Reporting and analyzing dose distributions: a concept of equivalent uniform dose

Dose-volume metrics and their relation to memory performance in pediatric brain tumor patients: A preliminary study

BACKGROUND

Advances in radiation treatment (RT), specifically volumetric planning with detailed dose and volumetric data for specific brain structures, have provided new opportunities to study neurobehavioral outcomes of RT in children treated for brain tumor. The present study examined the relationship between biophysical and physical dose metrics and neurocognitive ability, namely learning and memory, 2 years post-RT in pediatric brain tumor patients.

PROCEDURE

The sample consisted of 26 pediatric patients with brain tumor, 14 of whom completed neuropsychological evaluations on average 24 months post-RT. Prescribed dose and dose-volume metrics for specific brain regions were calculated including physical metrics (i.e., mean dose and maximum dose) and biophysical metrics (i.e., integral biological effective dose and generalized equivalent uniform dose). We examined the associations between dose-volume metrics (whole brain, right and left hippocampus), and performance on measures of learning and memory (Children's Memory Scale).

RESULTS

Biophysical dose metrics were highly correlated with the physical metric of mean dose but not with prescribed dose. Biophysical metrics and mean dose, but not prescribed dose, correlated with measures of learning and memory.

CONCLUSIONS

These preliminary findings call into question the value of prescribed dose for characterizing treatment intensity; they also suggest that biophysical dose has only a limited advantage compared to physical dose when calculated for specific regions of the brain. We discuss the implications of the findings for evaluating and understanding the relation between RT and neurocognitive functioning.

Dose escalation of radiotherapy in unresectable extrahepatic cholangiocarcinoma

Purpose

To evaluate the effect of escalated dose radiation therapy (EDR, defined as doses >50.4 Gy in 28 fractions [59.5 Gy BED]) on overall survival (OS), freedom from local progression (FFLP), and freedom from distant progression (FFDP) of patients with unresectable extrahepatic cholangiocarcinoma (EHCC).

Methods

A consecutive cohort of 80 patients who underwent radiotherapy for unresectable EHCC from 2001 to 2015 was identified. Demographic, tumor, treatment, toxicity, and laboratory variables were collected. The maximal RT doses ranged from 30 to 75 Gy (median 50.4 Gy, at 1.8‐4.5 Gy/fraction). Gross tumor volume (GTV) coverage by maximal dose in EDR group ranged from 38% to 100%. Kaplan–Meier method was used to estimate OS, FFLP, and FFDP. Univariate and multivariate Cox regression models were analyzed.

Results

After radiotherapy, median OS, FFLP, and FFDP were 18.7, 22.6, and 24.3 months, respectively. There was no significant difference in OS or FFLP between patients who received EDR to portions of the GTV and patients who did not. On multivariate analysis, bigger GTV, age, and ECOG performance status were independently associated with shorter OS. Local progression on chemotherapy prior to RT was independently associated with shorter FFLP. High baseline neutrophil/lymphocyte ratio (>5.3) was independently associated with shorter FFDP. Toxicity grades were similar in EDR and lower doses except lymphopenia which was higher in EDR (P = 0.053).

Conclusions

EDR to selective portions of the GTV may not benefit patients with unresectable EHCC despite having acceptable toxicity. New methods to improve local control and survival for unresectable EHCC are needed.

Proton therapy for low-grade gliomas in adults: A systematic review

For adult patients with diffuse low-grade glioma (LGG) proton therapy is an emerging radiotherapy modality. The number of proton facilities is rapidly increasing. However, there is a shortage of published data concerning the clinical effectiveness compared to photon radiotherapy and potential proton-specific toxicity. This study aimed to systematically review and summarize the relevant literature on proton therapy for adult LGG patients, including dosimetric comparisons, the type and frequency of acute and long-term toxicity and the clinical effectiveness. A systematic search was performed in several medical databases and 601 articles were screened for relevance. Nine articles were deemed eligible for in-depth analysis using a standardized data collection form by two independent researchers. Proton treatment plans compared favorably to photon-plans regarding dose to uninvolved neural tissue. Fatigue (27-100%), alopecia (37-85%), local erythema (78-85%) and headache (27-75%) were among the most common acute toxicities. One study reported no significant long-term cognitive impairments. Limited data was available on long-term survival. One study reported a 5-year overall survival of 84% and 5-year progression-free survival of 40%. We conclude that published data from clinical studies using proton therapy for adults with LGG are scarce. As the technique becomes more available, controlled clinical studies are urgently warranted to determine if the potential benefits based on comparative treatment planning translate into clinical benefits.

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients' clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited.

DVH Analytics: A DVH database for clinicians and researchers

In this study, we build a vendor-agnostic software application capable of importing and analyzing non-image-based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose-volume histogram (DVH) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVHs, communicating with a PostgreSQL database, performing statistical analyses, and creating a web-based user interface. This software is open-source and compatible with Windows, Mac OS, and Linux. For proof-of-concept, a database with over 3,000 DVHs from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs-at-risk (OARs) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume (PTV) and treatment beam source-to-skin distance (SSD). With DVH Analytics, we have developed a free, open-source software program to parse, organize, and analyze non-image-based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.

Development of a normal tissue complication probability model for late unfavourable aesthetic outcome after breast-conserving therapy

PURPOSE/OBJECTIVES

To develop a normal tissue complication probability (NTCP) model for late unfavourable aesthetic outcome (AO) after breast-conserving therapy.

MATERIAL AND METHODS

The BCCT.core software evaluated the AO using standardized photographs of patients treated between 2009 and 2014. Dose maps in 2 Gy equivalents were calculated assuming α/β = 3.6 Gy. Uni- and multivariable logistic regression analysis was performed to study the predictive value of clinicopathological and dosimetric variables for unfavourable AO. The Lyman Kutcher Burman (LKB) model was fit to the data with dose modifying factors (dmf). Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristic curve and bootstrap sampling.

