Optimized radiation therapy based on radiobiological objectives

Experimental validation in a neutron exposure frame of the MINAS TIRITH for cell damage simulation

In the domains of medicine and space exploration, refining risk assessment models for protecting healthy tissue from ionizing radiation is crucial. Understanding radiation-induced effects requires biological experimentations at the cellular population level and the cellular scale modeling using Monte Carlo track structure codes. We present MINAS TIRITH, a tool using Geant4-DNA Monte Carlo-generated databases to study DNA damage distribution at the cell population scale. It introduces a DNA damage location module and proposes a method to convert double-strand breaks (DSB) into DNA Damage Response foci. We evaluate damage location precision and DSB-foci conversion parameters. MINAS TIRITH's accuracy is validated againstγ-H2AX foci distribution from cell population exposed to monoenergetic neutron beams (2.5 or 15.1 MeV) under different configurations, yielding mixed radiation fields. Strong agreement between simulation and experimental results was found demonstrating MINAS TIRITH's predictive precision in radiation-induced DNA damage topology. Additionally, modeling intercellular damage variability within a population subjected to a specific macroscopic dose identifies subpopulations, enhancing realistic fate models. This approach advances our understanding of radiation-induced effects on cellular systems for risk assessment improvement.

TP53 and the Ultimate Biological Optimization Steps of Curative Radiation Oncology

The new biological interaction cross-section-based repairable-homologically repairable (RHR) damage formulation for radiation-induced cellular inactivation, repair, misrepair, and apoptosis was applied to optimize radiation therapy. This new formulation implies renewed thinking about biologically optimized radiation therapy, suggesting that most TP53 intact normal tissues are low-dose hypersensitive (LDHS) and low-dose apoptotic (LDA). This generates a fractionation window in LDHS normal tissues, indicating that the maximum dose to organs at risk should be ≤2.3 Gy/Fr, preferably of low LET. This calls for biologically optimized treatments using a few high tumor dose-intensity-modulated light ion beams, thereby avoiding secondary cancer risks and generating a real tumor cure without a caspase-3-induced accelerated tumor cell repopulation. Light ions with the lowest possible LET in normal tissues and high LET only in the tumor imply the use of the lightest ions, from lithium to boron. The high microscopic heterogeneity in the tumor will cause local microscopic cold spots; thus, in the last week of curative ion therapy, when there are few remaining viable tumor clonogens randomly spread in the target volume, the patient should preferably receive the last 10 GyE via low LET, ensuring perfect tumor coverage, a high cure probability, and a reduced risk for adverse normal tissue reactions. Interestingly, such an approach would also ensure a steeper rise in tumor cure probability and a higher complication-free cure, as the few remaining clonogens are often fairly well oxygenated, eliminating a shallower tumor response due to inherent ion beam heterogeneity. With the improved fractionation proposal, these approaches may improve the complication-free cure probability by about 10-25% or even more.

Radiobiological Evaluation of Combined Gamma Knife Radiosurgery and Hyperthermia for Pediatric Neuro-Oncology

Simple Summary

This study proposes a novel strategy in brain cancer management. Stereotactic radiosurgery delivered by the Gamma Knife was combined with hyperthermia. For the radiobiological modelling of this synergistic treatment modality, we used the linear-quadratic model with temperature-dependent parameters to assess the potential enhancement of the therapeutic outcome. The results indicate that focused intracranial heating can be used to boost the dose to the target. Alternatively, one can conclude that for the same therapeutic effect, hyperthermia can help to minimize the dose undesirably delivered to healthy tissues. This study is also the first to advocate a combination of stereotactic radiosurgery with focused heating and motivates the future development of hyperthermia systems for brain cancer treatment.

Abstract

Combining radiotherapy (RT) with hyperthermia (HT) has been proven effective in the treatment of a wide range of tumours, but the combination of externally delivered, focused heat and stereotactic radiosurgery has never been investigated. We explore the potential of such treatment enhancement via radiobiological modelling, specifically via the linear-quadratic (LQ) model adapted to thermoradiotherapy through modulating the radiosensitivity of temperature-dependent parameters. We extend this well-established model by incorporating oxygenation effects. To illustrate the methodology, we present a clinically relevant application in pediatric oncology, which is novel in two ways. First, it deals with medulloblastoma, the most common malignant brain tumour in children, a type of brain tumour not previously reported in the literature of thermoradiotherapy studies. Second, it makes use of the Gamma Knife for the radiotherapy part, thereby being the first of its kind in this context. Quantitative metrics like the biologically effective dose (BED) and the tumour control probability (TCP) are used to assess the efficacy of the combined plan.

Optimization in treatment planning of high dose-rate brachytherapy - Review and analysis of mathematical models

Treatment planning in high dose-rate brachytherapy has traditionally been conducted with manual forward planning, but inverse planning is today increasingly used in clinical practice. There is a large variety of proposed optimization models and algorithms to model and solve the treatment planning problem. Two major parts of inverse treatment planning for which mathematical optimization can be used are the decisions about catheter placement and dwell time distributions. Both these problems as well as integrated approaches are included in this review. The proposed models include linear penalty models, dose-volume models, mean-tail dose models, quadratic penalty models, radiobiological models, and multiobjective models. The aim of this survey is twofold: (i) to give a broad overview over mathematical optimization models used for treatment planning of brachytherapy and (ii) to provide mathematical analyses and comparisons between models. New technologies for brachytherapy treatments and methods for treatment planning are also discussed. Of particular interest for future research is a thorough comparison between optimization models and algorithms on the same dataset, and clinical validation of proposed optimization approaches with respect to patient outcome.

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.

Risk-optimized proton therapy to minimize radiogenic second cancers

Proton therapy confers substantially lower predicted risk of second cancer compared with photon therapy. However, no previous studies have used an algorithmic approach to optimize beam angle or fluence-modulation for proton therapy to minimize those risks. The objectives of this study were to demonstrate the feasibility of risk-optimized proton therapy and to determine the combination of beam angles and fluence weights that minimizes the risk of second cancer in the bladder and rectum for a prostate cancer patient. We used 6 risk models to predict excess relative risk of second cancer. Treatment planning utilized a combination of a commercial treatment planning system and an in-house risk-optimization algorithm. When normal-tissue dose constraints were incorporated in treatment planning, the risk model that incorporated the effects of fractionation, initiation, inactivation, repopulation and promotion selected a combination of anterior and lateral beams, which lowered the relative risk by 21% for the bladder and 30% for the rectum compared to the lateral-opposed beam arrangement. Other results were found for other risk models.

