Optimization of uncomplicated control for head and neck tumors

NTCP Modeling and Dose-Volume Correlations of Significant Hematocrit Drop 3 Months After Prostate Radiation Therapy

Purpose

Our purpose was to determine and model the dose-response relations of different parts of the pelvis regarding the endpoint of hematocrit level drop after pelvic radiation therapy (RT).

Methods and Materials

Two hundred and twenty-one patients treated with RT for prostate adenocarcinoma between 2014 and 2016 were included. All patients had complete blood counts collected at baseline and 3 months post-RT. The net difference of hematocrit level post-RT versus baseline was calculated, and the level of the 15th percentiles defined the thresholds of response in each case. The doses to 8 different pelvic structures were derived and fitted to the hematocrit levels using the relative seriality normal tissue complication probability model and the biologically equivalent uniform dose (D = ).

Results

Pelvic structures that correlated with significant decreases in hematocrit were the os coxae bilaterally superior to the acetabulum (OCUB), the total os coxae bilaterally, and the bone volume of the whole pelvis. The structure showing the highest correlation was OCUB with a maximum area under the curve (AUC) of 0.74. For V20 Gy < 30% the odds ratio was 9.8 with 95% CI of 2.9 to 32.9. For mean dose (Dmean) to OCUB, an AUC of 0.73 was observed where the dose threshold was 23 Gy and the odds ratio was 2.7 and 95% CI 1.3 to 5.6. The values for the D50, γ, and s parameters of the relative seriality model were 26.9 Gy (25.9-27.9), 1.3 (1.2-2.2), and 0.12 (0.10-0.83), respectively. The AUC of D = was 0.73 and patients with D = to OCUB ≥ 27 Gy had 8.2 times higher rate of significant hematocrit drop versus <27 Gy.

Conclusions

These findings confirm the association of radiation-induced damage to pelvic bone marrow with a drop in hematocrit. A threshold of V20 Gy < 30%, Dmean < 23 Gy, or D = < 27 Gy to OCUB may significantly reduce the risk for this endpoint.

Focal Boost in Prostate Cancer Radiotherapy: A Review of Planning Studies and Clinical Trials

BACKGROUND

Focal boost radiotherapy was developed to deliver elevated doses to functional sub-volumes within a target. Such a technique was hypothesized to improve treatment outcomes without increasing toxicity in prostate cancer treatment.

PURPOSE

To summarize and evaluate the efficacy and variability of focal boost radiotherapy by reviewing focal boost planning studies and clinical trials that have been published in the last ten years.

METHODS

Published reports of focal boost radiotherapy, that specifically incorporate dose escalation to intra-prostatic lesions (IPLs), were reviewed and summarized. Correlations between acute/late ≥G2 genitourinary (GU) or gastrointestinal (GI) toxicity and clinical factors were determined by a meta-analysis.

RESULTS

By reviewing and summarizing 34 planning studies and 35 trials, a significant dose escalation to the GTV and thus higher tumor control of focal boost radiotherapy were reported consistently by all reviewed studies. Reviewed trials reported a not significant difference in toxicity between focal boost and conventional radiotherapy. Acute ≥G2 GU and late ≥G2 GI toxicities were reported the most and least prevalent, respectively, and a negative correlation was found between the rate of toxicity and proportion of low-risk or intermediate-risk patients in the cohort.

CONCLUSION

Focal boost prostate cancer radiotherapy has the potential to be a new standard of care.

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.

A novel figure of merit to investigate 68Ga PET/CT image quality based on patient weight and lesion size using Q.Clear reconstruction algorithm: A phantom study

INTRODUCTION

Q.Clear is a Bayesian penalised-likelihood algorithm that uses a β-value for positron emission tomography(PET)/computed tomography(CT) image reconstruction(IR). Our study proposes a novel figure of merit, named CRBV, to compare the Q.Clear performances using 68Ga PET/CT image with the ordered-subset-expectation-maximization(OSEM) algorithm and to identify the optimal β-values for these images using two phantoms mimicking normal and overweight patients.

METHODS

NEMA IQ phantom with or without a ring of water-filled plastic bags (NEMAstd and NEMAow, respectively) was acquired and reconstructed with OSEM and Q.Clear at various β-values and minutes/bed position(min/bp). Contrast recovery(CR), background variability(BV) and CRBV were calculated. Highest CRBV values were used to identify optimal β-value ranges.

RESULTS

Q.Clear with 250 ≤ β ≤ 800 improved CRBV compared to OSEM for all the investigated spheres and acquisition setups. Outside of this range, Q.Clear still outperformed OSEM with few exceptions depending on spheres diameters and phantoms(e.g.,β-value = 1600 for diameters ≤ 17 mm using the NEMAow phantom). Regarding the CRBV performance for IR optimization, for the 4 min/bp NEMAstd IR, β-values = 300 ÷ 350 allowed to simultaneously optimize all diameters(except for the 10 mm); for the NEMAow IR, β-values = 350 ÷ 500 were needed for diameters > 20 mm, while β-values = 200 ÷ 250 were selected for the remaining diameters. For the 2 min/bp, β-value = 500 was suitable for diameters > 17 mm in both NEMAstd and NEMAow IR, while for smaller diameters β-value = 200 and β-values = 250 ÷ 350 were obtained for NEMAstd and NEMAow, respectively.

CONCLUSION

Almost all tested β-values of Q.Clear improved the CRBV compared to OSEM. In both phantoms, simulating normal and over-weight patients, optimal β-values were found according to lesion sizes and investigated acquisition times.

Dosimetric analysis and biological evaluation between proton radiotherapy and photon radiotherapy for the long target of total esophageal squamous cell carcinoma

Objective

The purpose of this study is to compare the dosimetric and biological evaluation differences between photon and proton radiation therapy.

Methods

Thirty esophageal squamous cell carcinoma (ESCC) patients were generated for volumetric modulated arc therapy (VMAT) planning and intensity-modulated proton therapy (IMPT) planning to compare with intensity-modulated radiation therapy (IMRT) planning. According to dose–volume histogram (DVH), dose–volume parameters of the plan target volume (PTV) and homogeneity index (HI), conformity index (CI), and gradient index (GI) were used to analyze the differences between the various plans. For the organs at risk (OARS), dosimetric parameters were compared. Tumor control probability (TCP) and normal tissue complication probability (NTCP) was also used to evaluate the biological effectiveness of different plannings.

Results

CI, HI, and GI of IMPT planning were significantly superior in the three types of planning (p < 0.001, p < 0.001, and p < 0.001, respectively). Compared to IMRT and VMAT planning, IMPT planning improved the TCP (p<0.001, p<0.001, respectively). As for OARs, IMPT reduced the bilateral lung and heart accepted irradiation dose and volume. The dosimetric parameters, such as mean lung dose (MLD), mean heart dose (MHD), V₅, V₁₀, and V₂₀, were significantly lower than IMRT or VMAT. IMPT afforded a lower maximum dose (D_max) of the spinal cord than the other two-photon plans. What’s more, the radiation pneumonia of the left lung, which was caused by IMPT, was lower than IMRT and VMAT. IMPT achieved the pericarditis probability of heart is only 1.73% ± 0.24%. For spinal cord myelitis necrosis, there was no significant difference between the three different technologies.

Conclusion

Proton radiotherapy is an effective technology to relieve esophageal cancer, which could improve the TCP and spare the heart, lungs, and spinal cord. Our study provides a prediction of radiotherapy outcomes and further guides the individual treatment.