RESULTS

Forty-four of the 121 analysed patients (36%) developed unfavourable AO. In the optimal multivariable logistic regression model, a larger breast volume receiving ≥55 Gy (V55), a seroma and an axillary lymph node dissection (ALND) were independently associated with an unfavourable AO, AUC = 0.75 (95%CI 0.64;0.85). Beta-estimates were -2.68 for β₀, 0.057 for V55, 1.55 for seroma and 1.20 for ALND. The optimal LKB model parameters were EUD_3.6(50) = 63.3 Gy, n = 1.00, m = 0.23, dmf(seroma) = 0.83 and dmf(ALND) = 0.84, AUC = 0.74 (95%CI 0.61;0.83).

CONCLUSIONS

An NTCP model for late unfavourable AO after breast-conserving therapy was developed including seroma, axillary lymphadenectomy and V55.

An evaluation method of clinical impact with setup, range, and radiosensitivity uncertainties in fractionated carbon-ion therapy

In light ion therapy, the dose concentration is highly sensitive to setup and range errors. Here we propose a method for evaluating the effects of these errors by using the correlation between fractions on tumour control probability (TCP) in carbon-ion therapy. This method incorporates the concept of equivalent stochastic dose (Cranmer-Sargison and Zavgorodni 2005 Phys. Med. Biol. 50 4097-109), which was defined as a dose that gives the mean expected survival fraction (SF) for the stochastically variable dose. The mean expected SFs were calculated while considering the correlation between fractions for setup and range errors. By using this SF, equivalent stochastic clinical doses (ESCD), which are weighted by relative biological effectiveness, of lung and prostate cases with varying errors were derived. To account for spatial dose heterogeneity, equivalent uniform stochastic clinical doses (EUSCD) were obtained by using the mean expected SF in the volume of interest. TCP curves were calculated for each assumed error considering inter-patient sensitivity variation with a fractionation effect. ESCD distributions, EUSCD, and TCP curves were affected by the inter-fraction correlation and the contribution of setup and range errors. Irradiated areas that could be affected by these errors can be visualized quantitatively by using the ESCD distribution. TCP curves for the errors of various conditions converged around the TCP curve in nominal conditions by using the EUSCD. EUSCD correlated well with TCP in setup and range errors when the errors were not large and was comparatively stably insensitive to uncertain biological parameters. The proposed evaluation method with EUSCD and TCP calculations will be useful to indicate tumour doses to improve realistic dose distributions in carbon-ion therapy.

A continuous arc delivery optimization algorithm for CyberKnife m6

PURPOSE

This study aims to reduce the delivery time of CyberKnife m6 treatments by allowing for noncoplanar continuous arc delivery. To achieve this, a novel noncoplanar continuous arc delivery optimization algorithm was developed for the CyberKnife m6 treatment system (CyberArc-m6).

METHODS AND MATERIALS

CyberArc-m6 uses a five-step overarching strategy, in which an initial set of beam geometries is determined, the robotic delivery path is calculated, direct aperture optimization is conducted, intermediate MLC configurations are extracted, and the final beam weights are computed for the continuous arc radiation source model. This algorithm was implemented on five prostate and three brain patients, previously planned using a conventional step-and-shoot CyberKnife m6 delivery technique. The dosimetric quality of the CyberArc-m6 plans was assessed using locally confined mutual information (LCMI), conformity index (CI), heterogeneity index (HI), and a variety of common clinical dosimetric objectives.

RESULTS

Using conservative optimization tuning parameters, CyberArc-m6 plans were able to achieve an average CI difference of 0.036 ± 0.025, an average HI difference of 0.046 ± 0.038, and an average LCMI of 0.920 ± 0.030 compared with the original CyberKnife m6 plans. Including a 5 s per minute image alignment time and a 5-min setup time, conservative CyberArc-m6 plans achieved an average treatment delivery speed up of 1.545x ± 0.305x compared with step-and-shoot plans.

CONCLUSIONS

The CyberArc-m6 algorithm was able to achieve dosimetrically similar plans compared to their step-and-shoot CyberKnife m6 counterparts, while simultaneously reducing treatment delivery times.

The effect of time interval between radiotherapy and hyperthermia on planned equivalent radiation dose

PURPOSE

Thermoradiotherapy is an effective treatment for locally advanced cervical cancer. However, the optimal time interval between radiotherapy and hyperthermia, resulting in the highest therapeutic gain, remains unclear. This study aims to evaluate the effect of time interval on the therapeutic gain using biological treatment planning.

METHODS

Radiotherapy and hyperthermia treatment plans were created for 15 cervical cancer patients. Biological modeling was used to calculate the equivalent radiation dose, that is, the radiation dose that results in the same biological effect as the thermoradiotherapy treatment, for different time intervals ranging from 0-4 h. Subsequently, the thermal enhancement ratio (TER, i.e. the ratio of the dose for the thermoradiotherapy and the radiotherapy-only plan) was calculated for the gross tumor volume (GTV) and the organs at risk (OARs: bladder, rectum, bowel), for each time interval. Finally, the therapeutic gain factor (TGF, i.e. TER_GTV/TER_OAR) was calculated for each OAR.

RESULTS

The median TER_GTV ranged from 1.05 to 1.16 for 4 h and 0 h time interval, respectively. Similarly, for bladder, rectum and bowel, TER_OARs ranged from 1-1.03, 1-1.04 and 1-1.03, respectively. Radiosensitization in the OARs was much less than in the GTV, because temperatures were lower, fractionation sensitivity was higher (lower α/β) and direct cytotoxicity was assumed negligible in normal tissue. TGFs for the three OARs were similar, and were highest (around 1.12) at 0 h time interval.

CONCLUSION

This planning study indicates that the largest therapeutic gain for thermoradiotherapy in cervical cancer patients can be obtained when hyperthermia is delivered immediately before or after radiotherapy.

Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer

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

Automated VMAT planning for postoperative adjuvant treatment of advanced gastric cancer

Background

Postoperative/adjuvant radiotherapy of advanced gastric cancer involves a large planning target volume (PTV) with multi-concave shapes which presents a challenge for volumetric modulated arc therapy (VMAT) planning. This study investigates the advantages of automated VMAT planning for this site compared to manual VMAT planning by expert planners.