The Importance of Quasi-4D Path-Integrated Dose Accumulation for More Accurate Risk Estimation in Stereotactic Liver Radiotherapy

Intrafraction organ deformation may be accounted for by inclusion of temporal information in dose calculation models. In this article, we demonstrate a quasi-4-dimensional method for improved risk estimation. Conventional 3-dimensional and quasi-4-dimensional calculations employing dose warping for dose accumulation were undertaken for patients with liver metastases planned for 42 Gy in 6 fractions of stereotactic body radiotherapy. Normal tissue complication probabilities and stochastic risks for radiation-induced carcinogenesis and cardiac complications were evaluated for healthy peripheral structures. Hypothetical assessments of other commonly employed dose/fractionation schedules on normal tissue complication probability estimates were explored. Conventional 3-dimensional dose computation may result in significant under- or overestimation of doses to organ at risk. For instance, doses differ (on average) by 17% (σ = 14%) for the left kidney, by 14% (σ = 7%) for the right kidney, by 7% (σ = 9%) for the large bowel, and by 10% (σ = 14%) for the duodenum. Discrepancies in the excess relative risk range up to about 30%. The 3-dimensional approach was shown to result in cardiac complication risks underestimated by >20%. For liver stereotactic body radiotherapy, we have shown that conventional 3-dimensional dose calculation may significantly over-/underestimate dose to organ at risk (90%-120% of the 4-dimensional estimate for the mean dose and 20%-150% for D2%). Providing dose estimates that most closely represent the actual dose delivered will provide valuable information to improve our understanding of the dose response for partial volume irradiation using hypofractionated schedules. Excess relative risks of radiocarcinogenesis were shown to range up to approximately excess relative risk = 4 and the prediction thereof depends greatly on the use of either 3-dimensional or 4-dimensional methods (with corresponding results differing by tens of percent).

Direct use of multivariable normal tissue complication probability models in treatment plan optimisation for individualised head and neck cancer radiotherapy produces clinically acceptable treatment plans

BACKGROUND AND PURPOSE

Recently, clinically validated multivariable normal tissue complication probability models (NTCP) for head and neck cancer (HNC) patients have become available. We test the feasibility of using multivariable NTCP-models directly in the optimiser for inverse treatment planning of radiotherapy to improve the dose distributions and corresponding NTCP-estimates in HNC patients.

MATERIAL AND METHODS

For 10 HNC cases, intensity-modulated radiotherapy plans were optimised either using objective functions based on the 'generalised equivalent uniform dose' (OFgEUD) or based on multivariable NTCP-models (OFNTCP). NTCP-models for patient-rated xerostomia, physician-rated RTOG grade II-IV dysphagia, and various patient-rated aspects of swallowing dysfunction were incorporated. The NTCP-models included dose-volume parameters as well as clinical factors contributing to a personalised optimisation process. Both optimisation techniques were compared by means of 'pseudo Pareto fronts' (target dose conformity vs. the sum of the NTCPs).

RESULTS

Both optimisation techniques resulted in clinically realistic treatment plans with only small differences. For nine patients the sum-NTCP was lower for the OFNTCP optimised plans (on average 5.7% (95%CI 1.7-9.9%, p<0.006)). Furthermore, the OFNTCP provided the advantages of fewer unknown optimisation parameters and an intrinsic mechanism of individualisation.

CONCLUSIONS

Treatment plan optimisation using multivariable NTCP-models directly in the OF is feasible as has been demonstrated for HNC radiotherapy.

Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters

Purpose

Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA.

Methods

The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms.

Results

In the prostate plans, the AAA predicted lower NTCP value (NTCP_AAA) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCP_AAA, that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCP_PBC’s ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCP_AAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCP_PBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCP_AAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCP_AAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the mean NTCP_PBC varied from 0.1% (sd = 0.0%) to 1.8% (sd = 0.2%) depending on the chosen parameters set.

Conclusions

When the original PBC treatment plans were recalculated using AAA with the same number of monitor units provided by PBC, the NTCP_AAA was lower than the NTCP_PBC, except for the breast treatments. The NTCP is strongly affected by the wide-ranging values of radiobiological parameters.

The use and QA of biologically related models for treatment planning: short report of the TG-166 of the therapy physics committee of the AAPM

Treatment planning tools that use biologically related models for plan optimization and/or evaluation are being introduced for clinical use. A variety of dose-response models and quantities along with a series of organ-specific model parameters are included in these tools. However, due to various limitations, such as the limitations of models and available model parameters, the incomplete understanding of dose responses, and the inadequate clinical data, the use of biologically based treatment planning system (BBTPS) represents a paradigm shift and can be potentially dangerous. There will be a steep learning curve for most planners. The purpose of this task group is to address some of these relevant issues before the use of BBTPS becomes widely spread. In this report, the authors (1) discuss strategies, limitations, conditions, and cautions for using biologically based models and parameters in clinical treatment planning; (2) demonstrate the practical use of the three most commonly used commercially available BBTPS and potential dosimetric differences between biologically model based and dose-volume based treatment plan optimization and evaluation; (3) identify the desirable features and future directions in developing BBTPS; and (4) provide general guidelines and methodology for the acceptance testing, commissioning, and routine quality assurance (QA) of BBTPS.

Monte Carlo role in radiobiological modelling of radiotherapy outcomes

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.

Datamining approaches for modeling tumor control probability

BACKGROUND

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively.

MATERIAL AND METHODS

Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods.

RESULTS

Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17).

CONCLUSIONS

The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

A systems biology approach to radiation therapy optimization

During the last 20 years, the field of cellular and not least molecular radiation biology has been developed substantially and can today describe the response of heterogeneous tumors and organized normal tissues to radiation therapy quite well. An increased understanding of the sub-cellular and molecular response is leading to a more general systems biological approach to radiation therapy and treatment optimization. It is interesting that most of the characteristics of the tissue infrastructure, such as the vascular system and the degree of hypoxia, have to be considered to get an accurate description of tumor and normal tissue responses to ionizing radiation. In the limited space available, only a brief description of some of the most important concepts and processes is possible, starting from the key functional genomics pathways of the cell that are not only responsible for tumor development but also responsible for the response of the cells to radiation therapy. The key mechanisms for cellular damage and damage repair are described. It is further more discussed how these processes can be brought to inactivate the tumor without severely damaging surrounding normal tissues using suitable radiation modalities like intensity-modulated radiation therapy (IMRT) or light ions. The use of such methods may lead to a truly scientific approach to radiation therapy optimization, particularly when invivo predictive assays of radiation responsiveness becomes clinically available at a larger scale. Brief examples of the efficiency of IMRT are also given showing how sensitive normal tissues can be spared at the same time as highly curative doses are delivered to a tumor that is often radiation resistant and located near organs at risk. This new approach maximizes the probability to eradicate the tumor, while at the same time, adverse reactions in sensitive normal tissues are as far as possible minimized using IMRT with photons and light ions.

Predicting radiotherapy outcomes using statistical learning techniques

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data.

Radiobiological model comparison of 3D conformal radiotherapy and IMRT plans for the treatment of prostate cancer

The main aim of radiotherapy is to deliver a dose of radiation that is high enough to destroy the tumour cells while at the same time minimising the damage to normal healthy tissues. Clinically, this has been achieved by assigning a prescription dose to the tumour volume and a set of dose constraints on critical structures. Once an optimal treatment plan has been achieved the dosimetry is assessed using the physical parameters of dose and volume. There has been an interest in using radiobiological parameters to evaluate and predict the outcome of a treatment plan in terms of both a tumour control probability (TCP) and a normal tissue complication probability (NTCP). In this study, simple radiobiological models that are available in a commercial treatment planning system were used to compare three dimensional conformal radiotherapy treatments (3D-CRT) and intensity modulated radiotherapy (IMRT) treatments of the prostate. Initially both 3D-CRT and IMRT were planned for 2 Gy/fraction to a total dose of 60 Gy to the prostate. The sensitivity of the TCP and the NTCP to both conventional dose escalation and hypo-fractionation was investigated. The biological responses were calculated using the Källman S-model. The complication free tumour control probability (P+) is generated from the combined NTCP and TCP response values. It has been suggested that the alpha/beta ratio for prostate carcinoma cells may be lower than for most other tumour cell types. The effect of this on the modelled biological response for the different fractionation schedules was also investigated.

