Lyman-Kutcher-Burman Model Research Articles

Establishment of the NTCP model for radiation-induced lung injury in patients with non-small cell lung cancer

Objective To examine the incidence of radiation-induced lung injury (RILI) after involved-field intensity-modulated radiation therapy (IMRT) in patients with locally advanced non-small cell lung cancer (NSCLC), and to evaluate the predictability of different models. Methods The clinical data of 242 inoperable or unresectable stage Ⅲ NSCLC patients treated in our hospital from 2007 to 2011 were reviewed. Grade 2 and grade 3 RILI that occurred within 6 months after IMRT were selected as outcome events in this study. The principal component analysis (PCA) model, Lyman-Kutcher-Burman (LKB) model, and mean lung dose (MLD) model were each used to establish a predictive model of normal tissue complication probability (NTCP) for evaluating the dosimetric parameters of IMRT. Results Four principal components were used in the PCA model. The areas under the receiver operating characteristic curve (AUCs) of grade 2 and grade 3 RILI were 0.652 and 0.611, respectively. For the LKB model, the fitted parameters were m=0.46, n=1.35, and D50=23.59 Gy for grade 2 RILI, and m=0.36, n=0.27, and D50=72.67 Gy for grade 3 RILI. The AUCs of grade 2 and grade 3 RILI in the LKB model were 0.607 and 0.585, respectively. For the MLD model, the estimated parameters were γ50=1.073 and D50=24.66 Gy for grade 2 RILI, and γ50=0.97 and D50=48.45 Gy for grade 3 RILI. The AUCs of grade 2 and grade 3 RILI in the MLD model were 0.604 and 0.569, respectively. Conclusions The use of large data set from a single patient population with the same mode of treatment is very important for improving model predictability and stability. Both the LKB model and PCA model can predict the probability of RILI, whereas the MLD model is less effective in predicting grade 3 RILI. Key words: Carcinoma, non-small-cell lung; Principal component analysis; Models, Theoretical; Lung injury; Normal tissue complication probability

Radiation Dose to the Thoracic Vertebral Bodies Is Associated With Acute Hematologic Toxicities in Patients Receiving Concurrent Chemoradiation for Lung Cancer: Results of a Single-Center Retrospective Analysis

Totest the hypothesis that increasing radiation therapy (RT) dose to the thoracic vertebral bodies (TVBs) contributes to the development of hematologic toxicities (HTs) in patients with lung cancer. Cases of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) treated with definitive chemoradiation with concurrent platinum-based doublet chemotherapy at our institution from 2007 to 2016 were identified. Mean TVB dose and the volume of TVBs receiving at least 5 to 60Gy(V5-V60) were retrospectively recorded. Logistic regression was used to test associations between grade ≥3 HT (HT3+) and dosimetric/clinical parameters. Normal tissue complication probability was evaluated using the Lyman-Kutcher-Burman (LKB) model for HT3+, and receiver operating characteristics analysis was used to determine dosimetric cut-points. We identified 201 patients, the majority having NSCLC (n=162, 81%) and stage III to IV disease (n=179, 89%). All patients received either cisplatin/etoposide (n=107, 53%) or carboplatin/paclitaxel (n=94, 47%). Median RT dose was 60Gy (range, 60-70Gy). The rate of HT3+ was 49% (n=99). Increasing mean TVB dose(per Gy) was associated with higher odds of developing HT3+ (odds ratio 1.041, 95% confidence interval 1.004-1.080, P=.032), as were increasing TVB V5 to V20. Thesedosimetric correlates to HT3+ persisted on multivariate analysis. Constrained optimization of the LKB model for HT3+ yielded the parameters: n=1, m=1.79, and TD50=21.4Gy. Optimal cut-points identified were V5=65%, V10=60%, V20=50%, and mean dose=23.5Gy. Patients with values above these cut-points had an approximately 2-fold increased risk of HT3+. We found that mean TVB dose and low-dose parameters (V5-V20) were associated with HT3+ in chemoradiation for lung cancer. Per the LKB model, bone marrow behaves like a parallel organ (n=1), implying that mean TVB dose is a useful predictor for toxicity. These data suggest that efforts to spare dose to the TVBs may reduce rates of severe HT.