Concepts and methods for the dosimetry of radioembolisation of the liver with Y-90-loaded microspheres

This article aims at presenting in a didactic way, dosimetry concepts and methods that are relevant for radio-embolization of the liver with 90Y-microspheres. The application of the medical internal radiation dose formalism to radio-embolization is introduced. This formalism enables a simplified dosimetry, where the absorbed dose in a given tissue depends on only its mass and initial activity. This is applied in the single-compartment method, partition model, for the liver, tumour and lung dosimetry, and multi-compartment method, allowing identification of multiple tumours. Voxel-based dosimetry approaches are also discussed. This allows taking into account the non-uniform uptake within a compartment, which translates into a non-uniform dose distribution, represented as a dose-volume histogram. For this purpose, dose-kernel convolution allows propagating the energy deposition around voxel-sources in a computationally efficient manner. Alternatively, local-energy deposition is preferable when the spatial resolution is comparable or larger than the beta-particle path. Statistical tools may be relevant in establishing dose-effect relationships in a given population. These include tools such as the logistic regression or receiver operator characteristic analysis. Examples are given for illustration purpose. Moreover, tumour control probability modelling can be assessed through the linear-quadratic model of Lea and Catcheside and its counterpart, the normal-tissue complication probability model of Lyman, which is suitable to the parallel structure of the liver. The selectivity of microsphere administration allows tissue sparing, which can be considered with the concept of equivalent uniform dose, for which examples are also given. The implication of microscopic deposition of microspheres is also illustrated through a liver toxicity model, even though it is not clinically validated. Finally, we propose a reflection around the concept of therapeutic index (TI), which could help tailor treatment planning by determining the treatment safety through the evaluation of TI based on treatment-specific parameters.

Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT-on-rails IGRT and patient-reported outcome scores

PURPOSE

To estimate the clinical impact of differences between delivered and planned dose using dose metrics and normal tissue complication probability (NTCP) modeling.

METHODS

Forty-six consecutive patients with prostate adenocarcinoma between 2010 and 2015 treated with intensity-modulated radiation therapy (IMRT) and who had undergone computed tomography on rails imaging were included. Delivered doses to bladder and rectum were estimated using a contour-based deformable image registration method. The bladder and rectum NTCP were calculated using dose-response parameters applied to planned and delivered dose distributions. Seven urinary and gastrointestinal symptoms were prospectively collected using the validated prostate cancer symptom indices patient reported outcome (PRO) at pre-treatment, weekly treatment, and post-treatment follow-up visits. Correlations between planned and delivered doses against PRO were evaluated in this study.

RESULTS

Planned mean doses to bladder and rectum were 44.9 ± 13.6 Gy and 42.8 ± 7.3 Gy, while delivered doses were 46.1 ± 13.4 Gy and 41.3 ± 8.7 Gy, respectively. D_10cc for rectum was 64.1 ± 7.6 Gy for planned and 60.1 ± 9.3 Gy for delivered doses. NTCP values of treatment plan were 22.3% ± 8.4% and 12.6% ± 5.9%, while those for delivered doses were 23.2% ± 8.4% and 9.9% ± 8.3% for bladder and rectum, respectively. Seven of 25 patients with follow-up data showed urinary complications (28%) and three had rectal complications (12%). Correlations of NTCP values of planned and delivered doses with PRO follow-up data were random for bladder and moderate for rectum (0.68 and 0.67, respectively).

CONCLUSION

Sensitivity of bladder to clinical variations of dose accumulation indicates that an automated solution based on a DIR that considers inter-fractional organ deformation could recommend intervention. This is intended to achieve additional rectum sparing in cases that indicate higher than expected dose accumulation early during patient treatment in order to prevent acute severity of bowel symptoms.

The radiobiological effect of using Acuros XB vs anisotropic analytical algorithm on hepatocellular carcinoma stereotactic body radiation therapy

The purpose of this work was to study the radiobiological effect of using Acuros XB (AXB) vs Analytic Anisotropic Algorithm (AAA) on hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT). Seventy SBRT volumetric modulated arc therapy (VMAT) plans for HCC were calculated using AAA and AXB respectively with the same treatment parameters. Published tumor control probability (TCP) and normal tissue complication probability (NTCP) models were used to quantify the effect of dosimetric difference between AAA and AXB on TCP, NTCP and uncomplicated tumor control probability (UTCP). There was an average decrease of 2.5% in 6-month TCP. Normal liver has the largest average decrease in NTCP which was 59.7%. Bowels followed with 26.6% average decrease in NTCP. Duodenum, stomach and esophagus had 10.2%, 5.1%, and 4.3% average decrease in NTCP. There was an average decrease of 1.8% and up to 7.2% in 6-month UTCP. There was an overall decrease in TCP, NTCP, and UTCP for HCC SBRT plans calculated using AXB compared to AAA which could be clinically significant.

Analysis of Hepatocellular Carcinoma Stereotactic Body Radiation Therapy Dose Prescription Method Using Uncomplicated Tumor Control Probability Model

Purpose

This work was to establish an uncomplicated tumor control probability (UTCP) model using hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT) clinical data in our institution. The model was then used to analyze the current dose prescription method and to seek the opportunity for improvement.

Methods and Materials

A tumor control probability (TCP) model was generated based on local clinical data using the maximum likelihood method. A UTCP model was then formed by combining the established TCP model with the normal tissue complication probability model based on the study by Dawson et al. The authors investigated the dependence of maximum achievable UTCP on planning target volume equivalent uniform dose (EUD) at various ratio between planning target volume EUD and normal liver EUD (T/N EUD ratios). A new term uncomplicated tumor control efficiency (UTCE) was also introduced to analyze the outcome. A UTCE value of 1 implied that the theoretical maximum UTCP for the corresponding T/N EUD ratio was achieved.

Results

The UTCE of the HCC SBRT patients based on the current dose prescription method was found to be 0.93 ± 0.05. It was found that the UTCE could be increased to 0.99 ± 0.03 by using a new dose prescription scheme, for which the UTCP could be maximized while keeping the normal tissue complication probability value smaller than 5%.

Conclusions

The dose prescription method of the current HCC SBRT in our institution was analyzed using a UTCP model established based on local clinical data. It was shown that there could be a potential to increase the prescription dose of HCC SBRT. A new dose prescription scheme was proposed to achieve better UTCP. Additional clinical trials would be required to validate the proposed dose prescription scheme in the future.

Radiobiological assessment of nasopharyngeal cancer IMRT using various collimator angles and non-coplanar fields

Aim:

The aim of this study was to evaluate clinical efficacy and radiobiological outcome of intensity-modulated radiation therapy (IMRT) modalities using various collimator angles and non-coplanar fields for nasopharyngeal cancer (NPC).

Materials and methods:

A 70-Gy planning target volume dose was administered for 30 NPC patients referred for IMRT. Standard IMRT plans were constructed based on the target and organs at risk (OARs) volume; and dose constraints recommended by Radiation Therapy Oncology Group (RTOG). Using various collimator angles and non-coplanar fields, 11 different additional IMRT protocols were investigated. Homogeneity indexes (HIs) and conformation numbers (CNs) were calculated. Poisson and relative seriality models were utilised for estimating tumour control probability (TCP) and normal tissue complication probabilities (NTCPs), respectively.

Results:

Various collimator angles and non-coplanar fields had no significant effect on HI, CN and TCP, while significant effects were noted for some OARs, with a maximum mean dose (Dmax). No significant differences were observed among the calculated NTCPs of all the IMRT protocols. However, the protocol with 10° collimator angle (for five fields out of seven) and 8° couch angle had the lowest NTCP. Furthermore, the standard and some of non-coplanar IMRT protocols led to the reduction in OARs Dmax.

Conclusions:

Using appropriate standard/non-coplanar IMRT protocols for NPC treatment could potentially reduce the dose to the OARs and the probability of inducing secondary cancer in patients.