On the impact of functional imaging accuracy on selective boosting IMRT

In order to quantify the impact of loss of functional imaging sensitivity and specificity on tumor control and normal tissue toxicity for selective boosting IMRT four selective boosting scenarios were designed: SB91-81 (EUD=91Gy for the high-risk tumor subvolume and EUD=81Gy for a remaining low-risk PTV (rPTV)), SB80-74, SB90-70, and risk-adaptive optimization. For each sensitivity loss level the loss in tumor control probability (DeltaTCP) was calculated. For each specificity loss level, the increase in rectal and bladder toxicity was quantified using the radiobiological indices (equivalent uniform dose (EUD) and normal tissue complication probability (NTCP)) as well as %-volumes irradiated. The impact of loss in sensitivity on local tumor control was maximal when the prescription dose level for rPTV had the lowest value. The SB90-70 plan had a DeltaTCP=29.6%, the SB91-81 plan had a DeltaTCP=9.5%, while for risk-adaptive optimization a DeltaTCP=4.7% was found. Independent of planning technique loss in functional imaging specificity appears to have a minimal impact on the expected normal tissue toxicity, since an increase in rectal or bladder toxicity as a function of loss in specificity was not observed. Additionally, all plans fulfilled the rectum and the bladder sparing criteria found in the literature for late rectal bleeding and genitourinary complications. Our study shows that the choice of a low-risk classification for the rPTV in selective boosting IMRT may lead to a significant loss in TCP. Furthermore, for the example considered in which normal tissue complications can be limited through the use of a tissue expander it appears that the therapeutic ratio can be improved using a functional imaging technique with a high sensitivity and limited specificity; while for cases were this is not possible, an optimal balance between sensitivity and specificity has to be found.

Is it beneficial to selectively boost high-risk tumor subvolumes? A comparison of selectively boosting high-risk tumor subvolumes versus homogeneous dose escalation of the entire tumor based on equivalent EUD plans

PURPOSE

To quantify and compare expected local tumor control and expected normal tissue toxicities between selective boosting IMRT and homogeneous dose escalation IMRT for the case of prostate cancer.

METHODS

Four different selective boosting scenarios and three different high-risk tumor subvolume geometries were designed to compare selective boosting and homogeneous dose escalation IMRT plans delivering the same equivalent uniform dose (EUD) to the entire PTV. For each scenario, differences in tumor control probability between both boosting strategies were calculated for the high-risk tumor subvolume and remaining low-risk PTV, and were visualized using voxel based iso-TCP maps. Differences in expected rectal and bladder complications were quantified using radiobiological indices (generalized EUD (gEUD) and normal tissue complication probability (NTCP)) as well as %-volumes.

RESULTS

For all investigated scenarios and high-risk tumor subvolume geometries, selective boosting IMRT improves expected TCP compared to homogeneous dose escalation IMRT, especially when lack of control of the high-risk tumor subvolume could be the cause for tumor recurrence. Employing, selective boosting IMRT significant increases in expected TCP can be achieved for the high-risk tumor subvolumes. The three conventional selective boosting IMRT strategies, employing physical dose objectives, did not show significant improvement in rectal and bladder sparing as compared to their counterpart homogeneous dose escalation plans. However, risk-adaptive optimization, utilizing radiobiological objective functions, resulted in reduction in NTCP for the rectum when compared to its corresponding homogeneous dose escalation plan.

CONCLUSIONS

Selective boosting is a more effective method than homogeneous dose escalation for achieving optimal treatment outcomes. Furthermore, risk-adaptive optimization increases the therapeutic ratio as compared to conventional selective boosting IMRT.

On Voxel based Iso-Tumor Control Probabilty and Iso-Complication Maps for Selective Boosting and Selective Avoidance Intensity Modulated Radiotherapy

Voxel based iso-Tumor Control Probability (TCP) maps and iso-Complication maps are proposed as a plan-review tool especially for functional image-guided intensity-modulated radiotherapy (IMRT) strategies such as selective boosting (dose painting) and conformal avoidance IMRT. The maps employ voxel-based phenomenological biological dose-response models for target volumes and normal organs. Two IMRT strategies for prostate cancer, namely conventional uniform IMRT delivering an EUD = 84 Gy (equivalent uniform dose) to the entire PTV and selective boosting delivering an EUD = 82 Gy to the entire PTV, are investigated, to illustrate the advantages of this approach over iso-dose maps. Conventional uniform IMRT did yield a more uniform isodose map to the entire PTV while selective boosting did result in a nonuniform isodose map. However, when employing voxel based iso-TCP maps selective boosting exhibited a more uniform tumor control probability map compared to what could be achieved using conventional uniform IMRT, which showed TCP cold spots in high-risk tumor subvolumes despite delivering a higher EUD to the entire PTV. Voxel based iso-Complication maps are presented for rectum and bladder, and their utilization for selective avoidance IMRT strategies are discussed. We believe as the need for functional image guided treatment planning grows, voxel based iso-TCP and iso-Complication maps will become an important tool to assess the integrity of such treatment plans.

Patient-specific dosimetry of conventional and intensity modulated radiation therapy using a novel full Monte Carlo phase space reconstruction method from electronic portal images

Electronic portal imagers have promising dosimetric applications in external beam radiation therapy. In this study a patient dose computation algorithm based on Monte Carlo (MC) simulations and on portal images is developed and validated. The patient exit fluence from primary photons is obtained from the portal image after correction for scattered radiation. The scattered radiation at the portal imager and the spectral energy distribution of the primary photons are estimated from MC simulations at the treatment planning stage. The patient exit fluence and the spectral energy distribution of the primary photons are then used to ray-trace the photons from the portal image towards the source through the CT geometry of the patient. Photon weights which reflect the probability of a photon being transmitted are computed during this step. A dedicated MC code is used to transport back these photons from the source through the patient CT geometry to obtain patient dose. Only Compton interactions are considered. This code also produces a reconstructed portal image which is used as a verification tool to ensure that the dose reconstruction is reliable. The dose reconstruction algorithm is compared against MC dose calculation (MCDC) predictions and against measurements in phantom. The reconstructed absolute absorbed doses and the MCDC predictions in homogeneous and heterogeneous phantoms agree within 3% for simple open fields. Comparison with film-measured relative dose distributions for IMRT fields yields agreement within 3 mm, 5%. This novel dose reconstruction algorithm allows for daily patient-specific dosimetry and verification of patient movement.

Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes

BACKGROUND AND PURPOSE

A tumor subvolume-based, risk-adaptive optimization strategy is presented.

METHODS AND MATERIALS

Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, delta, between TCP and NTCP on risk-adaptive optimization was investigated.

RESULTS

Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter delta had little effect on risk-adaptive optimization. However, the clinical parameters D(50) and gamma(50) that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization.

CONCLUSIONS

On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.

