Rainer J. Klement

Applicability of the linear-quadratic formalism for modeling local tumor control probability in high dose per fraction stereotactic body radiotherapy for early stage non-small cell lung cancer.

BACKGROUND AND PURPOSEnTo compare the linear-quadratic (LQ) and the LQ-L formalism (linear cell survival curve beyond a threshold dose dT) for modeling local tumor control probability (TCP) in stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC).nnnMATERIALS AND METHODSnThis study is based on 395 patients from 13 German and Austrian centers treated with SBRT for stage I NSCLC. The median number of SBRT fractions was 3 (range 1-8) and median single fraction dose was 12.5 Gy (2.9-33 Gy); dose was prescribed to the median 65% PTV encompassing isodose (60-100%). Assuming an α/β-value of 10 Gy, we modeled TCP as a sigmoid-shaped function of the biologically effective dose (BED). Models were compared using maximum likelihood ratio tests as well as Bayes factors (BFs).nnnRESULTSnThere was strong evidence for a dose-response relationship in the total patient cohort (BFs>20), which was lacking in single-fraction SBRT (BFs<3). Using the PTV encompassing dose or maximum (isocentric) dose, our data indicated a LQ-L transition dose (dT) at 11 Gy (68% CI 8-14 Gy) or 22 Gy (14-42 Gy), respectively. However, the fit of the LQ-L models was not significantly better than a fit without the dT parameter (p=0.07, BF=2.1 and p=0.86, BF=0.8, respectively). Generally, isocentric doses resulted in much better dose-response relationships than PTV encompassing doses (BFs>20).nnnCONCLUSIONnOur data suggest accurate modeling of local tumor control in fractionated SBRT for stage I NSCLC with the traditional LQ formalism.

Local tumor control probability modeling of primary and secondary lung tumors in stereotactic body radiotherapy

BACKGROUND AND PURPOSEnTo evaluate whether local tumor control probability (TCP) in stereotactic body radiotherapy (SBRT) varies between lung metastases of different primary cancer sites and between primary non-small cell lung cancer (NSCLC) and secondary lung tumors.nnnMATERIALS AND METHODSnA retrospective multi-institutional (n=22) database of 399 patients with stage I NSCLC and 397 patients with 525 lung metastases was analyzed. Irradiation doses were converted to biologically effective doses (BED). Logistic regression was used for local tumor control probability (TCP) modeling and the second-order bias corrected Akaike Information Criterion was used for model comparison.nnnRESULTSnAfter median follow-up of 19 months and 16 months (n.s.), local tumor control was observed in 87.7% and 86.7% of the primary and secondary lung tumors (n.s.), respectively. A strong dose-response relationship was observed in the primary NSCLC and metastatic cohort but dose-response relationships were not significantly different: the TCD90 (dose to achieve 90% TCP; BED of maximum planning target volume dose) estimates were 176 Gy (151-223) and 160 Gy (123-237) (n.s.), respectively. The dose-response relationship was not influenced by the primary cancer site within the metastatic cohort.nnnCONCLUSIONSnDose-response relationships for local tumor control in SBRT were not different between lung metastases of various primary cancer sites and between primary NSCLC and lung metastases.

Lack of a Dose-Effect Relationship for Pulmonary Function Changes After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

PURPOSEnTo evaluate the influence of tumor size, prescription dose, and dose to the lungs on posttreatment pulmonary function test (PFT) changes after stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC).nnnMETHODS AND MATERIALSnThe analysis is based on 191 patients treated at 5 international institutions: inclusion criteria were availability of pre- and post-SBRT PFTs and dose-volume histograms of the lung and planning target volume (PTV); patients treated with more than 1xa0SBRT course were excluded. Correlation between early (1-6 months, median 3 months) and late (7-24xa0months, median 12 months) PFT changes and tumor size, planning target volume (PTV) dose, and lung doses was assessed using linear regression analysis, receiver operating characteristics analysis, and Lymans normal tissue complication probability model. The PTV doses were converted to biologically effective doses and lung doses to 2 Gy equivalent doses before correlation analyses.nnnRESULTSnUp to 6 months after SBRT, forced expiratory volume in 1 second and carbon monoxide diffusion capacity changed by -1.4% (95% confidence interval [CI], -3.4% to 0) and -7.6% (95% CI, -10.2% to -3.4%) compared with pretreatment values, respectively. A modest decrease in PFTs was observed 7-24 months after SBRT, with changes of -8.1% (95% CI, -13.3% to -5.3%) and -12.4% (95% CI, -15.5% to -6.9%), respectively. Using linear regression analysis, receiver operating characteristic analysis, and normal tissue complication probability modeling, all evaluated parameters of tumor size, PTV dose, mean lung dose, and absolute and relative volumes of the lung exposed to minimum doses of 5-70 Gy were not correlated with early and late PFT changes. Subgroup analysis based on pre-SBRT PFTs (greater or equal and less than median) did not identify any dose-effect relationship.nnnCONCLUSIONSnThis study failed to demonstrate a significant dose-effect relationship for changesxa0of pulmonary function after SBRT for early-stage non-small cell lung cancer.

Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

BACKGROUNDnSeveral prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control.nnnMETHODS AND MATERIALSnWe investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection.nnnRESULTSnLocal failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED(ISO)) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED(ISO) and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED(ISO), age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively.nnnCONCLUSIONSnThese results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features, eventually helping in the task of personalized treatment planning.

Bayesian Cure Rate Modeling of Local Tumor Control: Evaluation in Stereotactic Body Radiation Therapy for Pulmonary Metastases

PURPOSEnMostxa0radiobiological models for prediction of tumor control probability (TCP) do not account for the fact that many events could remain unobserved because of censoring. We therefore evaluated a set of TCP models that take into account this censoring.nnnMETHODS AND MATERIALSnWe applied 2 fundamental Bayesian cure rate models to a sample of 770 pulmonary metastasis treated with stereotactic body radiation therapy at German, Austrian, and Swiss institutions: (1) the model developed by Chen, Ibrahim and Sinha (the CIS99 model); and (2) a mixture model similar to the classic model of Berkson and Gage (the BG model). In the CIS99 model the number of clonogens surviving the radiation treatment follows a Poisson distribution, whereas in the BG model only 1 dominant recurrence-competent tissue mass may remain. The dose delivered to the isocenter, tumor size and location, sex, age, and pretreatment chemotherapy were used as covariates for regression.nnnRESULTSnMean follow-up time was 15.5xa0months (range: 0.1-125). Tumor recurrence occurred in 11.6% of the metastases. Delivered dose, female sex, peripheral tumor location and having received no chemotherapy before RT were associated with higher TCP in all models. Parameter estimates of the CIS99 were consistent with the classical Cox proportional hazards model. The dose required to achieve 90% tumor control after 15.5xa0months was 146 (range: 114-188) Gy10 in the CIS99 and 133 (range: 101-164) Gy10 in the BG model; however, the BG model predicted lower tumor control at long (≳20 months) follow-up times and gave a suboptimal fit to the data compared to the CIS99 model.nnnCONCLUSIONSnBiologically motivated cure rate models allow adding the time component into TCP modeling without being restricted to the follow-up period which is the case for the Cox model. In practice, application of such models to the clinical setting could allow for adaption of treatment doses depending on whether local control should be achieved in the short or longer term.

Prediction of Early Death in Patients with Early-Stage NSCLC—Can We Select Patients without a Potential Benefit of SBRT as a Curative Treatment Approach?

Introduction: Stereotactic body radiotherapy (SBRT) is the guideline‐recommended treatment for medically inoperable patients with peripheral stage I non–small cell lung cancer (NSCLC). This study analyzed whether short‐term (<6 months) death can be predicted reliably to select a subgroup of patients who will not have a benefit from SBRT. Methods: A total of 779 patients with early‐stage NSCLC who had been treated with cone beam computed tomography–guided SBRT in five institutes and for whom information on overall survival during the first 6 months after treatment was available were included in this analysis. The probability of dying within 6 months after treatment was defined as the end point “early death” and modeled by multivariate logistic regression. Model fitting was performed using the least absolute shrinkage and selection operator method, and model test performance was estimated using double 10‐fold cross validation. The variables age, sex, Eastern Cooperative Oncology Group performance status, operability, forced expiratory volume in 1 second, and Charlson comorbidity index were considered for model building. Results: Eastern Cooperative Oncology Group performance status and (to a lesser extent) operability were the most important predictors of early death, whereas the Charlson comorbidity index was associated only with the overall survival time. On the basis of the best expected test performance (area under the curve = 0.699), the risk for early death would be 8.8% (range 8.2%–13.7%) and 4.1% (3.0%–4.3%) for the 10% of patients with the highest and lowest risk, respectively. Overall, predictive performance was too low for clinical application. Conclusions: SBRT should be offered to all patients irrespective of their comorbidities, unless the performance status of the patients and the comorbidities prevent accurate SBRT planning and delivery.