Dose–volume toxicity modeling for de-intensified chemo-radiation therapy for HPV-positive oropharynx cancer

Background and purposeThe aim is to determine the radiobiological parameters of four popular normal tissue complication probability (NTCP) models that describe the dose–response relations of salivary glands and pharyngeal constrictors to the severity of patient reported xerostomia and dysphagia, respectively 6 and 12months post chemo-radiotherapy, furthermore, to evaluate the goodness-of-fit of the NTCP models for different combinations of glands and constrictors. Material and methodsForty-three patients were treated on a prospective multi-institutional phase II study (ClinicalTrials.gov, NCT01530997) assessing the efficacy of de-intensified chemoradiotherapy in patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma. All patients received 60Gy intensity modulated radiotherapy with concurrent weekly intravenous cisplatinum. All patients reported severity of their xerostomia and dysphagia (pre- and post-treatment) using the patient reported outcome version of the CTCAE (PRO-CTCAE). A change in severity (from baseline) of ≥2 was considered clinically meaningful. The Lyman–Kutcher–Burman (LKB), Relative Seriality (RS), Logit, and Relative Seriality Logit (RSL) NTCP models were used to fit the patients’ dose/volume data to changes in PRO-CTCAE severity of xerostomia and dysphagia (from baseline to 6 and 12months post-treatment). The correlation of the models with the patient outcomes was performed for different combinations of salivary glands and different sections of pharyngeal constrictors. The goodness-of-fit of the different models was assessed through the area under the receiver operating characteristic curve (AUC), maximum of the log-likelihood function, normal error distribution and Akaike information criterion (AIC). ResultsThe dose/volume metrics of the combined contralateral (parotid+submandibular) glands appear to correlate best with xerostomia, at both 6- and 12-months. Among the different sections of pharyngeal constrictors, the dose/volume metrics of the superior pharyngeal constrictors appear to correlate best with dysphagia at 6months. The AUC values ranged from 0.72 to 0.85 in the case of xerostomia and 0.73 to 0.74 in the case of dysphagia over the different models. The four NTCP models showed similar goodness-of-fit. The differences in AIC between the different models were less than 2 and ranged within 0.7 and 0.8 in the cases of xerostomia and dysphagia, respectively. The calculated parameters of the LKB model were D50=26.9Gy, m=0.63, n=1.0 for the combined contralateral glands at 12months and D50=62.0Gy, m=0.10, n=0.49 for the superior pharyngeal constrictors at 6months. ConclusionsThe values of the parameters of four NTCP models were determined for salivary glands and pharyngeal constrictors. All four models could fit the clinical data equally well. The NTCP predictions of the combined contralateral glands and superior pharyngeal constrictors showed the best correlation with the patient reported outcomes of xerostomia and dysphagia, respectively.

Parotid gland mean dose as a xerostomia predictor in low-dose domains

Purpose: Xerostomia is a common side effect of radiotherapy resulting from excessive irradiation of salivary glands. Typically, xerostomia is modeled by the mean dose-response characteristic of parotid glands and prevented by mean dose constraints to either contralateral or both parotid glands. The aim of this study was to investigate whether normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands are suitable for the prediction of xerostomia in a highly conformal low-dose regime of modern intensity-modulated radiotherapy (IMRT) techniques.Material and methods: We present a retrospective analysis of 153 head and neck cancer patients treated with radiotherapy. The Lyman Kutcher Burman (LKB) model was used to evaluate predictive power of the parotid gland mean dose with respect to xerostomia at 6 and 12 months after the treatment. The predictive performance of the model was evaluated by receiver operating characteristic (ROC) curves and precision-recall (PR) curves.Results: Average mean doses to ipsilateral and contralateral parotid glands were 25.4 Gy and 18.7 Gy, respectively. QUANTEC constraints were met in 74% of patients. Mild to severe (G1+) xerostomia prevalence at both 6 and 12 months was 67%. Moderate to severe (G2+) xerostomia prevalence at 6 and 12 months was 20% and 15%, respectively. G1 + xerostomia was predicted reasonably well with area under the ROC curve ranging from 0.69 to 0.76. The LKB model failed to provide reliable G2 + xerostomia predictions at both time points.Conclusions: Reduction of the mean dose to parotid glands below QUANTEC guidelines resulted in low G2 + xerostomia rates. In this dose domain, the mean dose models predicted G1 + xerostomia fairly well, however, failed to recognize patients at risk of G2 + xerostomia. There is a need for the development of more flexible models able to capture complexity of dose response in this dose regime.