Methods

For 20 gastric cancer patients in the postoperative/adjuvant setting, dual-arc VMAT plans were generated using fully automated multi-criterial treatment planning (autoVMAT), and compared to manually generated VMAT plans (manVMAT). Both automated and manual plans were created to deliver a median dose of 45 Gy to the PTV using identical planning and segmentation parameters. Plans were evaluated by two expert radiation oncologists for clinical acceptability. AutoVMAT and manVMAT plans were also compared based on dose-volume histogram (DVH) and predicted normal tissue complication probability (NTCP) analysis.

Results

Both manVMAT and autoVMAT plans were considered clinically acceptable. Target coverage was similar (manVMAT: 96.6 ± 1.6%, autoVMAT: 97.4 ± 1.0%, p = 0.085). With autoVMAT, median kidney dose was reduced on average by > 25%; (for left kidney from 11.3 ± 2.1 Gy to 8.9 ± 3.5 Gy (p = 0.002); for right kidney from 9.2 ± 2.2 Gy to 6.1 ± 1.3 Gy (p <  0.001)). Median dose to the liver was lower as well (18.8 ± 2.3 Gy vs. 17.1 ± 3.6 Gy, p = 0.048). In addition, Dmax of the spinal cord was significantly reduced (38.3 ± 3.7 Gy vs. 31.6 ± 2.6 Gy, p <  0.001). Substantial improvements in dose conformity and integral dose were achieved with autoVMAT plans (4.2% and 9.1%, respectively; p <  0.001). Due to the better OAR sparing in the autoVMAT plans compared to manVMAT plans, the predicted NTCPs for the left and right kidney and the liver-PTV were significantly reduced by 11.3%, 12.8%, 7%, respectively (p ≤ 0.001). Delivery time and total number of monitor units were increased in autoVMAT plans (from 168 ± 19 s to 207 ± 26 s, p = 0.006) and (from 781 ± 168 MU to 1001 ± 134 MU, p = 0.003), respectively.

Conclusions

For postoperative/adjuvant radiotherapy of advanced gastric cancer, involving a complex target shape, automated VMAT planning is feasible and can substantially reduce the dose to the kidneys and the liver, without compromising the target dose delivery.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1032-z) contains supplementary material, which is available to authorized users.

Radiobiological Models of Tissue Response to Radiation in Treatment Planning Systems

Aims

To present several biological concepts and models of tissue response to fractionated radiotherapy. To describe practical implementation of these models in three-dimensional treatment planning systems.

Methods

Models of cell survival, Equivalent Uniform Dose (EUD) and Tumor Control Probability (TCP) are discussed. These models are based on the target-cell hypothesis which assumes that response of organs and tissues to radiation therapy can be explained and mathematically described in terms of survival of the specific target-cells.

Results

Several formulae for deriving and calculating EUD and TCP for a given three-dimensional dose distribution are presented and discussed.

Conclusions

Biological models of tissue response to radiation, when used wisely, have a potential to be useful in radiation therapy treatment planning. The models can advance our understanding of the underlying biological mechanisms, and may help in designing new and better treatment strategies. They should be particularly useful in modern conformai radiotherapy where treatment strategy for each patient can be individualized and optimized according to patient characteristics and available technology of delivering sophisticated treatment plans.

Dosimetric and Radiobiological Evaluation of Patient Setup Accuracy in Head-and-neck Radiotherapy Using Daily Computed Tomography-on-rails-based Corrections

Introduction:

This study evaluates treatment plans aiming at determining the expected impact of daily patient setup corrections on the delivered dose distribution and plan parameters in head-and-neck radiotherapy.

Materials and Methods:

In this study, 10 head-and-neck cancer patients are evaluated. For the evaluation of daily changes of the patient internal anatomy, image-guided radiation therapy based on computed tomography (CT)-on-rails was used. The daily-acquired CT-on-rails images were deformedly registered to the CT scan that was used during treatment planning. Two approaches were used during data analysis (“cascade” and “one-to-all”). The dosimetric and radiobiological differences of the dose distributions with and without patient setup correction were calculated. The evaluation is performed using dose–volume histograms; the biologically effective uniform dose () and the complication-free tumor control probability (P₊) were also calculated. The dose–response curves of each target and organ at risk (OAR), as well as the corresponding P₊ curves, were calculated.

Results:

The average difference for the “one-to-all” case is 0.6 ± 1.8 Gy and for the “cascade” case is 0.5 ± 1.8 Gy. The value of P₊ was lowest for the cascade case (in 80% of the patients).

Discussion:

Overall, the lowest P_I is observed in the one-to-all cases. Dosimetrically, CT-on-rails data are not worse or better than the planned data.

Conclusions:

The differences between the evaluated “one-to-all” and “cascade” dose distributions were small. Although the differences of those doses against the “planned” dose distributions were small for the majority of the patients, they were large for given patients at risk and OAR.

Dosimetrical and radiobiological approach to manage the dosimetric shift in the transition of dose calculation algorithm in radiation oncology: how to improve high quality treatment and avoid unexpected outcomes?

BACKGROUND

For a given prescribed dose of radiotherapy, with the successive generations of dose calculation algorithms, more monitor units (MUs) are generally needed. This is due to the implementation of successive improvements in dose calculation: better heterogeneity correction and more accurate estimation of secondary electron transport contribution. More recently, there is the possibility to report the dose-to-medium, physically more accurate compared to the dose-to-water as the reference one. This last point is a recent concern and the main focus of this study.

METHODS

In this paper, we propose steps for a general analysis procedure to estimate the dosimetric alterations, and the potential clinical changes, between a reference algorithm and a new one. This includes dosimetric parameters, gamma index, radiobiology indices based on equivalent uniform dose concept and statistics with bootstrap simulation. Finally, we provide a general recommendation on the clinical use of new algorithms regarding the dose prescription or dose limits to the organs at risks.

RESULTS

The dosimetrical and radiobiological data showed a significant effect, which might exceed 5-10%, of the calculation method on the dose the distribution and clinical outcomes for lung cancer patients. Wilcoxon signed rank paired comparisons indicated that the delivered dose in MUs was significantly increased (> 2%) using more advanced dose calculation methods as compared to the reference one.