Radiobiological parameters of liver and lung metastases derived from tumor control data of 3719 metastases

BACKGROUND AND PURPOSEnThe radiobiological parameters for liver and lung metastases treated with stereotactic body radiation therapy (SBRT) are poorly defined. This project aimed at estimating these parameters from published tumor control probability (TCP) data, and separately for metastases with colorectal cancer (CRC) and non-CRC histology.nnnMATERIALS AND METHODSnA total of 62 studies with 89 different treatment prescriptions for a total of 3719 metastases were analyzed in a Bayesian framework using four different radiobiological models: The LQ, mLQ, LQ-L and the regrowth model which accounts for tumor regrowth after SBRT.nnnRESULTSnDepending on the particular model, α/β ratios in the range 13-23Gy for pulmonary metastases and 16-28Gy for hepatic metastases were estimated. For CRC metastases the estimated α/β ratio was 43.1±4.7Gy compared to 21.6±7.8Gy for non-CRC metastases. Typical isocenter dose prescriptions of 3×12Gy, 3×14.5Gy and 3×17Gy applied within 5days were predicted sufficient to control 90% of lung, liver and CRC metastases after 1yr, respectively.nnnCONCLUSIONSnα/β ratios for liver and lung metastases are higher than the usually assumed 10Gy. Differences between CRC and non-CRC histology were found. Future studies confirming these findings in individual patient data are needed.

An Evolutionary and Mechanistic Perspective on Dietary Carbohydrate Restriction in Cancer Prevention

Author(s): Fine, Eugene J. | Abstract: The confluence of basic cell biochemistry, epidemiological and anthropologic evidence points to high dietary carbohydrate and the associated disruption of the glucose-insulin axis as causes of the current increase in metabolic disorders, metabolic syndrome, hypertension and cardiovascular disease. This hyperinsulinemic state likely contributes, as well, to an increased mutagenic microenvironment, with increased risk for cancer. This critical review discusses these risks in their historical and evolutionary context. The evidence supports the benefits of lowering the glycemic load of the diet as a preventive measure against the development of cancer.

Application of Bayesian evidence synthesis to modelling the effect of ketogenic therapy on survival of high grade glioma patients

BackgroundKetogenic therapy in the form of ketogenic diets or calorie restriction has been proposed as a metabolic treatment of high grade glioma (HGG) brain tumors based on mechanistic reasoning obtained mainly from animal experiments. Given the paucity of clinical studies of this relatively new approach, our goal is to extrapolate evidence from the greater number of animal studies and synthesize it with the available human data in order to estimate the expected effects of ketogenic therapy on survival in HGG patients. At the same time we are using this analysis as an example for demonstrating how Bayesianism can be applied in the spirit of a circularxa0view of evidence.ResultsA Bayesian hierarchical model was developed. Data from three human cohort studies and 17 animal experiments were included to estimate the effects of four ketogenic interventions (calorie restriction/ketogenic diets as monotherapy/combination therapy) on the restricted mean survival time ratio in humans using various assumptions for the relationships between humans, rats and mice. The impact of different biological assumptions about the relevance of animal data for humans as well as external information based on mechanistic reasoning or case studies was evaluated by specifying appropriate priors. We provide statistical and philosophical arguments for why our approach is an improvement over existing (frequentist) methods for evidence synthesis as it is able to utilize evidence from a variety of sources. Depending on the prior assumptions, a 30–70% restricted mean survival time prolongation in HGG patients was predicted by the models. The highest probability of a benefit (>u200990%) for all four ketogenic interventions was obtained when adopting an enthusiastic prior based on previous case reports together with assuming synergism between ketogenic therapies with other forms of treatment. Combinations with other treatments were generally found more effective than ketogenic monotherapy.ConclusionsCombining evidence from both human and animal studies is statistically possible using a Bayesian approach. We found an overall survival-prolonging effect of ketogenic therapy in HGG patients. Our approach is best compatible with a circular instead of hierarchical view of evidence and easy to update once more data become available.

Stereotactic body radiotherapy (SBRT) for multiple pulmonary oligometastases: Analysis of number and timing of repeat SBRT as impact factors on treatment safety and efficacy

BACKGROUNDnStereotactic body radiotherapy (SBRT) for oligometastatic disease is characterized by an excellent safety profile; however, experiences are mostly based on treatment of one single metastasis. It was the aim of this study to evaluate safety and efficacy of SBRT for multiple pulmonary metastases.nnnPATIENTS AND METHODSnThis study is based on a retrospective database of the DEGRO stereotactic working group, consisting of 637 patients with 858 treatments. Cox regression and logistic regression were used to analyze the association between the number of SBRT treatments or the number and the timing of repeat SBRT courses with overall survival (OS) and the risk of early death.nnnRESULTSnOut of 637 patients, 145 patients were treated for multiple pulmonary metastases; 88 patients received all SBRT treatments within one month whereas 57 patients were treated with repeat SBRT separated by at least one month. Median OS for the total patient population was 23.5u202fmonths and OS was not significantly influenced by the overall number of SBRT treatments or the number and timing of repeat SBRT courses. The risk of early death within 3 and 6u202fmonths was not increased in patients treated with multiple SBRT treatments, and no grade 4 or grade 5 toxicity was observed in these patients.nnnCONCLUSIONSnIn appropriately selected patients, synchronous SBRT for multiple pulmonary oligometastases and repeat SBRT may have a comparable safety and efficacy profile compared to SBRT for one single oligometastasis.