A modeling study of functional magnetic resonance imaging to individualize target definition of seminal vesicles for external beam radiotherapy

Background: Pre-treatment magnetic resonance imaging (MRI) can give patient-specific evaluation of suspected pathologically involved volumes in the seminal vesicles (SV) in prostate cancer patients. By targeting this suspicious volume we hypothesize that radiotherapy is more efficient without introducing more toxicity. In this study we evaluate the concept of using MRI-defined target volumes in terms of tumor control probability (TCP) and rectal normal tissue complication probability (NTCP).Material and methods: Twenty-one high-risk prostate cancer patients were included. Pre-treatment CT images, T2 weighted (T2w) MRI and two multi-parametric MRI were acquired. Overlap between a suspicious volume in the SV observed on T2w images and a suspicious volume observed on either multi-parametric MRI was assumed to reflect a true malignant region (named ‘MRI positive’). In addition the entire SV on the CT-scan was delineated. Three treatment plans of 2 Gy ×39 fractions were generated per patient: one covering the MRI positive volume in SV and prostate with margin of 11 mm to the MRI positive in the SV and two plans covering prostate and SV using 11 and 7 mm SV margin, respectively. All plans were prescribed the same PTV mean dose. Rectal NTCP grade ≥2 was evaluated with the Lyman–Kutcher–Burman model and TCP was estimated by a logistic model using the combined MRI positive volume in SV and prostate as region-of-interest.Results: Fourteen of twenty-one patients were classified as MRI positive, six of which had suspicious volumes in all three MRI modalities. On average TCP for the plan covering prostate and the MRI positive volume was 3% higher (up to 11%) than the two other plans which was statistically significant. The increased TCP was obtained without increasing rectal NTCP grade ≥2.Conclusions: Using functional MRI for individualized target delineation in the SV may improve the treatment outcome in radiotherapy of prostate cancer without increasing the rectal toxicity.

SU‐F‐T‐110: LKB Modeling of Acute Hematologic Toxicity in Rectal Cancer

Purpose:To investigate dosimetric parameters correlated with acute hematologic toxicity (HT) in patients with rectal cancer treated with IMRT and concurrent chemotherapy.Methods:We analyzed 127 rectal cancer patients receiving IMRT and concurrent chemotherapy. Whole pelvic bone marrow (PBM) was contoured for each patient including lumbosacral spine (LSSP), ilium and lower pelvic (LP). The equivalent uniform dose (EUD) of each region and the PBM were calculated. Endpoints for HT is grade ≥3 (HT3+) leukopenia, neutropenia or thrombocytopenia. Normal tissue complication probability (NTCP) was evaluated with the Lyman‐Kutcher‐Burman (LKB) model. On the basis of our NTCP modeling, we get the value of TD5, TD10, TD50 and other predictors of hematologic toxicity.Results:Twenty six patients experienced HT3+. Constrained optimization of the LKB model for HT3+ yielded the parameters m=0.583, n=1, and TD50=59.6Gy. With this model, mean PBM doses of 1.5Gy, 15Gy, 30.5Gy, 42Gy and 51Gy result in a 5%, 10%, 20%, 30% and 40% risk of HT3+, respectively. Our model suggested chemotherapy alone induced a HT3+ complication rate of about 4.6%.Conclusion:LKB modeling indicates that PBM acts like a parallel organ, implying that the mean dose to the organ is a useful predictor for toxicity. Compared with a Stanford study about anal cancer patients, the patients of anal cancer received chemotherapy with MMC and 5‐FU which can cause much more severe HT than 5‐FU or FOLFOX in our rectal cancer study. Therefore, the incidence of HT3+ is much higher in anal cancer and resulted in a much lower TD50 than our study.

Assessing the shift of radiobiological metrics in lung radiotherapy plans using 2D gamma index

The purpose of this work is to investigate the 2D gamma (γ) maps to illustrate the change of radiobiological outcomes for lung radiotherapy plans and evaluate the correlation between tumor control probability (TCP), normal tissue complication probability (NTCP) with γ passing rates (γ-rates). Nine patients with lung cancer were used. The doses were calculated using Modified Batho method integrated with pencil beam convolution (MB-PBC) and anisotropic analytical algorithm (AAA) using the same beam arrangements and prescription dose. The TCP and NTCP were estimated, respectively, using equivalent uniform dose (EUD) model and Lyman-Kutcher-Burman (LKB) model. The correlation between ΔTCP or ΔNTCP with γ-rates, from 2%/2 and 3%/3 mm, were tested to explore the best correlation predicting the relevant γ criteria using Spearman's rank test (ρ). Wilcoxon paired test was used to calculate P value. TCP value was significantly lower in the recalculated AAA plans as compared to MB plans. However, AAA predicted more NTCP on lung pneumonitis according to the LKB model and using relevant radiobiological parameters (n, m and TD50) for MB-PBC and AAA, with P=0.03. The data showed a weak correlation between radiobiological metrics with γ-rates or γ-mean, ρ<0.3. AAA and MB yield different TCP values as well as NTCP for lung pneumonitis based on the LKB model parameters. Therefore, 2D γ-maps, generated with 2%/2 or 3%/3 mm, could illustrate visual information about the radiobiological changes. The information is useful to evaluate the clinical outcome of a radiotherapy treatment and to approve the treatment plan of the patient if the dose constraints are respected. On the other hand, the γ-maps tool can be used as quality assurance (QA) process to check the predicted TCP and NTCP from radiobiological models.