IMRT: a review and preview

The very first cornerstone paper on intensity-modulated radiation therapy (IMRT) was published in Physics in Medicine and Biology, and many seminal IMRT works have since appeared in this journal. Today IMRT is a widely used clinical treatment modality in many countries. This contribution to the 50th anniversary issue reviews the physical, mathematical, and technological milestones that have facilitated the clinical implementation and success of IMRT. In particular, the basic concepts and developments of both IMRT treatment planning ('inverse planning') and the delivery of cone-beam IMRT with a multileaf collimator from a fixed number of static beam directions are discussed. An outlook into the future of IMRT concludes the paper.

The IMRT information process—mastering the degrees of freedom in external beam therapy

The techniques and procedures for intensity-modulated radiation therapy (IMRT) are reviewed in the context of the information process central to treatment planning and delivery of IMRT. A presentation is given of the evolution of the information based radiotherapy workflow and dose delivery techniques, as well as the volume and planning concepts for relating the dose information to image based patient representations. The formulation of the dose shaping process as an optimization problem is described. The different steps in the calculation flow for determination of machine parameters for dose delivery are described starting from the formulation of optimization objectives over dose calculation to optimization procedures. Finally, the main elements of the quality assurance procedure necessary for implementing IMRT clinically are reviewed.

An assessment of biologically-based optimization (BORT) in the IMRT era

The purpose of this study was to investigate the role of biological-based IMRT (BORT) in treatment planning development; more specifically, to assess the possible advantages of BORT over the classic 3-dimensional conformal radiation therapy (3DRT) and dose-based IMRT based on quantitative and qualitative indices. Three clinical cases are presented to evaluate the differences of BORT, IMRT, and 3DRT. 3DRT, IMRT, and BORT plans were generated for each case using a commercially available treatment planning system (Pinnacle by Philips). The plans were compared by evaluating biological endpoints such as tumor control probability (TCP), normal tissue control probability (NTCP), and uncomplicated tumor control probability (P+), as well as isodose line distribution, dose-volume histograms (DVHs), and dose uniformity. In all cases of this study, BORT yielded improved isodose coverage and P+. Our preliminary results suggest that BORT could play an important role in treatment planning optimization, especially as biological models and predictive assays become more accurate. Further case studies are needed to establish a definitive role for this type of optimization.

Investigation of using a power function as a cost function in inverse planning optimization

The purpose of this paper is to investigate the use of a power function as a cost function in inverse planning optimization. The cost function for each structure is implemented as an exponential power function of the deviation between the resultant dose and prescribed or constrained dose. The total cost function for all structures is a summation of the cost function of every structure. When the exponents of all terms in the cost function are set to 2, the cost function becomes a classical quadratic cost function. An independent optimization module was developed and interfaced with a research treatment planning system from the University of North Carolina for dose calculation and display of results. Three clinical cases were tested for this study with various exponents set for tumor targets and sensitive structures. Treatment plans with these exponent settings were compared, using dose volume histograms. The results of our study demonstrated that using an exponent higher than 2 in the cost function for the target achieved better dose homogeneity than using an exponent of 2. An exponent higher than 2 for serial sensitive structures can effectively reduce the maximum dose. Varying the exponent from 2 to 4 resulted in the most effective changes in dose volume histograms while the change from 4 to 8 is less drastic, indicating a situation of saturation. In conclusion, using a power function with exponent greater than 2 as a cost function can effectively achieve homogeneous dose inside the target and/or minimize maximum dose to the critical structures.

Long-term incidence of hypothyroidism after radiotherapy in patients with head-and-neck cancer

PURPOSE

To determine the long-term incidence of postirradiation hypothyroidism (HT) in patients with head-and-neck cancer.

METHODS AND MATERIALS

The incidence of overt HT was assessed prospectively in 391 patients with nonthyroid head-and-neck cancer admitted for radiotherapy (RT) consecutively between 1990 and 1996. Eighty-three patients were excluded from the analysis because of known thyroid disease before treatment (n = 27), no RT was given (n = 15), or inadequate follow-up (n = 41). Overt HT was defined as increased thyroid-stimulating hormone (TSH) in combination with decreased fT4/T4 or in combination with initiation of thyroxine replacement therapy.

RESULTS

With a median follow-up of 4.2 years (range, 3 months to 10.9 years) for 308 evaluable patients, the 5- and 10-year Kaplan-Meier actuarial risks of HT were 20% and 27%, respectively. The median time until development of HT was 1.8 years (3 months to 8.1 years). Multivariate analysis showed that patients with bilateral RT to the neck had a higher risk of HT in comparison with unilateral neck RT (relative hazard, 0.37; p = 0.02). The addition of surgery to RT increased the overall risk of HT (p < 0.001); and if surgery involved the thyroid gland, the relative hazard was 4.74 (p < 0.001). For an elevated pre-RT TSH value, the relative hazard was 1.58 (p < 0.001).

CONCLUSION

The incidence of overt HT after locoregional RT for nonthyroid head-and-neck cancer continues to increase with time, even after long-term follow-up. We recommend life-long TSH testing in these patients.

Inverse treatment planning with adaptively evolving voxel-dependent penalty scheme

In current inverse planning algorithms it is common to treat all voxels within a target or sensitive structure equally and use structure specific prescriptions and weighting factors as system parameters. In reality, the voxels within a structure are not identical in complying with their dosimetric goals and there exists strong intrastructural competition. Inverse planning objective function should not only balance the competing objectives of different structures but also that of the individual voxels in various structures. In this work we propose to model the intrastructural tradeoff through the modulation of voxel-dependent importance factors and deal with the challenging problem of how to obtain a sensible set of importance factors with a manageable amount of computing. Instead of letting the values of voxel-dependent importance to vary freely during the search process, an adaptive algorithm, in which the importance factors were tied to the local radiation doses through a heuristically constructed relation, was developed. It is shown that the approach is quite general and the EUD-based optimization is a special case of the proposed framework. The new planning tool was applied to study a hypothetical phantom case and a prostate case. Comparison of the results with that obtained using conventional inverse planning technique with structure specific importance factors indicated that the dose distributions from the conventional inverse planning are at best suboptimal and can be significantly improved with the help of the proposed nonuniform penalty scheme.

Incorporating model parameter uncertainty into inverse treatment planning

Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment.

A comparison of forward and inverse treatment planning for intensity-modulated radiotherapy of head and neck cancer

BACKGROUND AND PURPOSE

To compare intensity-modulated treatment plans of patients with head and neck cancer generated by forward and inverse planning.

MATERIALS AND METHODS

Ten intensity-modulated treatment plans, planned and treated with a step&shoot technique using a forward planning approach, were retrospectively re-planned with an inverse planning algorithm. For this purpose, two strategies were applied. First, inverse planning was performed with the same beam directions as forward planning. In addition, nine equidistant, coplanar incidences were used. The main objective of the optimisation process was the sparing of the parotid glands beside an adequate treatment of the planning target volume (PTV). Inverse planning was performed both with pencil beam and Monte Carlo dose computation to investigate the influence of dose computation on the result of the optimisation.