CONCLUSION

This paper illustrates and explains the use of dosimetrical, radiobiologcal and statistical tests for dosimetric comparisons in radiotherapy. The change of dose calculation algorithm may induce a dosimetric shift, which has to be evaluated by the physicists and the oncologists. This includes the impact on tumor control and on the risk of toxicity based on normal tissue dose constraints. In fact, the alteration in dose distribution makes it hard to keep exactly the same tumor control probability along with the same normal tissue complication probability.

Inter-institutional analysis demonstrates the importance of lower than previously anticipated dose regions to prevent late rectal bleeding following prostate radiotherapy

PURPOSE

To investigate whether inter-institutional cohort analysis uncovers more reliable dose-response relationships exemplified for late rectal bleeding (LRB) following prostate radiotherapy.

MATERIAL AND METHODS

Data from five institutions were used. Rectal dose-volume histograms (DVHs) for 989 patients treated with 3DCRT or IMRT to 70-86.4 Gy@1.8-2.0 Gy/fraction were obtained, and corrected for fractionation effects (α/β = 3 Gy). Cohorts with best-fit Lyman-Kutcher-Burman volume-effect parameter a were pooled after calibration adjustments of the available LRB definitions. In the pooled cohort, dose-response modeling (incorporating rectal dose and geometry, and patient characteristics) was conducted on a training cohort (70%) followed by final testing on the remaining 30%. Multivariate logistic regression was performed to build models with bootstrap stability.

RESULTS

Two cohorts with low bleeding rates (2%) were judged to be inconsistent with the remaining data, and were excluded. In the remaining pooled cohorts (n = 690; LRB rate = 12%), an optimal model was generated for 3DCRT using the minimum rectal dose and the absolute rectal volume receiving less than 55 Gy (AUC = 0.67; p = 0.0002; Hosmer-Lemeshow p-value, pHL = 0.59). The model performed nearly as well in the hold-out testing data (AUC = 0.71; p < 0.0001; pHL = 0.63), indicating a logistically shaped dose-response.

CONCLUSION

We have demonstrated the importance of integrating datasets from multiple institutions, thereby reducing the impact of intra-institutional dose-volume parameters explicitly correlated with prescription dose levels. This uncovered an unexpected emphasis on sparing of the low to intermediate rectal dose range in the etiology of late rectal bleeding following prostate radiotherapy.

Intensity-modulated radiation therapy: a review with a physics perspective

Intensity-modulated radiation therapy (IMRT) has been considered the most successful development in radiation oncology since the introduction of computed tomography into treatment planning that enabled three-dimensional conformal radiotherapy in 1980s. More than three decades have passed since the concept of inverse planning was first introduced in 1982, and IMRT has become the most important and common modality in radiation therapy. This review will present developments in inverse IMRT treatment planning and IMRT delivery using multileaf collimators, along with the associated key concepts. Other relevant issues and future perspectives are also presented.

Integration of radiobiological modeling and indices in comparative plan evaluation: A study comparing VMAT and 3D-CRT in patients with NSCLC

PURPOSE

The purpose of this article was to generate an algorithm to calculate radiobiological endpoints and composite indices and use them to compare volumetric modulated arc therapy (VMAT) and 3-dimensional conformal radiation therapy (3D-CRT) techniques in patients with locally advanced non-small cell lung cancer.

METHODS AND MATERIALS

The study included 25 patients with locally advanced non-small cell lung cancer treated with 3D-CRT at our center between January 1, 2010, and December 31, 2014. The planner generated VMAT plans using clones of the original computed tomography scans and regions of interest volumes, which did not include the original 3D plans. Both 3D-CRT and VMAT plans were generated using the same dose-volume constraint worksheet. The dose-volume histogram parameters for planning target volume and relevant organs at risk (OAR) were reviewed. The calculation engine was written in the R programming language; the user interface was developed with the "shiny" R Web library. Dose-volume histogram data were imported into the calculation engine and tumor control probability (TCP), normal tissue complication probability (NTCP), composite cardiopulmonary toxicity index (CPTI), morbidity index: MI = ∑_j = 1^{#ofrelevantOARs}(w_j ∗ NTCP_j), uncomplicated TCP (UTCP=TCP∗∏k=1#ofOARs1-NTCP_K100, and therapeutic gain (TG): ie, TG = TCP ∗ (100 - MI) were calculated.

RESULTS

TCP was better with 3D-CRT (12.62% vs 11.71%, P < .001), whereas VMAT demonstrated superior NTCP esophagus (4.45% vs 7.39%, P = .02). NTCP spinal cord (0.001% vs 0.009%, P = .001), and NTCP heart/perfusion defect (44.57% vs 56.42%, P = .016). There was no difference in NTCP lung (6.27% vs 7.62%, P = .221) and NTCP heart/pericarditis (0.001% vs 0.15%, P = .129) between 2 techniques. VMAT showed substantial improvement in morbidity index (11.06% vs. 14.31%, P = 0.01), CPTI (47.59% vs 59.41%, P = .03), TG (P = .035), and trend toward superiority in UTCP (5.89 vs 4.75, P=.057).

CONCLUSION

The study highlights the utility of the radiobiological algorithm and summary indices in comparative plan evaluation and demonstrates benefits of VMAT over 3D-CRT.

Comparison of the average surviving fraction model with the integral biologically effective dose model for an optimal irradiation scheme

In this paper, we compare two radiation effect models: the average surviving fraction (ASF) model and the integral biologically effective dose (IBED) model for deriving the optimal irradiation scheme and show the superiority of ASF. Minimizing the effect on an organ at risk (OAR) is important in radiotherapy. The biologically effective dose (BED) model is widely used to estimate the effect on the tumor or on the OAR, for a fixed value of dose. However, this is not always appropriate because the dose is not a single value but is distributed. The IBED and ASF models are proposed under the assumption that the irradiation is distributed. Although the IBED and ASF models are essentially equivalent for deriving the optimal irradiation scheme in the case of uniform distribution, they are not equivalent in the case of non-uniform distribution. We evaluate the differences between them for two types of cancers: high α/β ratio cancer (e.g. lung) and low α/β ratio cancer (e.g. prostate), and for various distributions i.e. various dose-volume histograms. When we adopt the IBED model, the optimal number of fractions for low α/β ratio cancers is reasonable, but for high α/β ratio cancers or for some DVHs it is extremely large. However, for the ASF model, the results keep within the range used in clinical practice for both low and high α/β ratio cancers and for most DVHs. These results indicate that the ASF model is more robust for constructing the optimal irradiation regimen than the IBED model.

Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy

Modern radiotherapy technologies such as proton beam therapy (PBT) permit dose escalation to the tumour and minimize unnecessary doses to normal tissues. To achieve appropriate patient selection for PBT, a normal tissue complication probability (NTCP) model can be applied to estimate the risk of treatment-related toxicity relative to X-ray therapy (XRT). A methodology for estimating the difference in NTCP (∆NTCP), including its uncertainty as a function of dose to normal tissue, is described in this study using the Delta method, a statistical method for evaluating the variance of functions, considering the variance-covariance matrix. We used a virtual individual patient dataset of radiation-induced liver disease (RILD) in liver tumour patients who were treated with XRT as a study model. As an alternative option for individual patient data, dose-bin data, which consists of the number of patients who developed toxicity in each dose level/bin and the total number of patients in that dose level/bin, are useful for multi-institutional data sharing. It provides comparable accuracy with individual patient data when using the Delta method. With reliable NTCP models, the ∆NTCP with uncertainty might potentially guide the use of PBT; however, clinical validation and a cost-effectiveness study are needed to determine the appropriate ∆NTCP threshold.

Selection of external beam radiotherapy approaches for precise and accurate cancer treatment

Physically precise external-beam radiotherapy (EBRT) technologies may not translate to the best outcome in individual patients. On the other hand, clinical considerations alone are often insufficient to guide the selection of a specific EBRT approach in patients. We examine the ways in which to compare different EBRT approaches based on physical, biological and clinical considerations, and how they can be enhanced with the addition of biophysical models and machine-learning strategies. The process of selecting an EBRT modality is expected to improve in tandem with knowledge-based treatment planning.

Using gEUD based plan analysis method to evaluate proton vs. photon plans for lung cancer radiation therapy

The goal of this study was to exam the efficacy of current DVH based clinical guidelines draw from photon experience for lung cancer radiation therapy on proton therapy. Comparison proton plans and IMRT plans were generated for 10 lung patients treated in our proton facility. A gEUD based plan evaluation method was developed for plan evaluation. This evaluation method used normal lung gEUD(a) curve in which the model parameter "a" was sampled from the literature reported value. For all patients, the proton plans delivered lower normal lung V5 Gy with similar V20 Gy and similar target coverage. Based on current clinical guidelines, proton plans were ranked superior to IMRT plans for all 10 patients. However, the proton and IMRT normal lung gEUD(a) curves crossed for 8 patients within the tested range of "a", which means there was a possibility that proton plan would be worse than IMRT plan for lung sparing. A concept of deficiency index (DI) was introduced to quantify the probability of proton plans doing worse than IMRT plans. By applying threshold on DI, four patients' proton plan was ranked inferior to the IMRT plan. Meanwhile if a threshold to the location of curve crossing was applied, 6 patients' proton plan was ranked inferior to the IMRT plan. The contradictory ranking results between the current clinical guidelines and the gEUD(a) curve analysis demonstrated there is potential pitfalls by applying photon experience directly to the proton world. A comprehensive plan evaluation based on radio-biological models should be carried out to decide if a lung patient would really be benefit from proton therapy.

Intensity-modulated radiation therapy versus volumetric-modulated arc therapy in non-small cell lung cancer: assessing the risk of radiation pneumonitis

Purpose

This study aimed to compare intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) regarding plan quality and healthy lung sparing, in stage III non-small cell lung cancer (NSCLC) patients.

Materials and methods

The plans of 60 patients were allocated either to the IMRT (n=30) or the VMAT (n=30) group. The dose prescribed to the planning target volume (PTV) was evaluated at the 95% level and the mean lung dose (MLD) and the healthy lung receiving 5, 10 and 20 Gy (V5, V10and V20, respectively) were analysed. The normal tissue complication probability (NTCP) for radiation pneumonitis was calculated with the Lyman–Kutcher–Burman model.

Results

Both techniques achieved comparable results for target coverage (V95%=97·87 versus 97·18%,p>0·05) and homogeneity. The MLD (15·57 versus 16·98 Gy,p>0·05), V5(60·35 versus 67·25%,p>0·05) and V10(45·22 versus 53·14%,p=0·011) were lower for IMRT, whereas VMAT reduced V20(26·44 versus 25·90%,p>0·05). The NTCP for radiation pneumonitis was higher for VMAT, but no statistical significance was observed (11·07 versus 12·75,p>0·05).

Conclusion

Both techniques seemed suitable for NSCLC treatment, but IMRT presented better results regarding lung sparing thus being beneficial in reducing the risk of radiation-induced pneumonitis.

Present developments in reaching an international consensus for a model-based approach to particle beam therapy

Particle beam therapy (PBT), including proton and carbon ion therapy, is an emerging innovative treatment for cancer patients. Due to the high cost of and limited access to treatment, meticulous selection of patients who would benefit most from PBT, when compared with standard X-ray therapy (XRT), is necessary. Due to the cost and labor involved in randomized controlled trials, the model-based approach (MBA) is used as an alternative means of establishing scientific evidence in medicine, and it can be improved continuously. Good databases and reasonable models are crucial for the reliability of this approach. The tumor control probability and normal tissue complication probability models are good illustrations of the advantages of PBT, but pre-existing NTCP models have been derived from historical patient treatments from the XRT era. This highlights the necessity of prospectively analyzing specific treatment-related toxicities in order to develop PBT-compatible models. An international consensus has been reached at the Global Institution for Collaborative Research and Education (GI-CoRE) joint symposium, concluding that a systematically developed model is required for model accuracy and performance. Six important steps that need to be observed in these considerations include patient selection, treatment planning, beam delivery, dose verification, response assessment, and data analysis. Advanced technologies in radiotherapy and computer science can be integrated to improve the efficacy of a treatment. Model validation and appropriately defined thresholds in a cost-effectiveness centered manner, together with quality assurance in the treatment planning, have to be achieved prior to clinical implementation.