SU‐D‐204‐03: Comparison of Patient Positioning Methods Through Modeling of Acute Rectal Toxicity in Intensity Modulated Radiation Therapy for Prostate Cancer. Does Quality of Data Matter More Than the Quantity?

Purpose:To determine if differences in patient positioning methods have an impact on the incidence and modeling of grade &gt;=2 acute rectal toxicity in prostate cancer patients who were treated with Intensity Modulated Radiation Therapy (IMRT).Methods:We compared two databases of patients treated with radiation therapy for prostate cancer: a database of 79 patients who were treated with 7 field IMRT and daily image guided positioning based on implanted gold markers (IGRTdb), and a database of 302 patients who were treated with 5 field IMRT and daily positioning using a trans‐abdominal ultrasound system (USdb). Complete planning dosimetry was available for IGRTdb patients while limited planning dosimetry, recorded at the time of planning, was available for USdb patients. We fit Lyman‐Kutcher‐Burman (LKB) model to IGRTdb only, and Univariate Logistic Regression (ULR) NTCP model to both databases. We perform Receiver Operating Characteristics analysis to determine the predictive power of NTCP models.Results:The incidence of grade &gt;= 2 acute rectal toxicity in IGRTdb was 20%, while the incidence in USdb was 54%. Fits of both LKB and ULR models yielded predictive NTCP models for IGRTdb patients with Area Under the Curve (AUC) in the 0.63 – 0.67 range. Extrapolation of the ULR model from IGRTdb to planning dosimetry in USdb predicts that the incidence of acute rectal toxicity in USdb should not exceed 40%. Fits of the ULR model to the USdb do not yield predictive NTCP models and their AUC is consistent with AUC = 0.5.Conclusion:Accuracy of a patient positioning system affects clinically observed toxicity rates and the quality of NTCP models that can be derived from toxicity data. Poor correlation between planned and clinically delivered dosimetry may lead to erroneous or poorly performing NTCP models, even if the number of patients in a database is large.

Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans.

BackgroundThe aim of this study is to evaluate the radiobiological impact of Acuros XB (AXB) vs. Anisotropic Analytic Algorithm (AAA) dose calculation algorithms in combined dose-volume and biological optimized IMRT plans of SBRT treatments for non-small-cell lung cancer (NSCLC) patients.MethodsTwenty eight patients with NSCLC previously treated SBRT were re-planned using Varian Eclipse (V11) with combined dose-volume and biological optimization IMRT sliding window technique. The total dose prescribed to the PTV was 60 Gy with 12 Gy per fraction. The plans were initially optimized using AAA algorithm, and then were recomputed using AXB using the same MUs and MLC files to compare with the dose distribution of the original plans and assess the radiobiological as well as dosimetric impact of the two different dose algorithms. The Poisson Linear-Quadatric (PLQ) and Lyman-Kutcher-Burman (LKB) models were used for estimating the tumor control probability (TCP) and normal tissue complication probability (NTCP), respectively. The influence of the model parameter uncertainties on the TCP differences and the NTCP differences between AAA and AXB plans were studied by applying different sets of published model parameters. Patients were grouped into peripheral and centrally-located tumors to evaluate the impact of tumor location.ResultsPTV dose was lower in the re-calculated AXB plans, as compared to AAA plans. The median differences of PTV(D95%) were 1.7 Gy (range: 0.3, 6.5 Gy) and 1.0 Gy (range: 0.6, 4.4 Gy) for peripheral tumors and centrally-located tumors, respectively. The median differences of PTV(mean) were 0.4 Gy (range: 0.0, 1.9 Gy) and 0.9 Gy (range: 0.0, 4.3 Gy) for peripheral tumors and centrally-located tumors, respectively. TCP was also found lower in AXB-recalculated plans compared with the AAA plans. The median (range) of the TCP differences for 30 month local control were 1.6 % (0.3 %, 5.8 %) for peripheral tumors and 1.3 % (0.5 %, 3.4 %) for centrally located tumors. The lower TCP is associated with the lower PTV coverage in AXB-recalculated plans. No obvious trend was observed between the calculation-resulted TCP differences and tumor size or location. AAA and AXB yield very similar NTCP on lung pneumonitis according to the LKB model estimation in the present study.ConclusionAAA apparently overestimates the PTV dose; the magnitude of resulting difference in calculated TCP was up to 5.8 % in our study. AAA and AXB yield very similar NTCP on lung pneumonitis based on the LKB model parameter sets we used in the present study.

Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers

AimTo model acute rectal toxicity in Intensity Modulated Radiation Therapy (IMRT) for prostate cancer using dosimetry and patient specific characteristics.MethodsA database of 79 prostate cancer patients treated with image guided IMRT was used to fit parameters of Lyman-Kutcher-Burman (LKB) and logistic regression Normal Tissue Complications Probability (NTCP) models to acute grade ≥ 2 rectal toxicities. We used a univariate regression model to find the dosimetric index which was most correlated with toxicity and a multivariate logistic regression model with machine learning algorithm to integrate dosimetry with patient specific characteristics. We used Receiver Operating Characteristics (ROC) analysis and the area under the ROC curve (AUC) to quantify the predictive power of models.ResultsSixteen patients (20.3%) developed acute grade≥2 rectal toxicity. Our best estimate (95% confidence interval) of LKB model parameters for acute rectal toxicity are exponent n=0.13 (0.1–0.16), slope m=0.09 (0.08–0.11), and threshold dose TD50=56.8 (53.7–59.9) Gy. The best dosimetric indices in the univariate logistic regression NTCP model were D25% and V50Gy. The best AUC of dosimetry only modeling was 0.67 (0.54, 0.8). In the multivariate logistic regression two patient specific variables were particularly strongly correlated with acute rectal toxicity, the use of statin drugs and PSA level prior to IMRT, while two additional variables, age and diabetes were weakly correlated. The AUC of the logistic regression NTCP model improved to 0.88 (0.8, 0.96) when patient specific characteristics were included. In a group of 79 patients, 40 took Statins and 39 did not. Among patients who took statins, (4/40)=10% developed acute grade ≥2 rectal toxicity, compared to (12/39)=30.8% who did not take statins (p=0.03). The average and standard deviation of PSA distribution for patients with acute rectal toxicity was PSAtox = 5.77 ± 2.27 and it was PSAnotox = 9.5 ± 7.8 for the remainder (p=0.01).ConclusionsPatient specific characteristics strongly influence the likelihood of acute grade ≥ 2 rectal toxicity in radiation therapy for prostate cancer.

Normal tissue complication probability modeling for cochlea constraints to avoid causing tinnitus after head-and-neck intensity-modulated radiation therapy

BackgroundRadiation-induced tinnitus is a side effect of radiotherapy in the inner ear for cancers of the head and neck. Effective dose constraints for protecting the cochlea are under-reported. The aim of this study is to determine the cochlea dose limitation to avoid causing tinnitus after head-and-neck cancer (HNC) intensity-modulated radiation therapy (IMRT).MethodsIn total 211 patients with HNC were included; the side effects of radiotherapy were investigated for 422 inner ears in the cohort. Forty-nine of the four hundred and twenty-two samples (11.6 %) developed grade 2+ tinnitus symptoms after IMRT, as diagnosed by a clinician. The Late Effects of Normal Tissues–Subjective, Objective, Management, Analytic (LENT-SOMA) criteria were used for tinnitus evaluation. The logistic and Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) models were used for the analyses.ResultsThe NTCP-fitted parameters were TD50 = 46.31 Gy (95 % CI, 41.46–52.50), γ50 = 1.27 (95 % CI, 1.02–1.55), and TD50 = 46.52 Gy (95 % CI, 41.91–53.43), m = 0.35 (95 % CI, 0.30–0.42) for the logistic and LKB models, respectively. The suggested guideline TD20 for the tolerance dose to produce a 20 % complication rate within a specific period of time was TD20 = 33.62 Gy (95 % CI, 30.15–38.27) (logistic) and TD20 = 32.82 Gy (95 % CI, 29.58–37.69) (LKB).ConclusionsTo maintain the incidence of grade 2+ tinnitus toxicity <20 % in IMRT, we suggest that the mean dose to the cochlea should be <32 Gy. However, models should not be extrapolated to other patient populations without further verification and should first be confirmed before clinical implementation.