Proposals of models for new formulations of the current complication-free cure (P+) and uncomplicated tumor control probability (UTCP) concepts, and total normal tissue complication probability of late complications

This study proposes phenomenological models for total normal tissue complication probability (TNTCP) and NTCP0. NTCP0 is a new acronym for reformulating the current complication-free cure (P+) and uncomplicated tumor control probability (UTCP) concepts, and TNTCP will reformulate the current NTCP involving multiple organs at risks. The current probabilistic concepts are incoherently formulated with mathematical operations of tumor control probability (TCP) and normal tissue complication probability (NTCP) that are associated with different stochastic processes and random variables. NTCP0 is equal to NTCP₀ (normal tissue non-complication probability) that is calculated as the ratio of a number of patients of a population without late complications and a total of them. As a cumulative distribution function (CDF) of late complications, TNTCP = sum(NTCP_i), where NTCP_i is the NTCP of the i^th late complication. TNTCP is also a new acronym, and the probabilistic complement of NTCP0, then NTCP0 = 100% - TNTCP. The NTCP0/TNTCP (D(d)) proposing models are based on the relationship between the NTCP0/TNTCP and total dose (D = n×d; where d = dose per fraction, and n = number of fractions). TNTCP(D) model will be correlated with LKB model (the normal CDF) that is an increasing function; and NTCP0(D) model with a decreasing function, which additionally will define clear limits of three possible regions for NTCP0: 0 and 100% deterministic, and a stochastic. These models are function D, which is widely used for characterizing radiation therapies.

Study of Asymmetric Margins in Prostate Cancer Radiation Therapy Using Fuzzy Logic

PURPOSE

The purpose of present study is to estimate asymmetric margins of prostate target volume based on biological limitations with help of knowledge based fuzzy logic considering the effect of organ motion and setup errors.

MATERIALS AND METHODS

A novel application of fuzzy logic modelling technique considering radiotherapy uncertainties including setup, delineation and organ motion was used in this study to derive margins. The new margin was applied in prostate cancer treatment planning and the results compared very well to current techniques Here volumetric modulated arc therapy treatment plans using stepped increments of asymmetric margins of planning target volume (PTV) were performed to calculate the changes in prostate radiobiological indices and results were used to formulate the rule based and membership function for Mamdani-type fuzzy inference system. The optimum fuzzy rules derived from input data, the clinical goals and knowledge-based conditions imposed on the margin limits. The PTV margin obtained using the fuzzy model was compared to the commonly used margin recipe.

RESULTS

For total displacement standard errors ranging from 0 to 5 mm the fuzzy PTV margin was found to be up to 0.5 mm bigger than the vanHerk derived margin, however taking the modelling uncertainty into account results in a good match between the PTV margin calculated using our model and the one based on van Herk et al. formulation for equivalent errors of up to 5 mm standard deviation (s. d.) at this range. When the total displacement standard errors exceed 5 mm s. d., the fuzzy margin remained smaller than the van Herk margin.

CONCLUSION

The advantage of using knowledge based fuzzy logic is that a practical limitation on the margin size is included in the model for limiting the dose received by the critical organs. It uses both physical and radiobiological data to optimize the required margin as per clinical requirement in real time or adaptive planning, which is an improvement on most margin models which mainly rely on physical data only.

Incorporating biological modeling into patient‐specific plan verification

Purpose

Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods.

Methods

A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient‐specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system.

Results

The complication‐free tumor control probability, P₊, has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (−3.9% ± 5.8%), head‐neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%).

Conclusion

Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.

Dosimetric Impact of Interfractional Variations for Post-prostatectomy Radiotherapy to the Prostatic Fossa-Relevance for the Frequency of Position Verification Imaging and Treatment Adaptation

Background and purpose: To analyze divergences between the planned and applied treatment doses for post-prostatectomy radiotherapy to the prostatic fossa on a voxel-by-voxel basis based on interfractional anatomic variations and imaging frequency. Materials and methods: For 10 patients receiving intensity-modulated postoperative radiotherapy to the prostatic fossa, position verification was carried out by daily in-room CT imaging in treatment position (340 fraction CTs). Applied fraction doses were recalculated on daily CT scans, and treatment doses were accumulated on a voxel-by-voxel basis after deformable image registration. To simulate weekly imaging, derived weekly position correction vectors were used to rigidly register all daily scans of the respective treatment week onto the planning CT before dose accumulation. Detailed dose statistics of the prescribed and applied treatment doses were compared in relation to the frequency of position verification imaging. Derived NTCP and P_injury values were calculated for the rectum and bladder. Results: Despite a large variability in the pelvic anatomy, daily CT-based patient repositioning resulted in largely negligible deviations of the analyzed dose-volume, conformity, and uniformity parameters from the planned doses for post-prostatectomy radiotherapy, and only the bladder exhibited significant increases in the accumulated mean and median doses. Derived NTCP for the applied doses to the rectum and bladder and P_injury values did not significantly deviate from the treatment plan. In contrast, weekly CT-based repositioning resulted in significant decreases of the PTV coverage and dose conformity as well as large deviations of the applied doses to the rectum and bladder from the planned doses. Consecutively, NTCP for the rectum and P_injury were found falsely reduced for weekly patient repositioning. Conclusions: Our data indicate for the first time in a voxel-by-voxel analysis that daily imaging is required for reliable adaptive delivery of intensity-modulated radiotherapy to the prostatic fossa. This work will help guiding adaptive treatment strategies for post-prostatectomy radiotherapy.

Dosimetric Impact of Interfractional Variations in Prostate Cancer Radiotherapy-Implications for Imaging Frequency and Treatment Adaptation

Background and purpose: To analyze deviations of the applied from the planned doses on a voxel-by-voxel basis for definitive prostate cancer radiotherapy depending on anatomic variations and imaging frequency.

Materials and methods: Daily in-room CT imaging was performed in treatment position for 10 patients with prostate cancer undergoing intensity-modulated radiotherapy (340 fraction CTs). Applied fraction doses were recalculated on daily images, and voxel-wise dose accumulation was performed using a deformable registration algorithm. For weekly imaging, weekly position correction vectors were derived and used to rigidly register daily scans of that week to the planning CT scan prior to dose accumulation. Applied and prescribed doses were compared in dependence of the imaging frequency, and derived TCP and NTCP values were calculated.

Results: Daily CT-based repositioning resulted in non-significant deviations of all analyzed dose-volume, conformity and uniformity parameters to the CTV, bladder and rectum irrespective of anatomic changes. Derived average TCP values were comparable, and NTCP values for the applied doses to the bladder and rectum did not significantly deviate from the planned values. For weekly imaging, the applied D₂ to the CTV, rectum and bladder significantly varied from the planned doses, and the CTV conformity index and D₉₈ decreased. While TCP values were comparable, the NTCP for the bladder erroneously appeared reduced for weekly repositioning.

Conclusions: Based on daily diagnostic quality CT imaging and voxel-wise dose accumulation, we demonstrated for the first time that daily, but not weekly imaging resulted in only negligible deviations of the applied from the planned doses for prostate intensity-modulated radiotherapy. Therefore, weekly imaging may not be adequately reliable for adaptive treatment delivery techniques for prostate. This work will contribute to devising adaptive re-planning strategies for prostate radiotherapy.