Mathematical Modelling for Patient Selection in Proton Therapy

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

Derivation of mean dose tolerances for new fractionation schemes and treatment modalities

Avoiding toxicities in radiotherapy requires the knowledge of tolerable organ doses. For new, experimental fractionation schemes (e.g. hypofractionation) these are typically derived from traditional schedules using the biologically effective dose (BED) model. In this report we investigate the difficulties of establishing mean dose tolerances that arise since the mean BED depends on the entire spatial dose distribution, rather than on the dose level alone. A formula has been derived to establish mean physical dose constraints such that they are mean BED equivalent to a reference treatment scheme. This formula constitutes a modified BED equation where the influence of the spatial dose distribution is summarized in a single parameter, the dose shape factor. To quantify effects we analyzed 24 liver cancer patients for whom both proton and photon IMRT treatment plans were available. The results show that the standard BED equation-neglecting the spatial dose distribution-can overestimate mean dose tolerances for hypofractionated treatments by up to 20%. The shape difference between photon and proton dose distributions can cause 30-40% differences in mean physical dose for plans having identical mean BEDs. Converting hypofractionated, 5/15-fraction proton doses to mean BED equivalent photon doses in traditional 35-fraction regimens resulted in up to 10 Gy higher doses than applying the standard BED formula. The dose shape effect should be accounted for to avoid overestimation of mean dose tolerances, particularly when estimating constraints for hypofractionated regimens. Additionally, tolerances established for one treatment modality cannot necessarily be applied to other modalities with drastically different dose distributions, such as proton therapy. Last, protons may only allow marginal (5-10%) dose escalation if a fraction-size adjusted organ mean dose is constraining instead of a physical dose.

Head and Neck Tumor Control Probability: Radiation Dose-Volume Effects in Stereotactic Body Radiation Therapy for Locally Recurrent Previously-Irradiated Head and Neck Cancer: Report of the AAPM Working Group

PURPOSE

Stereotactic body radiation therapy (SBRT) has emerged as a viable reirradiation strategy for locally recurrent previously-irradiated head and neck cancer. Doses in the literature have varied, which challenges clinical application of SBRT as well as clinical trial design.

MATERIAL & METHODS

A working group was formed through the American Association of Physicists in Medicine to study tumor control probabilities for SBRT in head and neck cancer. We herein present a systematic review of the available literature addressing the dose/volume data for tumor control probability with SBRT in patients with locally recurrent previously-irradiated head and neck cancer. Dose-response models are generated that present tumor control probability as a function of dose.

RESULTS

Data from more than 300 cases in 8 publications suggest that there is a dose-response relationship, with superior local control and possibly improved overall survival for doses of 35 to 45 Gy (in 5 fractions) compared with <30 Gy.

CONCLUSION

Stereotactic body radiation therapy doses equivalent to 5-fraction doses of 40 to 50 Gy are suggested for retreatment.

Automated inverse optimization facilitates lower doses to normal tissue in pancreatic stereotactic body radiotherapy

Purpose

Inverse planning is trial-and-error iterative process. This work introduces a fully automated inverse optimization approach, where the treatment plan is closely tailored to the unique patient anatomy. The auto-optimization is applied to pancreatic stereotactic body radiotherapy (SBRT).

Materials and methods

The automation is based on stepwise reduction of dose-volume histograms (DVHs). Five uniformly spaced points, from 1% to 70% of the organ at risk (OAR) volumes, are used. Doses to those DVH points are iteratively decreased through multiple optimization runs. With each optimization run the doses to the OARs are decreased, while the dose homogeneity over the target is increased. The iterative process is terminated when a pre-specified dose heterogeneity over the target is reached. Twelve pancreatic cases were retrospectively studied. Doses to the target, maximum doses to duodenum, bowel, stomach, and spinal cord were evaluated. In addition, mean doses to liver and kidneys were tallied. The auto-optimized plans were compared to the actual treatment plans, which are based on national protocols.

Results

The prescription dose to 95% of the planning target volume (PTV) is the same for the treatment and the auto-optimized plans. The average difference for maximum doses to duodenum, bowel, stomach, and spinal cord are -4.6 Gy, -1.8 Gy, -1.6 Gy, and -2.4 Gy respectively. The negative sign indicates lower doses with the auto-optimization. The average differences in the mean doses to liver and kidneys are -0.6 Gy, and -1.1 Gy to -1.5 Gy respectively.

Conclusions

Automated inverse optimization holds great potential for personalization and tailoring of radiotherapy to particular patient anatomies. It can be utilized for normal tissue sparing or for an isotoxic dose escalation.

Differences in lung injury after IMRT or proton therapy assessed by 18FDG PET imaging

BACKGROUND AND PURPOSE

To compare lung injury among non-small cell lung cancer (NSCLC) patients treated with IMRT or proton therapy as revealed by ¹⁸F-FDG post-treatment uptake and to determine factors predictive for clinically symptomatic radiation pneumonitis.

MATERIAL AND METHODS

For 83 patients treated with IMRT or proton therapy, planning CT and follow up ¹⁸F-FDG PET-CT were analyzed. Post-treatment PET-CT was aligned with planning CT to establish a voxel-to-voxel correspondence between PET and planning dose images. ¹⁸F-FDG uptake as a function of radiation dose to normal lung was obtained for each patient. PET image-derived parameters as well as demographic, clinical, treatment and dosimetric patient characteristics were correlated with clinical symptoms of pneumonitis.