A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy

The aim of this work was to develop a framework for modeling organ effects within TOPAS (TOol for PArticle Simulation), a wrapper of the Geant4 Monte Carlo toolkit that facilitates particle therapy simulation. The DICOM interface for TOPAS was extended to permit contour input, used to assign voxels to organs. The following dose response models were implemented: The Lyman–Kutcher–Burman model, the critical element model, the population based critical volume model, the parallel-serial model, a sigmoid-based model of Niemierko for normal tissue complication probability and tumor control probability (TCP), and a Poisson-based model for TCP. The framework allows easy manipulation of the parameters of these models and the implementation of other models.As part of the verification, results for the parallel-serial and Poisson model for x-ray irradiation of a water phantom were compared to data from the AAPM Task Group 166. When using the task group dose-volume histograms (DVHs), results were found to be sensitive to the number of points in the DVH, with differences up to 2.4%, some of which are attributable to differences between the implemented models. New results are given with the point spacing specified. When using Monte Carlo calculations with TOPAS, despite the relatively good match to the published DVH’s, differences up to 9% were found for the parallel-serial model (for a maximum DVH difference of 2%) and up to 0.5% for the Poisson model (for a maximum DVH difference of 0.5%). However, differences of 74.5% (in Rectangle1), 34.8% (in PTV) and 52.1% (in Triangle) for the critical element, critical volume and the sigmoid-based models were found respectively.We propose a new benchmark for verification of organ effect models in proton therapy. The benchmark consists of customized structures in the spread out Bragg peak plateau, normal tissue, tumor, penumbra and in the distal region. The DVH’s, DVH point spacing, and results of the organ effect models are provided. The models were used to calculate dose response for a Head and Neck patient to demonstrate functionality of the new framework and indicate the degree of variability between the models in proton therapy.

SU‐E‐T‐803: Verification of QUANTEC Lyman Kutcher Burman (LKB) Model for Grade&gt;=2(2+) Late Rectal Complication Rates Using a Database of 79 Prostate Patients Treated with IMRT

Purpose:QUANTEC review of best parameters for the LKB NTCP model of rectal complications is based exclusively on datasets obtained with 3D conformal techniques. The report suggests that inherent differences in rectal dose distributions obtained with IMRT techniques could require modification of the parameters which provide the best fit to clinical data.Methods:We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub‐volume of a prostate which was identified on a pre‐treatment MRI study. Rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient's medical records. Late grade 2+ toxicities were defined as toxicities occurring later than 90 days following the end of treatment. We defined the model in terms of parameters, m,n and TD_50, as recommended in the Quantec report. We converted the dose to 2Gy equivalent dose using linear‐quadratic model with alpha over beta of 3Gy. We applied QUANTEC model to our data and compared results to our own fit of LKB model using two sample t‐test.Results:Grade 2+ late rectal toxicity occurred in 4% (3/79 patients). The best fit of LKB model to data was obtained for n = 0.26, m = 0.25, and TD_50 = 81.53Gy. The two sample t‐test yielded p value of 0.67. Average NTCP predicted by our parametrization is 3.8% while average NTCP predicted by QUANTEC parametrization is 3.5%.Conclusion:Predictions of late rectal toxicities in IMRT patients using LKB model with parameters from QUANTEC report match observed toxicity rates. Independent fit of LKB model to IMRT data produces somewhat different parameter set, but two parametrizations are equivalent within statistical uncertainties.

TH‐AB‐304‐02: Fitting Grade&gt;=2(2+) Acute Rectal Complication Rates in Prostate Cancer Patients to Lyman Kutcher Burman (LKB) and Logistic Regression NTCP Models Using Dosimetry and Patient Specific Characteristics