RESULTS

In most cases, both inverse planning strategies managed to improve the treatment plans distinctly due to a better target coverage, a better sparing of the parotid glands or both. A reduction of the mean dose by 3-11Gy for at least one of the parotid glands could be achieved for most of the patients. For three patients, inverse planning allowed to spare a parotid gland that had to be sacrificed by forward planning. Inverse planning increased the number of segments compared to forward planning by a factor of about 3; from 9-15 to 27-46. No significant differences for PTV and parotid glands between both inverse planning approaches were found. Also, the use of Monte Carlo instead of pencil beam dose computation did not influence the results significantly.

CONCLUSION

The results demonstrate the potential of inverse planning to improve intensity-modulated treatment plans for head and neck cases compared to forward planning while retaining clinical utility in terms of treatment time and quality assurance.

Conversion of dose–volume constraints to dose limits

The purpose of this study is to introduce two techniques for converting dose-volume constraints to dose limits for treatment planning optimization, and to evaluate their performance. The first technique, called dose-sorting, is based on the assumption that higher dose limits should be assigned to the constraint points receiving higher doses, and vice versa. The second technique, the hybrid technique, is a hybrid of the dose-sorting technique and the mixed integer linear programming (MILP) technique. Among all constraint points in an organ at risk, the dose limits for the points far from a dose-volume constraint are determined by dose-sorting, while the dose limits for the points close to a dose-volume constraint are determined by MILP. We evaluated the performance of the two new techniques for one treatment geometry by comparing them with the MILP technique. The dose-sorting technique had a high probability of finding the global optimum when no more than three organs at risk have dose-volume constraints. It was much faster than the MILP technique. The hybrid technique always found the global optimum when the MILP percentage (the percentage of constraint points for which the dose limits are determined by the MILP technique) was large enough, but its computation time increased dramatically with the MILP percentage. In conclusion, the dose-sorting technique and the hybrid technique with a low MILP percentage are clinically feasible.

Potential of intensity-modulated radiotherapy to escalate doses to head-and-neck cancers: What is the maximal dose?

PURPOSE

To investigate the potential of intensity-modulated radiotherapy (IMRT) to escalate doses to head-and-neck cancer and find the maximal dose that could be prescribed to the target volume with IMRT while doses to critical organs were maintained at their currently acceptable levels. The secondary goal was to search for limits in current IMRT technology.

METHODS AND MATERIALS

For a group of 12 head-and-neck cancer patients with different tumor locations and shapes, we performed IMRT planning using a simultaneous integrated boost strategy, that is, the gross tumor volume (GTV), clinical target volume (CTV), and electively treated nodes were treated simultaneously at different dose levels. The critical structures involved in the treatment field that needed to be spared included the brainstem, spinal cord, and parotid glands, depending on the disease site. Nine coplanar 6-MV photon beams were used for planning with the IMRT system developed at our institution, and dose-volume criteria were used for optimization. By varying the optimization parameters, we gradually increased the dose to the GTV while keeping the dose to the critical structures at less than the acceptable tolerance level. The criteria for accepting the plan included the following: (1) the prescription dose to the GTV had to cover 99% of the volume, and the dose homogeneity of the GTV needed to be <10%; (2) the prescription to the CTV (which was set either at 60 Gy or 10 Gy less than that of the GTV) had to cover 95% of the volume, and the same amount of normal tissue outside the CTV received the CTV prescription dose as in the current acceptable plan; (3) the prescription to the electively treated lymph nodes needed to cover 90% of the volume; and (4) the maximal dose to the brainstem and spinal cord had to be <55 Gy and 45 Gy, respectively. For parotid glands, the dose needed to be as low as possible without compromising the target doses. The deliverable plans as determined by the actual multileaf collimator leaf sequences were used for the final evaluation. To verify that the acceptable plans were deliverable, the experimental measurements of planar dose distribution were performed in phantom with film.

RESULTS

The maximal dose to the GTV varied from 86 to 176 Gy if the CTV dose increased with the GTV dose. It was reduced to 76-82 Gy if the CTV dose was kept at 60 Gy. The competing criteria usually are the requirements of the tolerance doses to the critical organs and target dose homogeneity, not the target prescription dose. Using more beams only increased the dose marginally. The results could change significantly if a different set of criteria for the plan evaluation were used. Dosimetric measurements confirmed that such a high dose and dose gradient could be delivered accurately with dynamic multileaf collimators. Statistical analyses showed no significant correlations between the maximal doses and the number of GTVs and volume of GTVs and CTVs.

CONCLUSION

Doses to head-and-neck cancers with simultaneous integrated boost IMRT can be escalated to a greater level than currently prescribed clinically. The limit of IMRT in head-and-neck cancer has not been reached at the current prescription level of 70 Gy. Such high total and fractionated doses should be carefully evaluated before being prescribed clinically.

Inverse treatment planning by physically constrained minimization of a biological objective function

In the current state-of-the art of clinical inverse planning, the design of clinically acceptable IMRT plans is predominantly based on the optimization of physical rather than biological objective functions. A major impetus for this trend is the unproven predictive power of radiobiological models, which is largely due to the scarcity of data sets for an accurate evaluation of the model parameters. On the other hand, these models do capture the currently known dose-volume effects in tissue dose-response, which should be accounted for in the process of optimization. In order to incorporate radiobiological information in clinical treatment planning optimization, we propose a hybrid physico-biological approach to inverse treatment planning based on the application of a continuous penalty function method to the constrained minimization of a biological objective. The objective is defined as the weighted sum of normal tissue complication probabilities evaluated with the Lyman normal-tissue complication probability model. Physical constraints specify the admissible minimum and maximum target dose. The continuous penalty function method is then used to find an approximate solution of the resulting large-scale constrained minimization problem. Plans generated by our approach are compared to ones produced by a commercial planning system incorporating physical optimization. The comparisons show clinically negligible differences, with the advantage that the hybrid technique does not require specifications of any dose-volume constraints to the normal tissues. This indicates that the proposed hybrid physico-biological method can be used for the generation of clinically acceptable plans.

Patient-Reported Toxicity Correlated to Dose–Volume Histograms of the Rectum in Radiotherapy of the Prostate

We studied 73 patients treated with 3-dimensional conformal radiotherapy for prostate cancer to determine whether there is a correlation between dose per volume to either the whole rectum, rectal wall, rectal surface, or anal canal and the development of late rectal complications measured with prostate-specific quality-of-life (QOL) questionnaires. Given doses were 66.6 to 72 Gy. The prostate cancer modules used were the UCLA-Prostate Cancer Index module (UCLA-PCI) (5 questions), the Functional Assessment of Cancer Therapy-Prostate module (FACT-P) (1 question), and the European Organization for Research and Treatment of Cancer prostate cancer module (EORTC QLQ-PR25) (4 questions). A Spearman correlation analysis between the total toxicity score and the dose-volume histograms (DVHs) was performed. All statistical tests were 2-sided. Sixty-five (89%) patients returned the questionnaire, and 18 (28%) underwent endoscopy for rectal bleeding. We found that only patients who had had an endoscopy showed a correlation between rectal toxicity and dose per volume, as compared with the other patients who showed none. Correlation between rectal toxicity and dose per volume for all 4 structures was stronger for higher doses. For 70 Gy, all contours, except the anal canal, showed a significant dose-volume correlation. Our results indicate that only in cases of pronounced rectal toxicity is there a dose-volume correlation, especially for doses of 70 Gy or more. DVHs of the whole rectum, wall, or surface, but not the anal canal, are all equivalent in predicting late rectal toxicity.