RESULTS

The dose distributions for the two modalities were significantly different; V5 was higher for IMRT, whereas V60 was higher for protons. The mean lung dose (MLD) was similar for the two modalities. The slope of linear ¹⁸F-FDG-uptake - dose response did not differ significantly between the two modalities. The MLD, slope, and 95th percentile of SUV were identified as three major factors associated with radiation pneumonitis.

CONCLUSIONS

Despite significantly different dose distributions for IMRT and for protons, the slope of the SUV-dose linear regression line previously shown to be associated with RP did not differ between IMRT and protons. Patients who developed radiation pneumonitis had statistically significantly higher MLD and higher slope regardless of treatment modality.

Radiobiological Optimization in Lung Stereotactic Body Radiation Therapy: Are We Ready to Apply Radiobiological Models?

Lung tumors are often associated with a poor prognosis although different schedules and treatment modalities have been extensively tested in the clinical practice. The complexity of this disease and the use of combined therapeutic approaches have been investigated and the use of high dose-rates is emerging as effective strategy. Technological improvements of clinical linear accelerators allow combining high dose-rate and a more conformal dose delivery with accurate imaging modalities pre- and during therapy. This paper aims at reporting the state of the art and future direction in the use of radiobiological models and radiobiological-based optimizations in the clinical practice for the treatment of lung cancer. To address this issue, a search was carried out on PubMed database to identify potential papers reporting tumor control probability and normal tissue complication probability for lung tumors. Full articles were retrieved when the abstract was considered relevant, and only papers published in English language were considered. The bibliographies of retrieved papers were also searched and relevant articles included. At the state of the art, dose–response relationships have been reported in literature for local tumor control and survival in stage III non-small cell lung cancer. Due to the lack of published radiobiological models for SBRT, several authors used dose constraints and models derived for conventional fractionation schemes. Recently, several radiobiological models and parameters for SBRT have been published and could be used in prospective trials although external validations are recommended to improve the robustness of model predictive capability. Moreover, radiobiological-based functions have been used within treatment planning systems for plan optimization but the advantages of using this strategy in the clinical practice are still under discussion. Future research should be directed toward combined regimens, in order to potentially improve both local tumor control and survival. Indeed, accurate knowledge of the relevant parameters describing tumor biology and normal tissue response is mandatory to correctly address this issue. In this context, the role of medical physicists and the AAPM in the development of radiobiological models is crucial for the progress of developing specific tool for radiobiological-based optimization treatment planning.

On the Inclusion of Short-distance Bystander Effects into a Logistic Tumor Control Probability Model

Currently, interactions between voxels are neglected in the tumor control probability (TCP) models used in biologically-driven intensity-modulated radiotherapy treatment planning. However, experimental data suggests that this may not always be justified when bystander effects are important. We propose a model inspired by the Ising model, a short-range interaction model, to investigate if and when it is important to include voxel to voxel interactions in biologically-driven treatment planning. This Ising-like model for TCP is derived by first showing that the logistic model of tumor control is mathematically equivalent to a non-interacting Ising model. Using this correspondence, the parameters of the logistic model are mapped to the parameters of an Ising-like model and bystander interactions are introduced as a short-range interaction as is the case for the Ising model. As an example, we apply the model to study the effect of bystander interactions in the case of radiation therapy for prostate cancer. The model shows that it is adequate to neglect bystander interactions for dose distributions that completely cover the treatment target and yield TCP estimates that lie in the shoulder of the dose response curve. However, for dose distributions that yield TCP estimates that lie on the steep part of the dose response curve or for inhomogeneous dose distributions having significant hot and/or cold regions, bystander effects may be important. Furthermore, the proposed model highlights a previously unexplored and potentially fruitful connection between the fields of statistical mechanics and tumor control probability/normal tissue complication probability modeling.

Re-irradiation for recurrent glioma- the NCI experience in tumor control, OAR toxicity and proposal of a novel prognostic scoring system

Purpose/objectives

Despite mounting evidence for the use of re-irradiation (re-RT) in recurrent high grade glioma, optimal patient selection criteria for re-RT remain unknown. We present a novel scoring system based on radiobiology principles including target independent factors, the likelihood of target control, and the anticipated organ at risk (OAR) toxicity to allow for proper patient selection in the setting of recurrent glioma.

Materials/methods

Thirty one patients with recurrent glioma who received re-RT (2008–2016) at NCI – NIH were included in the analysis. A novel scoring system for overall survival (OS) and progression free survival (PFS) was designed to include:1) target independent factors (age, KPS (Karnofsky Performance Status), histology, presence of symptoms), 2) target control, and 3) OAR toxicity risk. Normal tissue complication probability (NTCP) calculations were performed using the Lyman model. Kaplan-Meier analysis was performed for overall survival (OS) and progression free survival (PFS) for comparison amongst variables.

Results

No patient, including those who received dose to OAR above the published tolerance dose, experienced any treatment related grade 3–5 toxicity with a median PFS and OS from re-RT of 4 months (0.5–103) and 6 months (0.7–103) respectively. Based on cumulative maximum doses the average NTCP was 25% (0–99%) for the chiasm, 21% (0–99%) for the right optic nerve, 6% (0–92%) for the left optic nerve, and 59% (0–100%) for the brainstem. The independent factor and target control scores were each statistically significant for OS and the combination of independent factors plus target control was also significant for both OS (p = 0.02) and PFS (p = 0.006). The anticipated toxicity risk score was not statistically significant.

Conclusion

Our scoring system may represent a novel approach to patient selection for re-RT in recurrent high grade glioma. Further validation in larger patient cohorts including compilation of doses to tumor and OAR may help refine this further for inclusion into clinical trials and general practice.

Electronic supplementary material

The online version of this article (10.1186/s13014-017-0930-9) contains supplementary material, which is available to authorized users.