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with computer-simulation study

In the majority of current radiation therapy (RT) applications, image quality is still assessed subjectively or by utilizing physical measures. A novel theory that applies objective task-based image quality assessment in radiation therapy (IQA-in-RT) was recently proposed, in which the area under the therapeutic operating characteristic curve (AUTOC) was employed as the figure-of-merit (FOM) for evaluating RT effectiveness. Although theoretically more appealing than conventional subjective or physical measures, a comprehensive implementation and evaluation of this novel task-based IQA-in-RT theory is required for its further application in improving clinical RT. In this work, a practical and modular IQA-in-RT framework is presented for implementing this theory for the assessment of imaging components on the basis of RT treatment outcomes. Computer-simulation studies are conducted to demonstrate the feasibility and utility of the proposed IQA-in-RT framework in optimizing x-ray computed tomography (CT) pre-treatment imaging, including the optimization of CT imaging dose and image reconstruction parameters. The potential advantages of optimizing imaging components in the RT workflow by use of the AUTOC as the FOM are also compared against those of other physical measures. The results demonstrate that optimization using the AUTOC leads to selecting different parameters from those indicated by physical measures, potentially improving RT performance. The sources of systemic randomness and bias that affect the determination of the AUTOC are also analyzed. The presented work provides a practical solution for the further investigation and analysis of the task-based IQA-in-RT theory and advances its applications in improving RT clinical practice and cancer patient care.

Calculating the individualized fraction regime in stereotactic body radiotherapy for non-small cell lung cancer based on uncomplicated tumor control probability function

BACKGROUND

To calculate the individualized fraction regime (IFR) in stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) patients using the uncomplicated tumor control probability (UTCP, P⁺) function.

METHODS

Thirty-three patients with peripheral lung cancer or lung metastases who had undergone SBRT were analyzed. Treatment planning was performed using the dose regime of 48 Gy in 4 fractions. Dose volume histogram (DVH) data for the gross tumor volume (GTV), lung, chest wall (CW) and rib were exported and the dose bin was multiplied by a certain percentage of the dose in that bin which ranged from 1 to 200% in steps of 1%. For each dose fraction, P⁺ values were calculated by considering the tumor control probability (TCP), radiation-induced pneumonitis (RIP), chest wall pain (CWP) and radiation-induced rib fracture (RIRF). UTCP values as a function of physical dose were plotted and the maximum P⁺ values corresponded to the optimal therapeutic gain. The IFR in 3 fractions was also calculated with the same method by converting the dose using the linear quadratic (LQ) model.

RESULTS

Thirty-three patients attained an IFR using the introduced methods. All the patients achieved a TCP value higher than 92.0%. The IFR ranged from 3 × 10.8 Gy to 3 × 12.5 Gy for 3 fraction regimes and from 4 × 9.2 Gy to 4 × 10.7 Gy for 4 fraction regimes. Four patients with typical tumor characteristics demonstrated that the IFR was patient-specific and could maximize the therapeutic gain. Patients with a large tumor had a lower TCP and UTCP and a smaller fractional dose than patients with a small tumor. Patients with a tumor adjacent to the organ at risk (OAR) or at a high risk of RIP had a lower UTCP and a smaller fractional dose compared with patients with a tumor located distant from the OAR.

CONCLUSIONS

The proposed method is capable of predicting the IFR for NSCLC patients undergoing SBRT. Further validation in clinical samples is required.

Objective assessment of the effects of tumor motion in radiation therapy

PURPOSE

Internal organ motion reduces the accuracy and efficacy of radiation therapy. However, there is a lack of tools to objectively (based on a medical or scientific task) assess the dosimetric consequences of motion, especially on an individual basis. We propose to use therapy operating characteristic (TOC) analysis to quantify the effects of motion on treatment efficacy for individual patients. We demonstrate the application of this tool with pancreatic stereotactic body radiation therapy (SBRT) clinical data and explore the origin of motion sensitivity.

METHODS

The technique is described as follows. (a) Use tumor-motion data measured from patients to calculate the motion-convolved dose of the gross tumor volume (GTV) and the organs at risk (OARs). (b) Calculate tumor control probability (TCP) and normal tissue complication probability (NTCP) from the motion-convolved dose-volume histograms. (c) Construct TOC curves from TCP and NTCP models. (d) Calculate the area under the TOC curve (AUTOC) and use it as a figure of merit for treatment efficacy. We used tumor motion data measured from patients to calculate the relation between AUTOC and motion magnitude for 25 pancreatic SBRT treatment plans. Furthermore, to explore the driving factor of motion sensitivity of a given plan, we compared the dose distribution of motion-sensitive plans and motion-robust plans and studied the dependence of motion sensitivity to motion directions.

RESULTS

Our technique is able to recognize treatment plans that are sensitive to motion. Under the presence of motion, the treatment efficacy of some plans changes from providing high tumor control and low risks of complications to providing no tumor control and high risks of side effects. Several treatment plans experience falloffs in AUTOC at a smaller magnitude of motion than other plans. In our dataset, a potential indicator of a motion-sensitive treatment plan is that the duodenum is in proximity to the tumor in the SI direction.

CONCLUSIONS

The TOC framework can serve as a tool to quantify the effects of internal organ motion in radiation therapy. With pancreatic SBRT clinical data, we applied this tool to study the change in treatment efficacy induced by motion for individual treatment plans. This framework could potentially be used clinically to understand the effects of motion in an individual patient and to design a patient-specific motion management plan. This framework could also be used in research to evaluate different components of the treatment process, such as motion-management techniques, treatment-planning algorithms, and treatment margins.

Verification of calculations carried out with the Eclipse treatment planning system

The goal of radiotherapy is to deliver prescribed dose to the target volume and simultaneously minimize the dose to the healthy organs. The purpose of this work was to verify the accuracy of calculations carried out with a treatment planning system (TPS). Measurements carried out with thermoluminescence detectors (TLDs) were compared with doses calculated with TPS. Doses were measured and calculated both in the open beam’s region and under individual blocks. Measurements were performed in the Randophantom. The work was carried out for photon beams generated in the Varian CLINAC 2100C accelerator. The maximum / minimum percentage differences between measured and calculated doses were 4.9/0.6%, 2.6/0%, and 3.5%/0.5% in open, shielded and partially shielded points, respectively. Differences between the measured and calculated doses were within acceptable limits.

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.

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.

Stereotactic ablative radiotherapy for ultra-central lung tumors: prioritize target coverage or organs at risk?

Background

Lung stereotactic ablative radiotherapy (SABR) is associated with low morbidity, however there is an increased risk of treatment-related toxicity in tumors directly abutting or invading the proximal bronchial tree, termed ‘ultra-central’ tumors. As there is no consensus regarding the optimal radiotherapy treatment regimen for these tumors, we performed a modeling study to evaluate the trade-offs between predicted toxicity and local control for commonly used high-precision dose-fractionation regimens.

Methods

Ten patients with ultra-central lung tumors were identified from our institutional database. New plans were generated for 3 different hypofractionated schemes: 50 Gy in 5 fractions, 60 Gy in 8 fractions and 60 Gy in 15 fractions. For each regimen, one plan was created that prioritized planning target volume (PTV) coverage, potentially at the expense of organ at risk (OAR) tolerance, and a second that compromised PTV coverage to respect OAR dose constraints. Published radiobiological models were employed to evaluate competing treatment plans based on estimates for local control and the likelihood for toxicity to OAR.

Results

The risk of esophageal or pulmonary toxicity was low (< 5%) in all scenarios. When PTV coverage was prioritized, tumor control probabilities were 92.9% for 50 Gy in 5 fractions, 92.4% for 60 Gy in 8 fractions, and 52.0% for 60 Gy in 15 fractions; however the estimated risk of grade ≥ 4 toxicity to the proximal bronchial tree was 68%, 44% and 2% respectively. When dose to OAR was prioritized, the risk of major pulmonary toxicity was reduced to < 1% in all schemes, but this compromise reduced tumor control probability to 60.3% for 50 Gy in 5 fractions, 65.7% for 60 Gy in 8 fractions and 47.8% for 60 Gy in 15 fractions.