[Optimization criteria in intensity-modulated radiotherapy]

The present paper provides an overview on the inverse treatment planning for the assessment of intensity-modulated fields. The problem is to find the optimal dose distribution for given attributes of the irradiated tissue. The attributes of the optimal dose distribution are delineated by an objective function. In practice, models are used that evaluate the physical dose distribution, either directly or through their radiobiological effects. In the simplest case, the squared deviation of the achieved dose distribution is minimized to the prescribed dose distribution. For organs structured in parallel, it is common to introduce dose-volume constraints. Another approach is to optimize a value for the probability of complication-free tumor control. The complication probability for normal tissue, in turn, is a rather complex function. However, using the relative seriality, a simple model can be devised with a certain approximation. Other models of "effective dose" are also presented.

Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee

Intensity-modulated radiation therapy (IMRT) represents one of the most significant technical advances in radiation therapy since the advent of the medical linear accelerator. It allows the clinical implementation of highly conformal nonconvex dose distributions. This complex but promising treatment modality is rapidly proliferating in both academic and community practice settings. However, these advances do not come without a risk. IMRT is not just an add-on to the current radiation therapy process; it represents a new paradigm that requires the knowledge of multimodality imaging, setup uncertainties and internal organ motion, tumor control probabilities, normal tissue complication probabilities, three-dimensional (3-D) dose calculation and optimization, and dynamic beam delivery of nonuniform beam intensities. Therefore, the purpose of this report is to guide and assist the clinical medical physicist in developing and implementing a viable and safe IMRT program. The scope of the IMRT program is quite broad, encompassing multileaf-collimator-based IMRT delivery systems, goal-based inverse treatment planning, and clinical implementation of IMRT with patient-specific quality assurance. This report, while not prescribing specific procedures, provides the framework and guidance to allow clinical radiation oncology physicists to make judicious decisions in implementing a safe and efficient IMRT program in their clinics.

Image-guided conformal radiation therapy planning and delivery for non-small-cell lung cancer

BACKGROUND

Our understanding of both the importance of local control for survival of patients with unresectable lung cancer and the inadequacy of conventional radiation therapy (RT) to provide this local control has undergone marked changes in the past 2 decades.

METHODS

A review was conducted of recent studies and meta-analyses in the literature that have convincingly demonstrated the value of thoracic irradiation in increasing long-term survival in patients with both small-cell lung cancer and non-small-cell lung cancer (NSCLC).

RESULTS

Large cooperative trials have shown long-term local control of only approximately 10% for NSCLC using conventionally planned radiation to doses of 60-64 Gy either as a single modality or when preceded by induction chemotherapy. Concurrent chemotherapy may modestly improve local control at the cost of greater acute esophageal toxicity. Simple escalation of radiation dose is limited by the tolerance of normal intrathoracic organs. Recent developments in anatomic and functional imaging, computerized RT planning, and RT delivery, as well as a reassessment of the appropriate target volumes for RT in the context of combined modality therapy, provide the capability to better conform regions of high dose to the target volume and test the hypothesis that increases in tumor dose will improve local control and survival.

CONCLUSIONS

Encouraging phase II data have been reported from single institutions using individually developed software and hardware. The availability of commercial tools for planning and delivering such conformal treatment will allow prospective assessment of the true value of these technologies in the management of patients with lung cancer.

Biologically optimized 3-dimensional in vivo predictive assay-based radiation therapy using positron emission tomography-computerized tomography imaging

PET-CT is probably the ultimate tool for accurate tumor imaging and 3-dimensional in vivo predictive assay of radiation sensitivity. By imaging the tumor twice during the early course of therapy, it should be possible to quantify both the tumor responsiveness to therapy and the rate of loss of functional tumor cells using the presently derived equations. This new information is ideal for use together with biologically based therapy optimization and makes it possible accurately to quantitate the dose-response relation, at least for the bulk of the tumor cells. Since the tumor responsiveness is available after about one and a half weeks of therapy, the information is also ideal for use with adaptive therapy where all forms of deviations from the original treatment plan can be accurately corrected for since they generally influence the still functional, but mainly doomed tumor cell compartment. Thus, uncertainties such as: 1) the geometric misalignment of the therapeutic beam with the tumor, 2) deviations of the delivered dose distribution from the planned delivery whether due to 3) an erroneous treatment planning algorithm or 4) treatment equipment uncertainties and 5) deviations in the anticipated responsiveness of the tumor of the patient based on historical response data, can all be taken into account. Fortunately, when a larger tumor cell compartment than expected is seen an increased dose during the remainder of the treatment should always be delivered independently on whichever combination of the above deviations was the true reason. With high-energy photon and hadron therapy it is even possible to image the integral dose delivery in vivo during or after a treatment using PET-CT imaging. The high-energy photons above about 20 MeV produce positron emitters through photonuclear reactions in tissue which are proportional to the photon fluence and thus approximately also to the absorbed dose. Light ion beams, the ultimate radiation modality with regard to physical and biological selectivity, instead produce PET emitters through direct nuclear interactions in tissue, but can also be used as radioactive beams consisting of intrinsic PET emitters such as 8B, 11C, 13N and 15O. These radioactive beams allow more accurate imaging of the Bragg peak distribution and thus indirectly the absorbed dose. The most universal feedback for adaptive radiation therapy would then be to use the measured image of mean dose delivery during the early part of the treatment while revising the treatment plan based on the initially planned dose distribution and the radiation responsiveness of the tumor as seen after the first week or two of therapy. By this so-called BIO-ART approach (Biologically Optimized 3D in vivo predictive Assay-based Radiation Therapy) radiation therapy optimization may become an almost exact science, where the patient's true individual radiation response, considering hypoxia and general radiation resistance as well as possible dose delivery and planning errors, is taken into account.

Therapeutic treatment plan optimization with probability density-based dose prescription

The dose optimization in inverse planning is realized under the guidance of an objective function. The prescription doses in a conventional approach are usually rigid values, defining in most instances an ill-conditioned optimization problem. In this work, we propose a more general dose optimization scheme based on a statistical formalism [Xing et al., Med. Phys. 21, 2348-2358 (1999)]. Instead of a rigid dose, the prescription to a structure is specified by a preference function, which describes the user's preference over other doses in case the most desired dose is not attainable. The variation range of the prescription dose and the shape of the preference function are predesigned by the user based on prior clinical experience. Consequently, during the iterative optimization process, the prescription dose is allowed to deviate, with a certain preference level, from the most desired dose. By not restricting the prescription dose to a fixed value, the optimization problem becomes less ill-defined. The conventional inverse planning algorithm represents a special case of the new formalism. An iterative dose optimization algorithm is used to optimize the system. The performance of the proposed technique is systematically studied using a hypothetical C-shaped tumor with an abutting circular critical structure and a prostate case. It is shown that the final dose distribution can be manipulated flexibly by tuning the shape of the preference function and that using a preference function can lead to optimized dose distributions in accordance with the planner's specification. The proposed framework offers an effective mechanism to formalize the planner's priorities over different possible clinical scenarios and incorporate them into dose optimization. The enhanced control over the final plan may greatly facilitate the IMRT treatment planning process.