Purpose:Models of rectal toxicity which include dosimetry only are known to have a relatively low predictive power, at least as measured by the area under the ROC curve (AUC). It has been suggested that the predictive power of models can be improved by including non‐dosimetric patient specific characteristics.Methods:We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub‐volume of a prostate which was identified on a pre‐treatment MRI study. Acute grade 2+ rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient's medical records. We modified the LKB model to include one patient specific variable at a time, and we also used an NTCP model based on logistic regression to perform multi‐variate analysis. We used patient specific variables available to us in a retrospective study: age, diabetes, hormonal treatment, Gleason Score, PSA, Statin use, prostate volume, boost volume and rectal volume.Results:Grade 2+ acute rectal toxicity occurred in 20% (16/79 patients). The LKB model with dosimetry alone gives AUC=0.65. Four variables, age, diabetes, PSA, Statin use, increase the AUC in LKB model to a maximum of 0.79. The same four variables in the logistic regression model increase AUC to 0.87. The most significant correlations are with PSA and with Statin use.Conclusion:Including patient specific variables in toxicity models can significantly increase apparent predictive power of a model. Somewhat surprising finding of a strong correlation between rectal toxicity and PSA in our dataset suggests that conclusions from each individual study should be treated with caution, until independently confirmed. Larger databases from prospective studies or meta‐analysis of multiple studies may be needed to find patient characteristics that are truly predictive.

SU‐E‐T‐466: Implementation of An Extension Module for Dose Response Models in the TOPAS Monte Carlo Toolkit

Purpose:To develop and verify an extension to TOPAS for calculation of dose response models (TCP/NTCP). TOPAS wraps and extends Geant4.Methods:The TOPAS DICOM interface was extended to include structure contours, for subsequent calculation of DVH's and TCP/NTCP. The following dose response models were implemented: Lyman‐Kutcher‐Burman (LKB), critical element (CE), population based critical volume (CV), parallel‐serials, a sigmoid‐based model of Niemierko for NTCP and TCP, and a Poisson‐based model for TCP. For verification, results for the parallel‐serial and Poisson models, with 6 MV x‐ray dose distributions calculated with TOPAS and Pinnacle v9.2, were compared to data from the benchmark configuration of the AAPM Task Group 166 (TG166). We provide a benchmark configuration suitable for proton therapy along with results for the implementation of the Niemierko, CV and CE models.Results:The maximum difference in DVH calculated with Pinnacle and TOPAS was 2%. Differences between TG166 data and Monte Carlo calculations of up to 4.2%±6.1% were found for the parallel‐serial model and up to 1.0%±0.7% for the Poisson model (including the uncertainty due to lack of knowledge of the point spacing in TG166). For CE, CV and Niemierko models, the discrepancies between the Pinnacle and TOPAS results are 74.5%, 34.8% and 52.1% when using 29.7 cGy point spacing, the differences being highly sensitive to dose spacing. On the other hand, with our proposed benchmark configuration, the largest differences were 12.05%±0.38%, 3.74%±1.6%, 1.57%±4.9% and 1.97%±4.6% for the CE, CV, Niemierko and LKB models, respectively.Conclusion:Several dose response models were successfully implemented with the extension module. Reference data was calculated for future benchmarking. Dose response calculated for the different models varied much more widely for the TG166 benchmark than for the proposed benchmark, which had much lower sensitivity to the choice of DVH dose points.This work was supported by National Cancer Institute Grant R01CA140735

Prediction of gastrointestinal toxicity after external beam radiotherapy for localized prostate cancer.

BackgroundGastrointestinal (GI) toxicity is a common effect following radiation therapy (RT) for prostate cancer. Purpose of the present work is to compare two Normal Tissue Complication Probability (NTCP) modelling approaches for prediction of late radio-induced GI toxicity after prostate external beam radiotherapy.MethodsThe study includes 84 prostate cancer patients evaluated for late rectal toxicity after 3D conformal radiotherapy. Median age was 72 years (range 53-85). All patients received a total dose of 76 Gy to the prostate gland with daily fractions of 2 Gy. The acute and late radio-induced GI complications were classified according to the RTOG/EORTC scoring system. Rectum dose-volume histograms were extracted for Lyman-Kutcher-Burman (LKB) NTCP model fitting using Maximum Likelihood Estimation. The bootstrap method was employed to test the fit robustness. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive power of the LKB and to compare it with a multivariate logistic NTCP model previously determined.ResultsAt a median follow-up of 36 months, 42% (35/84) of patients experienced grade 1-2 (G1-2) acute GI events while 25% (21/84) of patients developed G1-2 late GI events. The best-estimate of fitting parameters for LKB NTCP model for mild\\moderate GI toxicity resulted to be: D50 = 87.3 Gy, m = 0.37 and n = 0.10. Bootstrap result showed that the parameter fit was robust. The AUC values for the LKB and for the multivariate logistic models were 0.60 and 0.75, respectively.ConclusionsWe derived the parameters of the LKB model for mild\\moderate GI toxicity prediction and we compared its performance with that of a data-driven multivariate model. Compared to LKB, the multivariate model confirmed a higher predictive power as showed by the AUC values.

Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126

The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. A total of 219 IMRT patients from the high-dose arm (79.2Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant (P=.21) due to insufficient statistical power in this retrospective study. Plan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications.

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer

Background and purposeWe explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients. Materials and methodsSixty-two patients who underwent curative hypofractionated radiotherapy (66Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman–Kutcher–Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data. ResultsIntegration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models. ConclusionAs a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities.

Complication Probability Models for Radiation-Induced Heart Valvular Dysfunction: Do Heart-Lung Interactions Play a Role?

PurposeThe purpose of this study is to compare different normal tissue complication probability (NTCP) models for predicting heart valve dysfunction (RVD) following thoracic irradiation.MethodsAll patients from our institutional Hodgkin lymphoma survivors database with analyzable datasets were included (n = 90). All patients were treated with three-dimensional conformal radiotherapy with a median total dose of 32 Gy. The cardiac toxicity profile was available for each patient. Heart and lung dose-volume histograms (DVHs) were extracted and both organs were considered for Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) NTCP model fitting using maximum likelihood estimation. Bootstrap refitting was used to test the robustness of the model fit. Model performance was estimated using the area under the receiver operating characteristic curve (AUC).ResultsUsing only heart-DVHs, parameter estimates were, for the LKB model: D50 = 32.8 Gy, n = 0.16 and m = 0.67; and for the RS model: D50 = 32.4 Gy, s = 0.99 and γ = 0.42. AUC values were 0.67 for LKB and 0.66 for RS, respectively. Similar performance was obtained for models using only lung-DVHs (LKB: D50 = 33.2 Gy, n = 0.01, m = 0.19, AUC = 0.68; RS: D50 = 24.4 Gy, s = 0.99, γ = 2.12, AUC = 0.66). Bootstrap result showed that the parameter fits for lung-LKB were extremely robust. A combined heart-lung LKB model was also tested and showed a minor improvement (AUC = 0.70). However, the best performance was obtained using the previously determined multivariate regression model including maximum heart dose with increasing risk for larger heart and smaller lung volumes (AUC = 0.82).ConclusionsThe risk of radiation induced valvular disease cannot be modeled using NTCP models only based on heart dose-volume distribution. A predictive model with an improved performance can be obtained but requires the inclusion of heart and lung volume terms, indicating that heart-lung interactions are apparently important for this endpoint.

Influence of image slice thickness on rectal dose–response relationships following radiotherapy of prostate cancer*

When pooling retrospective data from different cohorts, slice thicknesses of acquired computed tomography (CT) images used for treatment planning may vary between cohorts. It is, however, not known if varying slice thickness influences derived dose–response relationships. We investigated this for rectal bleeding using dose–volume histograms (DVHs) of the rectum and rectal wall for dose distributions superimposed on images with varying CT slice thicknesses. We used dose and endpoint data from two prostate cancer cohorts treated with three-dimensional conformal radiotherapy to either 74 Gy (N = 159) or 78 Gy (N = 159) at 2 Gy per fraction. The rectum was defined as the whole organ with content, and the morbidity cut-off was Grade ≥2 late rectal bleeding. Rectal walls were defined as 3 mm inner margins added to the rectum. DVHs for simulated slice thicknesses from 3 to 13 mm were compared to DVHs for the originally acquired slice thicknesses at 3 and 5 mm. Volumes, mean, and maximum doses were assessed from the DVHs, and generalized equivalent uniform dose (gEUD) values were calculated. For each organ and each of the simulated slice thicknesses, we performed predictive modeling of late rectal bleeding using the Lyman–Kutcher–Burman (LKB) model. For the most coarse slice thickness, rectal volumes increased (≤18%), whereas maximum and mean doses decreased (≤0.8 and ≤4.2 Gy, respectively). For all a values, the gEUD for the simulated DVHs were ≤1.9 Gy different than the gEUD for the original DVHs. The best-fitting LKB model parameter values with 95% CIs were consistent between all DVHs. In conclusion, we found that the investigated slice thickness variations had minimal impact on rectal dose–response estimations. From the perspective of predictive modeling, our results suggest that variations within 10 mm in slice thickness between cohorts are unlikely to be a limiting factor when pooling multi-institutional rectal dose data that include slice thickness variations within this range.