Conclusions

The tradeoff between local control and central airway toxicity are considerable in the use of 3 commonly used hypofractionated radiotherapy regimens for ultra-central lung cancer. The results of this planning study predict that the best balance may be achieved with 60 Gy in 8 fractions compromising PTV coverage as required to maintain acceptable doses to OAR. A prospective phase I trial (SUNSET) is planned to further evaluate this challenging clinical scenario.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1001-6) contains supplementary material, which is available to authorized users.

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.

Deep reinforcement learning for automated radiation adaptation in lung cancer

PURPOSE

To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).

METHODS

In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL.

RESULTS

Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose escalation/de-escalation between 1.5 and 3.8 Gy, a range similar to that used in the clinical protocol. The same DQN yielded two patterns of dose escalation for the 34 test patients, but with different reward variants. First, using the baseline P+ reward function, individual adaptive fraction doses of the DQN had similar tendencies to the clinical data with an RMSE = 0.76 Gy; but adaptations suggested by the DQN were generally lower in magnitude (less aggressive). Second, by adjusting the P+ reward function with higher emphasis on mitigating local failure, better matching of doses between the DQN and the clinical protocol was achieved with an RMSE = 0.5 Gy. Moreover, the decisions selected by the DQN seemed to have better concordance with patients eventual outcomes. In comparison, the traditional temporal difference (TD) algorithm for reinforcement learning yielded an RMSE = 3.3 Gy due to numerical instabilities and lack of sufficient learning.

CONCLUSION

We demonstrated that automated dose adaptation by DRL is a feasible and a promising approach for achieving similar results to those chosen by clinicians. The process may require customization of the reward function if individual cases were to be considered. However, development of this framework into a fully credible autonomous system for clinical decision support would require further validation on larger multi-institutional datasets.

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.

Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms

RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.

Impact of dose calculation models on radiotherapy outcomes and quality adjusted life years for lung cancer treatment: do we need to measure radiotherapy outcomes to tune the radiobiological parameters of a normal tissue complication probability model?

BACKGROUND

The equivalent uniform dose (EUD) radiobiological model can be applied for lung cancer treatment plans to estimate the tumor control probability (TCP) and the normal tissue complication probability (NTCP) using different dose calculation models. Then, based on the different calculated doses, the quality adjusted life years (QALY) score can be assessed versus the uncomplicated tumor control probability (UTCP) concept in order to predict the overall outcome of the different treatment plans.

METHODS

Nine lung cancer cases were included in this study. For the each patient, two treatments plans were generated. The doses were calculated respectively from pencil beam model, as pencil beam convolution (PBC) turning on 1D density correction with Modified Batho's (MB) method, and point kernel model as anisotropic analytical algorithm (AAA) using exactly the same prescribed dose, normalized to 100% at isocentre point inside the target and beam arrangements. The radiotherapy outcomes and QALY were compared. The bootstrap method was used to improve the 95% confidence intervals (95% CI) estimation. Wilcoxon paired test was used to calculate P value.

RESULTS

Compared to AAA considered as more realistic, the PBC_MB overestimated the TCP while underestimating NTCP, P<0.05. Thus the UTCP and the QALY score were also overestimated.

CONCLUSIONS

To correlate measured QALY's obtained from the follow-up of the patients with calculated QALY from DVH metrics, the more accurate dose calculation models should be first integrated in clinical use. Second, clinically measured outcomes are necessary to tune the parameters of the NTCP model used to link the treatment outcome with the QALY. Only after these two steps, the comparison and the ranking of different radiotherapy plans would be possible, avoiding over/under estimation of QALY and any other clinic-biological estimates.

Comparison of composite prostate radiotherapy plan doses with dependent and independent boost phases

Prostate cases commonly consist of dual phase planning with a primary plan followed by a boost. Traditionally, the boost phase is planned independently from the primary plan with the risk of generating hot or cold spots in the composite plan. Alternatively, boost phase can be planned taking into account the primary dose. The aim of this study was to compare the composite plans from independently and dependently planned boosts using dosimetric and radiobiological metrics. Ten consecutive prostate patients previously treated at our institution were used to conduct this study on the Raystation™ 4.0 treatment planning system. For each patient, two composite plans were developed: a primary plan with an independently planned boost and a primary plan with a dependently planned boost phase. The primary plan was prescribed to 54 Gy in 30 fractions to the primary planning target volume (PTV1) which includes prostate and seminal vesicles, while the boost phases were prescribed to 24 Gy in 12 fractions to the boost planning target volume (PTV2) that targets only the prostate. PTV coverage, max dose, median dose, target conformity, dose homogeneity, dose to OARs, and probabilities of benefit, injury, and complication-free tumor control (P+) were compared. Statistical significance was tested using either a 2-tailed Student's t-test or Wilcoxon signed-rank test. Dosimetrically, the composite plan with dependent boost phase exhibited smaller hotspots, lower maximum dose to the target without any significant change to normal tissue dose. Radiobiologically, for all but one patient, the percent difference in the P+ values between the two methods was not significant. A large percent difference in P+ value could be attributed to an inferior primary plan. The benefits of considering the dose in primary plan while planning the boost is not significant unless a poor primary plan was achieved.

SPIDERplan: A tool to support decision-making in radiation therapy treatment plan assessment

AIM

In this work, a graphical method for radiotherapy treatment plan assessment and comparison, named SPIDERplan, is proposed. It aims to support plan approval allowing independent and consistent comparisons of different treatment techniques, algorithms or treatment planning systems.

BACKGROUND

Optimized plans from modern radiotherapy are not easy to evaluate and compare because of their inherent multicriterial nature. The clinical decision on the best treatment plan is mostly based on subjective options.

MATERIALS AND METHODS

SPIDERplan combines a graphical analysis with a scoring index. Customized radar plots based on the categorization of structures into groups and on the determination of individual structures scores are generated. To each group and structure, an angular amplitude is assigned expressing the clinical importance defined by the radiation oncologist. Completing the graphical evaluation, a global plan score, based on the structures score and their clinical weights, is determined. After a necessary clinical validation of the group weights, SPIDERplan efficacy, to compare and rank different plans, was tested through a planning exercise where plans had been generated for a nasal cavity case using different treatment planning systems.

RESULTS

SPIDERplan method was applied to the dose metrics achieved by the nasal cavity test plans. The generated diagrams and scores successfully ranked the plans according to the prescribed dose objectives and constraints and the radiation oncologist priorities, after a necessary clinical validation process.

CONCLUSIONS

SPIDERplan enables a fast and consistent evaluation of plan quality considering all targets and organs at risk.

RADBIOMOD: A simple program for utilising biological modelling in radiotherapy plan evaluation

PURPOSE

Radiotherapy plan evaluation is currently performed by assessing physical parameters, which has many limitations. Biological modelling can potentially allow plan evaluation that is more reflective of clinical outcomes, however further research is required into this field before it can be used clinically.

METHODS

A simple program, RADBIOMOD, has been developed using Visual Basic for Applications (VBA) for Microsoft Excel that incorporates multiple different biological models for radiotherapy plan evaluation, including modified Poisson tumour control probability (TCP), modified Zaider-Minerbo TCP, Lyman-Kutcher-Burman normal tissue complication probability (NTCP), equivalent uniform dose (EUD), EUD-based TCP, EUD-based NTCP, and uncomplicated tumour control probability (UTCP). RADBIOMOD was compared to existing biological modelling calculators for 15 sample cases.

RESULTS

Comparing RADBIOMOD to the existing biological modelling calculators, all models tested had mean absolute errors and root mean square errors less than 1%.

CONCLUSIONS

RADBIOMOD produces results that are non-significantly different from existing biological modelling calculators for the models tested. It is hoped that this freely available, user-friendly program will aid future research into biological modelling.