Biological and dosimetric characterisation of spatially fractionated proton minibeams

The biological effectiveness of proton beams varies with depth, spot size and lateral distance from the beam central axis. The aim of this work is to incorporate proton relative biological effectiveness (RBE) and equivalent uniform dose (EUD) considerations into comparisons of broad beam and highly modulated proton minibeams. A Monte Carlo model of a small animal proton beamline is presented. Dose and variable RBE is calculated on a per-voxel basis for a range of energies (30-109 MeV). For an open beam, the RBE values at the beam entrance ranged from 1.02-1.04, at the Bragg peak (BP) from 1.3 to 1.6, and at the distal end of the BP from 1.4 to 2.0. For a 50 MeV proton beam, a minibeam collimator designed to produce uniform dose at the depth of the BP peak, had minimal impact on the open beam RBE values at depth. RBE changes were observed near the surface when the collimator was placed flush with the irradiated object, due to a higher neutron contribution derived from proton interactions with the collimator. For proton minibeams, the relative mean RBE weighted entrance dose (RWD) was ~25% lower than the physical mean dose. A strong dependency of the EUD with fraction size was observed. For 20 Gy fractions, the EUD varied widely depending on the radiosensitivity of the cells. For radiosensitive cells, the difference was up to ~50% in mean dose and ~40% in mean RWD and the EUD trended towards the valley dose rather than the mean dose. For comparative studies of uniform dose with spatially fractionated proton minibeams, EUD derived from a per-voxel RWD distribution is recommended for biological assessments of reproductive cell survival and related endpoints.

On the gEUD biological optimization objective for organs at risk in Photon Optimizer of Eclipse treatment planning system

Inverse planning optimization using biologically based objectives is becoming part of the intensity modulated optimization process. The performances and efficacy of the biologically based gEUD (generalized Equivalent Uniform Dose) objective implemented in the Photon Optimizer (PO) of Varian Eclipse treatment planning system have been here analyzed. gEUD is associated with the parameter a that accounts for the seriality of a structure, being higher for more serial organs. The PO was used to optimize volumetric modulated arc therapy (VMAT) plans on a virtual homogeneous cylindrical phantom presenting a target and an organ at risk (OAR). The OAR was placed at 4 mm, 1 and 2 cm distance, or cropped at 0, 2 and 4 mm from the target. Homogeneous target dose of 60 Gy in 20 fractions was requested with physical dose-volume objectives, while OAR dose was minimized with the upper gEUD objective. The gEUD specific a parameter was varied from 0.1 to 40 to assess its impact to OAR sparing and target coverage. Actual head and neck and prostate cases, with one parotid and the rectum as test OAR, were also analyzed to translate the results in the more complex clinical environment. Increasing the a parameter value in the gEUD objective, the optimization achieved lower volumes of the OAR which received the highest dose levels. The maximum dose in the OAR was minimized well with a values up to 20, while further increase of a to 40 did not further improve the result. The OAR mean dose was reduced for the OAR located at 1 and 2 cm distance from the target, enforced with increasing a. For cropped OARs, a mean dose reduction was achieved for a values up to 3-5, but mean dose increased for higher a values. The optimal choice of the parameter a depends on the mutual OAR and target position, and seriality of the organ. Today no significant compendium of clinical and biological specific a and gEUD values are available for a wide range of OARs.

Considerations for using data envelopment analysis for the assessment of radiotherapy treatment plan quality

Purpose The operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment. Design/methodology/approach In total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method. Findings The input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results. Research limitations/implications This work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable. Practical implications DEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit. Social implications DEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit. Originality/value To the authors' knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

Dose-volume histogram analysis of brainstem necrosis in head and neck tumors treated using carbon-ion radiotherapy

BACKGROUND AND PURPOSE

We aimed to evaluate the relationship between brainstem necrosis and dose-volume histograms in patients with head and neck tumors after carbon-ion radiotherapy.

MATERIAL AND METHODS

We evaluated 85 patients with head and neck tumors who underwent carbon-ion radiotherapy and were followed-up for ≥12months. Brainstem necrosis was evaluated using the Common Terminology Criteria for Adverse Events (version 4.0).

RESULTS

The median follow-up was 24months, and four patients developed grade 1 brainstem necrosis, with 2-year and 3-year cumulative rates of 2.8% and 6.5%, respectively. Receiver operating characteristic curve analysis revealed the following significant cut-off values: a maximum brainstem dose of 48Gy (relative biological effectiveness [RBE]), D1cm³ of 27Gy (RBE), V40Gy (RBE) of 0.1cm³, V30Gy (RBE) of 0.7cm³, and V20Gy (RBE) of 1.4cm³. Multivariate analysis revealed that V30Gy (RBE) was most significantly associated with brainstem necrosis. The 2-year cumulative rates were 33% and 0% for V30Gy (RBE) of ≥0.7cm³ and <0.7cm³, respectively (p<0.001).

CONCLUSIONS

The present study indicated that the dose constraints might help minimize brainstem necrosis after carbon-ion radiotherapy.

Optimization of radiotherapy fractionation schedules based on radiobiological functions

OBJECTIVE

To present a method for optimizing radiotherapy fractionation schedules using radiobiological tools and taking into account the patient´s dose-volume histograms (DVH).

METHODS

This method uses a figure of merit based on the uncomplicated tumour control probability (P⁺) and the generalized equivalent uniform dose (gEUD). A set of doses per fraction is selected in order to find the dose per fraction and the total dose, thus maximizing the figure of merit and leading to a biologically effective dose that is similar to the prescribed schedule.

RESULTS

As a clinical example, a fractionation schedule for a prostate treatment plan is optimized and presented herein. From a prescription schedule of 70 Gy/35 × 2  Gy, the resulting optimal schema, using a figure of merit which only takes into account P⁺, is 54.4 Gy/16 × 3.4  Gy. If the gEUD is included in that figure of merit, the result is 65 Gy/26 × 2.5  Gy. Alternative schedules, which include tumour control probability (TCP) and the normal tissue complication probability (NTCP) values are likewise shown. This allows us to compare different schedules instead of solely finding the optimal value, as other possible clinical factors must be taken into account to make the best decision for treatment.

CONCLUSION

The treatment schedule can be optimized for each patient through radiobiological analysis. The optimization process shown below offers physicians alternative schedules that meet the objectives of the prescribed radiotherapy. Advances in knowledge: This article provides a simple, radiobiological-function-based method to take advantage of a patient's dose-volume histograms in order to better select the most suitable treatment schedule.