IMRT dose shaping with regionally variable penalty scheme

A commonly known deficiency of currently available inverse planning systems is the difficulty in fine-tuning the final dose distribution. In practice, it is not uncommon that just a few unsatisfactory regions in the planning target volume or an organ at risk prevent an intensity modulated radiation therapy (IMRT) plan from being clinically acceptable. The purpose of this work is to introduce a mechanism for controlling the regional doses after a conventional IMRT plan is obtained and to demonstrate its clinical utility. Two types of importance factors are introduced in the objective function to model the tradeoffs of different clinical objectives. The first is the conventional structure-dependent importance factor, which quantifies the interstructure tradeoff. The second type is the voxel-dependent importance factor which "modulates" the importance of different voxels within a structure. The planning proceeds in two major steps. First a conventional inverse planning is performed, where the structure-dependent importance factors are determined in a trial-and-error fashion. The next level of planning involves fine-tuning the regional doses to meet specific clinical requirements. To achieve this, the voxels where doses need to be modified are identified either graphically on the isodose layouts, or on the corresponding dose-volume histogram (DVH) curves. The importance value of these voxels is then adjusted to increase/decrease the penalty at the corresponding regions. The technique is applied to two clinical cases. It was found that both tumor hot spots and critical structure maximal doses can be easily controlled by varying the regional penalty. One to three trials were sufficient for the conventionally optimized dose distributions to be adjusted to meet clinical expectation. Thus introducing the voxel-dependent penalty scheme provides an effective means for IMRT dose distributions painting and sculpting.

Determination of beam intensity in a single step for IMRT inverse planning

In intensity modulated radiotherapy (IMRT), targets are treated by multiple beams at different orientations each with spatially-modulated beam intensities. This approach spreads the normal tissue dose to a greater volume and produces a higher dose conformation to the target. In general, inverse planning is used for IMRT treatment planning. The inverse planning requires iterative calculation of dose distribution in order to optimize the intensity profile for each beam and is very computation intensive. In this paper, we propose a single-step method utilizing a figure of merit (FoM) to estimate the beam intensities for IMRT treatment planning. The FoM of a ray is defined as the ratio between the delivered tumour dose and normal tissue dose and is a good index for the dose efficacy of the ray. To maximize the beam utility, it is natural to irradiate the tumour with intensity of each ray proportional to the value of the FoM. The nonuniform beam intensity profiles are then fixed and the weights of the beam are determined iteratively in order to yield a uniform tumour dose. In this study, beams are employed at equispaced angles around the patient. Each beam with its field size that just covers the tumour is divided into a fixed number of beamlets. The FoM is calculated for each beamlet and this value is assigned to be the beam intensity. Various weighting factors are incorporated in the FoM computation to accommodate different clinical considerations. Two clinical datasets are used to test the feasibility of the algorithm. The resultant dose-volume histograms of this method are presented and compared to that of conformal therapy. Preliminary results indicate that this method reduces the critical organ doses at a small expense of uniformity in tumour dose distribution. This method estimates the beam intensity in one single step and the computation time is extremely fast and can be finished in less than one minute using a regular PC.

Evaluation of concave dose distributions created using an inverse planning system

PURPOSE

To evaluate and develop optimum inverse treatment planning strategies for the treatment of concave targets adjacent to normal tissue structures.

METHODS AND MATERIALS

Optimized dose distributions were designed using an idealized geometry consisting of a cylindrical phantom with a concave kidney-shaped target (PTV) and cylindrical normal tissues (NT) placed 5-13 mm from the target. Targets with radii of curvature from 1 to 2.75 cm were paired with normal tissues with radii between 0.5 and 2.25 cm. The target was constrained to a prescription dose of 100% and minimum and maximum doses of 95% and 105% with relative penalties of 25. Maximum dose constraint parameters for the NT varied from 10% to 70% with penalties from 10 to 1000. Plans were evaluated using the PTV uniformity index (PTV D(max)/PTV D(95)) and maximum normal tissue doses (NT D(max)/PTV D(95)).

RESULTS

In nearly all situations, the achievable PTV uniformity index and the maximum NT dose exceeded the corresponding constraints. This was particularly true for small PTV-NT separations (5-8 mm) or strict NT dose constraints (10%-30%), where the achievable doses differed from the requested by 30% or more. The same constraint parameters applied to different PTV-NT separations yielded different dose distributions. For most geometries, a range of constraints could be identified that would lead to acceptable plans. The optimization results were fairly independent of beam energy and radius of curvature, but improved as the number of beams increased, particularly for small PTV-NT separations or strict dose constraints.

CONCLUSION

Optimized dose distributions are strongly affected by both the constraint parameters and target-normal tissue geometry. Standard site-specific constraint templates can serve as a starting point for optimization, but the final constraints must be determined iteratively for individual patients. A strategy whereby NT constraints and penalties are modified until the highest acceptable PTV uniformity index is achieved is discussed. This strategy can be used, in simple patient geometries, to ensure the lowest possible normal tissue dose. Strategies for setting the optimum dose constraints and penalties may vary for different optimization algorithms and objective functions. Increasing the number of beams can significantly improve normal tissue dose and target uniformity in situations where the PTV-NT separation is small or the normal tissue dose limits are severe. Setting unrealistically severe constraints in such situations often results in dose distributions that are inferior to plans achieved with more lenient constraints.

[IMCO(++)--a Monte Carlo based IMRT system]

The application of intensity modulated radiotherapy (IMRT) to dose escalation in the target volume sets particular demands in terms of accuracy of dose calculation. Dose calculation errors due to approximations are compensated by the optimization algorithm, a procedure that ultimately leads to incorrect fluence modulation. Such inaccuracies affect particularly the dose distribution in areas with secondary electron disequilibrium. In case tissues heterogeneity predominates, conventional dose calculation methods (such as Pencil Beam) can produce relative errors up to more than 10%. The accuracy can be significantly improved by the application of a Monte-Carlo (MC) algorithm. This paper describes a MC-based inverse treatment planning algorithm (IMCO++), based on a non-iterative approach with a feedback-controlling process. The convergence behavior of IMCO++ was investigated and the used MC dose-calculation codes MMms and XVMC were compared by means of a heterogeneous phantom. IMCO++ plans were optimized in various phantoms. All plans showed conformity in terms of dose distribution of the target volume and dose reduction in risk organs (according to the requirements of the target parameter), as well as a very fast convergence of the algorithm (in less than 10 optimization steps).

Issues in optimization for planning of intensity-modulated radiation therapy

The clinical use of intensity-modulated radiation therapy (IMRT) is expanding rapidly in academic and, more recently, in community-based radiotherapy centers due to a high level of clinician interest, improving reimbursement patterns, and the availability of the tools required to plan and deliver IMRT plans. These tools include inverse planning optimization algorithms and linear accelerator control systems with automated, multifield delivery capabilities. The hazards of this new technology are due primarily to the nonintuitive nature of the inverse planning process and the highly complex methods of delivery required for IMRT dose delivery. Important efforts are being made to define the required quality assurance for these computer-optimized IMRT plans and to find ways to reduce their complexity without reducing the quality of the resulting plans. By minimizing the complexity of these dose plans, one also minimizes the treatment time and the probability of dose delivery errors. Methods of optimization and evaluation of dose plans and practical considerations in inverse planning are discussed. In addition, this article points out the potential hazards of inverse-planned IMRT and discusses methods by which the complexity of these plans might be reduced.