Personalized dose selection in radiation therapy using statistical models for toxicity and efficacy with dose and biomarkers as covariates

Selection of dose for cancer patients treated with radiation therapy (RT) must balance the increased efficacy with the increased toxicity associated with higher dose. Historically, a single dose has been selected for a population of patients (e.g., all stage III non-small cell lung cancer). However, the availability of new biologic markers for toxicity and efficacy allows the possibility of selecting a more personalized dose. We consider the use of statistical models for toxicity and efficacy as a function of RT dose and biomarkers to select an optimal dose for an individual patient, defined as the dose that maximizes the probability of efficacy minus the sum of weighted toxicity probabilities. This function can be shown to be equal to the expected value of the utility derived from a particular family of bivariate outcome utility matrices. We show that if dose is linearly related to the probability of toxicity and efficacy, then any marker that only acts additively with dose cannot improve efficacy, without also increasing toxicity. Using a dataset of lung cancer patients treated with RT, we illustrate this approach and compare it to non-marker-based dose selection. Because typical metrics used in evaluating new markers (e.g., area under the ROC curve) do not directly address the ability of a marker to improve efficacy at a fixed probability of toxicity, we utilize a simulation study to assess the effects of marker-based dose selection on toxicity and efficacy outcomes.

γ+ index: A new evaluation parameter for quantitative quality assurance

PURPOSE

The accuracy of dose delivery and the evaluation of differences between calculated and delivered dose distributions, has been studied by several groups. The aim of this investigation is to extend the gamma index by including radiobiological information and to propose a new index that we will here forth refer to as the gamma plus (γ+). Furthermore, to validate the robustness of this new index in performing a quality control analysis of an IMRT treatment plan using pure radiobiological measures such as the biologically effective uniform dose (D) and complication-free tumor control probability (P+).

MATERIAL AND METHODS

A new quality assurance index, the (γ+), is proposed based on the theoretical concept of gamma index presented by Low et al. (1998). In this study, the dose difference, including the radiobiological dose information (biological effective dose, BED) is used instead of just the physical dose difference when performing the γ+ calculation. An in-house software was developed to compare different dose distributions based on the γ+ concept. A test pattern for a two-dimensional dose comparison was built using the in-house software platform. The γ+ index was tested using planar dose distributions (exported from the treatment planning system) and delivered (film) dose distributions acquired in a solid water phantom using a test pattern and a theoretical clinical case. Furthermore, a lung cancer case for a patient treated with IMRT was also selected for the analysis. The respective planar dose distributions from the treatment plan and the film were compared based on the γ+ index and were evaluated using the radiobiological measures of P+ and D.

RESULTS

The results for the test pattern analysis indicate that the γ+ index distributions differ from those of the gamma index since the former considers radiobiological parameters that may affect treatment outcome. For the theoretical clinical case, it is observed that the γ+ index varies for different treatment parameters (e.g. dose per fraction). The dose area histogram (DAH) from the plan and film dose distributions are associated with P+ values of 50.8% and 49.0%, for a D to the target of 54.0 Gy and 53.3 Gy, respectively.

CONCLUSION

The γ+ index shows advantageous properties in the quantitative evaluation of dose delivery and quality control of IMRT treatments because it includes information about the expected responses and radiobiological doses of the individual tissues.

Hypoxia-targeted radiotherapy dose painting for head and neck cancer using (18)F-FMISO PET: a biological modeling study

BACKGROUND

This study investigates the use of (18)F-fluoromisonidazole (FMISO) PET-guided radiotherapy dose painting for potentially overcoming the radioresistant effects of hypoxia in head and neck squamous cell carcinoma (HNSCC).

MATERIAL AND METHODS

The study cohort consisted of eight patients with HNSCC who were planned for definitive radiotherapy. Hypoxic subvolumes were automatically generated on pre-radiotherapy FMISO PET scans. Three radiotherapy plans were generated for each patient: a standard (STD) radiotherapy plan to a dose of 70 Gy, a uniform dose escalation (UDE) plan to the standard target volumes to a dose of 84 Gy, and a hypoxia dose-painted (HDP) plan with dose escalation only to the hypoxic subvolume to 84 Gy. Plans were compared based on tumor control probability (TCP), normal tissue complication probability (NTCP), and uncomplicated tumor control probability (UTCP).

RESULTS

The mean TCP increased from 73% with STD plans to 95% with the use of UDE plans (p < 0.001) and to 93% with HDP plans (p < 0.001). The mean parotid NTCP increased from 26% to 44% with the use of UDE plans (p = 0.003), and the mean mandible NTCP increased from 2% to 27% with the use of UDE plans (p = 0.001). There were no statistically significant differences between any of the NTCPs between the STD plans and HDP plans. The mean UTCP increased from 48% with STD plans to 66% with HDP plans (p = 0.016) and dropped to 37% with UDE plans (p = 0.138).

CONCLUSION

Hypoxia-targeted radiotherapy dose painting for head and neck cancer using FMISO PET is technically feasible, increases the TCP without increasing the NTCP, and increases the UTCP. This approach is superior to uniform dose escalation.

A gEUD-based inverse planning technique for HDR prostate brachytherapy: feasibility study

PURPOSE

The purpose of this work was to study the feasibility of a new inverse planning technique based on the generalized equivalent uniform dose for image-guided high dose rate (HDR) prostate cancer brachytherapy in comparison to conventional dose-volume based optimization.

METHODS

The quality of 12 clinical HDR brachytherapy implants for prostate utilizing HIPO (Hybrid Inverse Planning Optimization) is compared with alternative plans, which were produced through inverse planning using the generalized equivalent uniform dose (gEUD). All the common dose-volume indices for the prostate and the organs at risk were considered together with radiobiological measures. The clinical effectiveness of the different dose distributions was investigated by comparing dose volume histogram and gEUD evaluators.

RESULTS

Our results demonstrate the feasibility of gEUD-based inverse planning in HDR brachytherapy implants for prostate. A statistically significant decrease in D10 or/and final gEUD values for the organs at risk (urethra, bladder, and rectum) was found while improving dose homogeneity or dose conformity of the target volume.

CONCLUSIONS

Following the promising results of gEUD-based optimization in intensity modulated radiation therapy treatment optimization, as reported in the literature, the implementation of a similar model in HDR brachytherapy treatment plan optimization is suggested by this study. The potential of improved sparing of organs at risk was shown for various gEUD-based optimization parameter protocols, which indicates the ability of this method to adapt to the user's preferences.

Employing the therapeutic operating characteristic (TOC) graph for individualised dose prescription

Background

In current practice, patients scheduled for radiotherapy are treated according to ‘rigid’ protocols with predefined dose prescriptions that do not consider risk-taking preferences of individuals. The therapeutic operating characteristic (TOC) graph is applied as a decision-aid to assess the trade-off between treatment benefit and morbidity to facilitate dose prescription customisation.

Methods

Historical dose-response data from prostate cancer patient cohorts treated with 3D-conformal radiotherapy is used to construct TOC graphs. Next, intensity-modulated (IMRT) plans are generated by optimisation based on dosimetric criteria and dose-response relationships. TOC graphs are constructed for dose-scaling of the optimised IMRT plan and individualised dose prescription. The area under the TOC curve (AUC) is estimated to measure the therapeutic power of these plans.

Results

On a continuous scale, the TOC graph directly visualises treatment benefit and morbidity risk of physicians’ or patients’ choices for dose (de-)escalation. The trade-off between these probabilities facilitates the selection of an individualised dose prescription. TOC graphs show broader therapeutic window and higher AUCs with increasing target dose heterogeneity.

Conclusions

The TOC graph gives patients and physicians access to a decision-aid and read-out of the trade-off between treatment benefit and morbidity risks for individualised dose prescription customisation over a continuous range of dose levels.

