhibiting a higher NO3− removal rate (NRR, from 2.5 to 7.1 g N m−3 d−1) compared to the wall (0.15 g N m−3 d−1). The NRR depended on the influent NO3− concentration, as low influent concentrations triggered NO3− limitation. The highest NRR was observed in a bed installed in the dry tropics, with relatively high and consistent NO3− influent concentrations due to the use of groundwater, with elevated NO3−, for irrigation. This study demonstrates that bioreactors can be a useful edge-of-field technology for reducing NO3− in runoff to the GBR, when sited and designed to maximise NO3− removal performance

Impact of 90Y PET gradient-based tumor segmentation on voxel-level dosimetry in liver radioembolization

Abstract Background The purpose was to validate 90Y PET gradient-based tumor segmentation in phantoms and to evaluate the impact of the segmentation method on reported tumor absorbed dose (AD) and biological effective dose (BED) in 90Y microsphere radioembolization (RE) patients. A semi-automated gradient-based method was applied to phantoms and patient tumors on the 90Y PET with the initial bounding volume for gradient detection determined from a registered diagnostic CT or MR; this PET-based segmentation (PS) was compared with radiologist-defined morphologic segmentation (MS) on CT or MRI. AD and BED volume histogram metrics (D90, D70, mean) were calculated using both segmentations and concordance/correlations were investigated. Spatial concordance was assessed using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). PS was repeated to assess intra-observer variability. Results In phantoms, PS demonstrated high accuracy in lesion volumes (within 15%), AD metrics (within 11%), high spatial concordance relative to morphologic segmentation (DSC > 0.86 and MDA  0.99, MDA < 0.2 mm, AD/BED metrics within 2%). For patients (58 lesions), spatial concordance between PS and MS was degraded compared to in-phantom (average DSC = 0.54, average MDA = 4.8 mm); the average mean tumor AD was 226 ± 153 and 197 ± 138 Gy, respectively for PS and MS. For patient AD metrics, the best Pearson correlation (r) and concordance correlation coefficient (ccc) between segmentation methods was found for mean AD (r = 0.94, ccc = 0.92), but worsened as the metric approached the minimum dose (for D90, r = 0.77, ccc = 0.69); BED metrics exhibited a similar trend. Patient PS showed low intra-observer variability (average DSC = 0.81, average MDA = 2.2 mm, average AD/BED metrics within 3.0%). Conclusions 90Y PET gradient-based segmentation led to accurate/robust results in phantoms, and showed high concordance with MS for reporting mean tumor AD/BED in patients. However, tumor coverage metrics such as D90 exhibited worse concordance between segmentation methods, highlighting the need to standardize segmentation methods when reporting AD/BED metrics from post-therapy 90Y PET. Estimated differences in reported AD/BED metrics due to segmentation method will be useful for interpreting RE dosimetry results in the literature including tumor response data.https://deepblue.lib.umich.edu/bitstream/2027.42/146544/1/40658_2018_Article_230.pd

Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage

PURPOSE: Functional-guided radiation therapy (RT) plans have the potential to limit damage to normal tissue and reduce toxicity. Although functional imaging modalities have continued to improve, a limited understanding of the functional response to radiation and its application to personalized therapy has hindered clinical implementation. The purpose of this study was to retrospectively model the longitudinal, patient-specific dose-function response in non-small cell lung cancer patients treated with RT to better characterize the expected functional damage in future, unknown patients. METHODS AND MATERIALS: Perfusion single-photon emission computed tomography/computed tomography scans were obtained at baseline (n = 81), midtreatment (n = 74), 3 months post-treatment (n = 51), and 1 year post-treatment (n = 26) and retrospectively analyzed. Patients were treated with conventionally fractionated RT or stereotactic body RT. Normalized perfusion single-photon emission computed tomography voxel intensity was used as a surrogate for local lung function. A patient-specific logistic model was applied to each individual patient's dose-function response to characterize functional reduction at each imaging time point. Patient-specific model parameters were averaged to create a population-level logistic dose-response model. RESULTS: A significant longitudinal decrease in lung function was observed after RT by analyzing the voxelwise change in normalized perfusion intensity. Generated dose-function response models represent the expected voxelwise reduction in function, and the associated uncertainty, for an unknown patient receiving conventionally fractionated RT or stereotactic body RT. Differential treatment responses based on the functional status of the voxel at baseline suggest that initially higher functioning voxels are damaged at a higher rate than lower functioning voxels. CONCLUSIONS: This study modeled the patient-specific dose-function response in patients with non-small cell lung cancer during and after radiation treatment. The generated population-level dose-function response models were derived from individual patient assessment and have the potential to inform functional-guided treatment plans regarding the expected functional lung damage. This type of patient-specific modeling approach can be applied broadly to other functional response analyses to better capture intrapatient dependencies and characterize personalized functional damage

Early phase clinical trials extension to the guidelines for the content of statistical analysis plans

This paper reports guidelines for the content of statistical analysis plans for early phase clinical trials, ensuring specification of the minimum reporting analysis requirements, by detailing extensions (11 new items) and modifications (25 items) to existing guidance after a review by various stakeholders

Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

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