Inverse planning algorithms for external beam radiation therapy

Intensity-modulated radiation therapy (IMRT) is a new treatment technique that has the potential to produce superior dose distributions to those of conventional techniques. An important step in IMRT is inverse planning, or optimization. This is a process by which the optimum intensity distribution is determined by minimizing (or maximizing) an objective function. For radiation therapy, the objective function is used to describe the clinical goals, which can be expressed in terms of dose and dose/volume requirements, or in terms of biological indices. There are 2 types of search algorithms, stochastic and deterministic. Typical algorithms that are currently in use are presented. For clinical implementations, other issues are also discussed, such as global minimum vs. local minima, dose uniformity in the target and sparing of normal tissues, smoothing of the intensity profile, and skin flash. To illustrate the advantages of IMRT, clinical examples for the treatment of the prostate, nasopharynx, and breast are presented. IMRT is an emerging technique that has shown encouraging results thus far. However, the technique is still in its infancy and more research and improvements are needed. For example, the effects of treatment uncertainties on the planning and delivery of IMRT requires further study. As with any new technology, IMRT should be used with great caution.

Dose optimization via index-dose gradient minimization

This paper presents an iterative optimization algorithm based on gradient minimization of index dose, defined as the product of physical dose and a numerical index. Acting as a template the index distribution is designed to represent the dosimetry that meets the dose volume histogram-based optimization objectives. The treatment dosimetry is optimized when the uniformity of the index-dose distribution is maximized. Prior to optimization the user can select all or only some of the beams to be intensity modulated. The remaining unmodulated beams can be either open or wedged photon beams, electron beams, or beams of previous treatments. The optimization result and treatment delivery efficiency can often be enhanced by including not only the IM photon beams but also all suitable fixed-beams available on the linac in the treatment plan. In addition, the doses from previous treatments can also be considered in the optimization of current treatment. Five clinical examples with different complexities in optimization objective are presented. The effects of two nonoptimization variables, beam setup and initial beam weights, on the quality of the dose optimization are also presented. The results are analyzed in terms of isodose distribution, dose volume histograms, and a dose optimization quality factor. The optimization algorithm, implemented in our in-house TPS PLanUNC, has been used in clinical application since 1996. The primary advantages of our optimization algorithm include computational efficiency, intensity modulation selection choice, and performance reliability for a wide range of clinical beam setups and optimization objectives.

Direct aperture optimization: a turnkey solution for step-and-shoot IMRT

IMRT treatment plans for step-and-shoot delivery have traditionally been produced through the optimization of intensity distributions (or maps) for each beam angle. The optimization step is followed by the application of a leaf-sequencing algorithm that translates each intensity map into a set of deliverable aperture shapes. In this article, we introduce an automated planning system in which we bypass the traditional intensity optimization, and instead directly optimize the shapes and the weights of the apertures. We call this approach "direct aperture optimization." This technique allows the user to specify the maximum number of apertures per beam direction, and hence provides significant control over the complexity of the treatment delivery. This is possible because the machine dependent delivery constraints imposed by the MLC are enforced within the aperture optimization algorithm rather than in a separate leaf-sequencing step. The leaf settings and the aperture intensities are optimized simultaneously using a simulated annealing algorithm. We have tested direct aperture optimization on a variety of patient cases using the EGS4/BEAM Monte Carlo package for our dose calculation engine. The results demonstrate that direct aperture optimization can produce highly conformal step-and-shoot treatment plans using only three to five apertures per beam direction. As compared with traditional optimization strategies, our studies demonstrate that direct aperture optimization can result in a significant reduction in both the number of beam segments and the number of monitor units. Direct aperture optimization therefore produces highly efficient treatment deliveries that maintain the full dosimetric benefits of IMRT.

On the implementation of dose-volume objectives in gradient algorithms for inverse treatment planning

A method that allows a straightforward implementation of dose-volume constraints in gradient algorithms for inverse treatment planning is presented. The method is consistent with the penalty function approach, which requires the formulation of an objective function with penalty terms proportional to the magnitudes of constraint violations. Dose constraints with respect to minimum and maximum target dose levels are incorporated in quadratic, dose-penalty terms. Analogously, quadratic volume-penalty terms in the objective function reflect the violation of dose-volume constraints imposing limits on the fractions of healthy organ volumes that can be irradiated above specified dose levels. It has been demonstrated that within the framework of this formulation neither modified objective functions nor finite difference gradient calculations are necessary for the incorporation of gradient minimization algorithms. As an example, a simple steepest descent algorithm is presented along with its application to illustrate prostate and lung cases.

[A simple dose-effect model for the optimization of IMRT]

Optimization of percutaneous photon beams with intensity modulation was investigated in terms of the influence of different dose-effect functions for lung tissue on the resulting dose distributions. The fluence profiles were optimized for a cylindrical phantom with a L-shaped target, the spinal cord and lung presenting the critical organs. Concurrent criteria were a minimum dose constraint for the target and a maximum dose constraint for the spinal cord. The dose effect in the lung was minimized using different approaches. All tested approaches were able to control the dose distribution in the lung. The mean dose remained constant, where as the volume of low dose could be changed. Due to the simplicity of functions and parameters, these models are suitable for clinical implementation.

Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose

PURPOSE

The equivalent uniform dose (EUD) for tumors is defined as the biologically equivalent dose that, if given uniformly, will lead to the same cell kill in the tumor volume as the actual nonuniform dose distribution. Recently, a new formulation of EUD was introduced that applies to normal tissues as well. EUD can be a useful end point in evaluating treatment plans with nonuniform dose distributions for three-dimensional conformal radiotherapy and intensity-modulated radiotherapy. In this study, we introduce an objective function based on the EUD and investigate the feasibility and usefulness of using it for intensity-modulated radiotherapy optimization.

METHODS AND MATERIALS

We applied the EUD-based optimization to obtain intensity-modulated radiotherapy plans for prostate and head-and-neck cancer patients and compared them with the corresponding plans optimized with dose-volume-based criteria.

RESULTS

We found that, for the same or better target coverage, EUD-based optimization is capable of improving the sparing of critical structures beyond the specified requirements. We also found that, in the absence of constraints on the maximal target dose, the target dose distributions are more inhomogeneous, with significant hot spots within the target volume. This is an obvious consequence of unrestricted maximization target cell kill and, although this may be considered beneficial for some cases, it is generally not desirable. To minimize the magnitude of hot spots, we applied dose inhomogeneity constraints to the target by treating it as a "virtual" normal structure as well. This led to much-improved target dose homogeneity, with a small, but expected, degradation in normal structure sparing. We also found that, in principle, the dose-volume objective function may be able to arrive at similar optimum dose distributions by using multiple dose-volume constraints for each anatomic structure and with considerably greater trial-and-error to adjust a large number of objective function parameters.

CONCLUSION

The general inference drawn from our investigation is that the EUD-based objective function has the advantages that it needs only a small number of parameters and allows exploration of a much larger universe of solutions, making it easier for the optimization system to balance competing requirements in search of a better solution.