Improving the therapeutic ratio in stereotactic radiosurgery: optimizing treatment protocols based on kinetics of repair of sublethal radiation damage

Sublethal damage after radiation exposure may become lethal or be repaired according to repair kinetics. This is a well-established concept in conventional radiotherapy. It also plays an important role in single-dose stereotactic radiotherapy treatments, often called stereotactic radiosurgery, when duration of treatment is extended due to source decay or treatment planning protocol. The purpose of this study is to look into the radiobiological characteristics of normal brain tissue and treatment protocols and find a way to optimize the time course of these protocols. The general problem is nonlinear and can be solved numerically. For numerical optimization of the time course of radiation protocol, a biexponential repair model with slow and fast components was considered. With the clinically imposed constraints of a fixed total dose and total treatment time, three parameters for each fraction (dose-rate, fraction duration, time of each fraction) were simultaneously optimized. A biological optimization can be performed by maximizing the therapeutic difference between tumor control probability and normal tissue complication probability. Specifically, for gamma knife radiosurgery, this approach can be implemented for normal brain tissue or tumor voxels separately in a treatment plan. Differences in repair kinetics of normal tissue and tumors can be used to find clinically optimized protocols. Thus, in addition to considering the physical dose in tumor and normal tissue, we also account for repair of sublethal damage in both these tissues.

Radiobiological Evaluation of Breast Cancer Radiotherapy Accounting for the Effects of Patient Positioning and Breathing in Dose Delivery. A Meta Analysis

In breast cancer radiotherapy, significant discrepancies in dose delivery can contribute to underdosage of the tumor or overdosage of normal tissue, which is potentially related to a reduction of local tumor control and an increase of side effects. To study the impact of these factors in breast cancer radiotherapy, a meta analysis of the clinical data reported by Mavroidis et al. (2002) in Acta Oncol (41:471–85), showing the patient setup and breathing uncertainties characterizing three different irradiation techniques, were employed. The uncertainties in dose delivery are simulated based on fifteen breast cancer patients (5 mastectomized, 5 resected with negative node involvement (R-) and 5 resected with positive node involvement (R+)), who were treated by three different irradiation techniques, respectively. The positioning and breathing effects were taken into consideration in the determination of the real dose distributions delivered to the CTV and lung in each patient. The combined frequency distributions of the positioning and breathing distributions were obtained by convolution. For each patient the effectiveness of the dose distribution applied is calculated by the Poisson and relative seriality models and a set of parameters that describe the dose-response relations of the target and lung. The three representative radiation techniques are compared based on radiobiological measures by using the complication-free tumor control probability, P+ and the biologically effective uniform dose, D̿ concepts. For the Mastectomy case, the average P+ values of the planned and delivered dose distributions are 93.8% for a D̿CTV of 51.8 Gy and 85.0% for a D̿CTV of 50.3 Gy, respectively. The respective total control probabilities, PB values are 94.8% and 92.5%, whereas the corresponding total complication probabilities, PI values are 0.9% and 7.4%. For the R- case, the average P+ values are 89.4% for a D̿CTV of 48.9 Gy and 88.6% for a D̿CTV of 49.0 Gy, respectively. The respective PB values are 89.8% and 89.9%, whereas the corresponding PI values are 0.4% and 1.2%. For the R+ case, the average P+ values are 86.1% for a D̿CTV of 49.2 Gy and 85.5% for a D̿CTV of 49.1 Gy, respectively. The respective PB values are 90.2% and 90.1%, whereas the corresponding PI values are 4.1% and 4.6%. The combined effects of positioning uncertainties and breathing can introduce a significant deviation between the planned and delivered dose distributions in lung in breast cancer radiotherapy. The positioning and breathing uncertainties do not affect much the dose distribution to the CTV. The simulated delivered dose distributions show larger lung complication probabilities than the treatment plans. This means that in clinical practice the true expected complications are underestimated. Radiation pneumonitis of Grade 1–2 is more frequent and any radiotherapy optimization should use this as a more clinically relevant endpoint.

Investigating the Clinical Aspects of Using CT vs. CT-MRI Images during Organ Delineation and Treatment Planning in Prostate Cancer Radiotherapy

In order to apply highly conformal dose distributions, which are characterized by steep dose fall-offs, it is necessary to know the exact target location and extension. This study aims at evaluating the impact of using combined CT-MRI images in organ delineation compared to using CT images alone, on the clinical results. For 10 prostate cancer patients, the respective CT and MRI images at treatment position were acquired. The CTV was delineated using the CT and MRI images, separately, whereas bladder and rectum were delineated using the CT images alone. Based on the CT and MRI images, two CTVs were produced for each patient. The mutual information algorithm was used in the fusion of the two image sets. In this way, the structures drawn on the MRI images were transferred to the CT images in order to produce the treatment plans. For each set of structures of each patient, IMRT and 3D-CRT treatment plans were produced. The individual treatment plans were compared using the biologically effective uniform dose ([Formula: see text]) and the complication-free tumor control probability ( P+) concepts together with the DVHs of the targets and organs at risk and common dosimetric criteria. For the IMRT treatment, at the optimum dose level of the average CT and CT-MRI delineated CTV dose distributions, the P+ values are 74.7% in both cases for a [Formula: see text] of 91.5 Gy and 92.1 Gy, respectively. The respective average total control probabilities, PB are 90.0% and 90.2%, whereas the corresponding average total complication probabilities, PI are 15.3% and 15.4%. Similarly, for the 3D-CRT treatment, the average P+ values are 42.5% and 46.7%, respectively for a [Formula: see text] of 86.4 Gy and 86.7 Gy, respectively. The respective average PB values are 80.0% and 80.6%, whereas the corresponding average PI values are 37.4% and 33.8%, respectively. For both radiation modalities, the improvement mainly stems from the better sparing of rectum. According to these results, the expected clinical effectiveness of IMRT can be increased by a maximum Δ P+ of around 0.9%, whereas of 3D-CRT by about 4.2% when combined CT-MRI delineation is performed instead of using CT images alone. It is apparent that in both IMRT and 3D-CRT radiation modalities, the better knowledge of the CTV extension improved the produced dose distribution. It is shown that the CTV is irradiated more effectively, while the complication probabilities of bladder and rectum, which is the principal organs at risk, are lower in the CT-MRI based treatment plans.

IsoBED: a tool for automatic calculation of biologically equivalent fractionation schedules in radiotherapy using IMRT with a simultaneous integrated boost (SIB) technique

Background

An advantage of the Intensity Modulated Radiotherapy (IMRT) technique is the feasibility to deliver different therapeutic dose levels to PTVs in a single treatment session using the Simultaneous Integrated Boost (SIB) technique. The paper aims to describe an automated tool to calculate the dose to be delivered with the SIB-IMRT technique in different anatomical regions that have the same Biological Equivalent Dose (BED), i.e. IsoBED, compared to the standard fractionation.

Methods

Based on the Linear Quadratic Model (LQM), we developed software that allows treatment schedules, biologically equivalent to standard fractionations, to be calculated. The main radiobiological parameters from literature are included in a database inside the software, which can be updated according to the clinical experience of each Institute. In particular, the BED to each target volume will be computed based on the alpha/beta ratio, total dose and the dose per fraction (generally 2 Gy for a standard fractionation). Then, after selecting the reference target, i.e. the PTV that controls the fractionation, a new total dose and dose per fraction providing the same isoBED will be calculated for each target volume.

Results

The IsoBED Software developed allows: 1) the calculation of new IsoBED treatment schedules derived from standard prescriptions and based on LQM, 2) the conversion of the dose-volume histograms (DVHs) for each Target and OAR to a nominal standard dose at 2Gy per fraction in order to be shown together with the DV-constraints from literature, based on the LQM and radiobiological parameters, and 3) the calculation of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) curve versus the prescribed dose to the reference target.

Radiobiological and Dosimetric Analysis of Daily Megavoltage CT Registration on Adaptive Radiotherapy with Helical Tomotherapy

Pre-treatment patient repositioning in highly conformal image-guided radiation therapy modalities is a prerequisite for reducing setup uncertainties. In Helical Tomotherapy (HT) treatment, a megavoltage CT (MVCT) image is usually acquired to evaluate daily changes in the patient's internal anatomy and setup position. This MVCT image is subsequently compared to the kilovoltage CT (kVCT) study that was used for dosimetric planning, by applying a registration process. This study aims at investigating the expected effect of patient setup correction using the Hi-Art tomotherapy system by employing radiobiological measures such as the biologically effective uniform dose ([Formula: see text]) and the complication-free tumor control probability ( P+). A new module of the Tomotherapy software (TomoTherapy, Inc, Madison, WI) called Planned Adaptive is employed in this study. In this process the delivered dose can be calculated by using the sinogram for each delivered fraction and the registered MVCT image set that corresponds to the patient's position and anatomical distribution for that fraction. In this study, patients treated for lung, pancreas and prostate carcinomas are evaluated by this method. For each cancer type, a Helical Tomotherapy plan was developed. In each cancer case, two dose distributions were calculated using the MVCT image sets before and after the patient setup correction. The fractional dose distributions were added and renormalized to the total number of fractions planned. The dosimetric and radiobiological differences of the dose distributions with and without patient setup correction were calculated. By using common statistical measures of the dose distributions and the P+ and [Formula: see text] concepts and plotting the tissue response probabilities vs. [Formula: see text] a more comprehensive comparison was performed based on radiobiological measures. For the lung cancer case, at the clinically prescribed dose levels of the dose distributions, with and without patient setup correction, the complication-free tumor control probabilities, P+ are 48.5% and 48.9% for a [Formula: see text] of 53.3 Gy. The respective total control probabilities, PB are 56.3% and 56.5%, whereas the corresponding total complication probabilities, PI are 7.9% and 7.5%. For the pancreas cancer case, at the prescribed dose levels of the two dose distributions, the P+ values are 53.7% and 45.7% for a [Formula: see text] of 54.7 Gy and 53.8 Gy, respectively. The respective PB values are 53.7% and 45.8%, whereas the corresponding PI values are ~0.0% and 0.1%. For the prostate cancer case, at the prescribed dose levels of the two dose distributions, the P+ values are 10.9% for a [Formula: see text] of 75.2 Gy and 11.9% for a [Formula: see text] of 75.4 Gy, respectively. The respective PB values are 14.5% and 15.3%, whereas the corresponding PI values are 3.6% and 3.4%. Our analysis showed that the very good daily patient setup and dose delivery were very close to the intended ones. With the exception of the pancreas cancer case, the deviations observed between the dose distributions with and without patient setup correction were within ±2% in terms of P+. In the radiobiologically optimized dose distributions, the role of patient setup correction using MVCT images could appear to be more important than in the cases of dosimetrically optimized treatment plans were the individual tissue radiosensitivities are not precisely considered.

Comparison of the helical tomotherapy against the multileaf collimator-based intensity-modulated radiotherapy and 3D conformal radiation modalities in lung cancer radiotherapy

OBJECTIVES

The aim of this study was to compare three-dimensional (3D) conformal radiotherapy and the two different forms of IMRT in lung cancer radiotherapy.

METHODS

Cases of four lung cancer patients were investigated by developing a 3D conformal treatment plan, a linac MLC-based step-and-shoot IMRT plan and an HT plan for each case. With the use of the complication-free tumour control probability (P(+)) index and the uniform dose concept as the common prescription point of the plans, the different treatment plans were compared based on radiobiological measures.

RESULTS

The applied plan evaluation method shows the MLC-based IMRT and the HT treatment plans are almost equivalent over the clinically useful dose prescription range; however, the 3D conformal plan inferior. At the optimal dose levels, the 3D conformal treatment plans give an average P(+) of 48.1% for a effective uniform dose to the internal target volume (ITV) of 62.4 Gy, whereas the corresponding MLC-based IMRT treatment plans are more effective by an average ΔP(+) of 27.0% for a Δ effective uniform dose of 16.3 Gy. Similarly, the HT treatment plans are more effective than the 3D-conformal plans by an average ΔP(+) of 23.8% for a Δ effective uniform dose of 11.6 Gy.

CONCLUSION

A radiobiological treatment plan evaluation can provide a closer association of the delivered treatment with the clinical outcome by taking into account the dose-response relations of the irradiated tumours and normal tissues. The use of P - effective uniform dose diagrams can complement the traditional tools of evaluation to compare and effectively evaluate different treatment plans.

Toolkit for determination of dose-response relations, validation of radiobiological parameters and treatment plan optimization based on radiobiological measures

Accurately determined dose-response relations of the different tumors and normal tissues should be estimated and used in the clinic. The aim of this study is to demonstrate developed tools that are necessary for determining the dose-response parameters of tumors and normal tissues, for clinically verifying already published parameter sets using local patient materials and for making use of all this information in the optimization and comparison of different treatment plans and radiation techniques. One of the software modules (the Parameter Determination Module) is designed to determine the dose-response parameters of tumors and normal tissues. This is accomplished by performing a maximum likelihood fitting to calculate the best estimates and confidence intervals of the parameters used by different radiobiological models. Another module of this software (the Parameter Validation Module) concerns the validation and compatibility of external or reported dose-response parameters describing tumor control and normal tissue complications. This is accomplished by associating the expected response rates, which are calculated using different models and published parameter sets, with the clinical follow-up records of the local patient population. Finally, the last module of the software (the Radiobiological Plan Evaluation Module) is used for estimating and optimizing the effectiveness a treatment plan in terms of complication-free tumor control, P(+). The use of the Parameter Determination Module is demonstrated by deriving the dose-response relation of proximal esophagus from head and neck cancer radiotherapy. The application of the Parameter Validation Module is illustrated by verifying the clinical compatibility of those dose-response parameters with the examined treatment methodologies. The Radiobiological Plan Evaluation Module is demonstrated by evaluating and optimizing the effectiveness of head and neck cancer treatment plans. The results of the radiobiological evaluation are compared against dosimetric criteria. The presented toolkit appears to be very convenient and efficient for clinical implementation of radiobiological modeling. It can also be used for the development of a clinical data and health information database for assisting the performance of epidemiological studies and the collaboration between different institutions within research and clinical frameworks.

Dose-painting IMRT optimization using biological parameters

PURPOSE

Our work on dose-painting based on the possible risk characteristics for local recurrence in tumor subvolumes and the optimization of treatment plans using biological objective functions that are region-specific are reviewed.

MATERIALS AND METHODS

A series of intensity modulated dose-painting techniques are compared to their corresponding intensity modulated plans in which the entire PTV is treated to a single dose level, delivering the same equivalent uniform dose (EUD) to the entire PTV. Iso-TCP and iso-NTCP maps are introduced as a tool to aid the planner in the evaluation of the resulting non-uniform dose distributions. Iso-TCP and iso-NTCP maps are akin to iso-dose maps in 3D conformal radiotherapy. The impact of the currently limited diagnostic accuracy of functional imaging on a series of dose-painting techniques is also discussed.

RESULTS

Utilizing biological parameters (risk-adaptive optimization) in the generation of dose-painting plans results in an increase in the therapeutic ratio as compared to conventional dose-painting plans in which optimization techniques based on physical dose are employed.

CONCLUSION

Dose-painting employing biological parameters appears to be a promising approach for individualized patient- and disease-specific radiotherapy.