Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology

The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows and technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided, as is a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided

Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

Robust Treatment Planning and Robustness Evaluation for Proton Therapy of Head and Neck Cancer

Intensity modulated proton therapy (IMPT) in head and neck squamous cell carcinoma (HNSCC) offers superior advantages over conventional photon therapy, by generating high conformal doses to the target volume and improved sparing of the organ at risks (OARs). Besides, robust treatment planning approaches, which account for uncertainties directly into the plan optimization process, are able to generate high quality plans robust against uncertainties compared to a PTV margin expansion approach. During radiation treatment, patients are prone to present anatomical variations during the treatment course, which can be random deviations in patient positioning, as well as treatment-induced tumor shrinkage and patient weight variations. For IMPT plans using a PTV margin expansion, these anatomical variations might disturb the calculated nominal plan, with a decrease to the dose delivered to the target volume and/or increased dose to the OARs above its tolerance, and a plan adaptation might be needed. However, the influence of these anatomical variations in robustly optimized plans for HNSCC entities has not been determined. The first part of this thesis compared two proton therapy methods, single-field optimization (SFO) and multi-field optimization (MFO), applied to the treatment of unilateral HNSCC target volumes, consisting of a cohort of 8 patients. For each method, a PTV-based and a robustly optimized plan were generated, resulting in four plans per patient. The four plans showed adequate target coverage on the nominal plan, with larger doses to the ipsilateral parotid gland for both SFO approaches. No plan showed a clear advantage when variations in the anatomy during the treatment course were considered, and the same was observe considering additional setup and range uncertainties. Hence, no plan showed a decisive superiority regarding plan robustness and potential need of replanning. In the second part of this thesis, an anatomical robustly optimized plan approach was proposed (aRO), which considers additional CT datasets in the plan optimization, representing random non-rigid patient positioning variations. The aRO approach was compared to a classical robustly optimized plan (cRO) and a PTV-based approach for a cohort of 20 bilateral HNSCC patients. PTV-based and cRO approaches were not sufficient to account for weekly anatomical variations, showing a degradation in the target coverage in 10 and 5 of 20 cases, respectively. Conversely, the proposed aRO approach was able to preserve the target coverage in 19 of 20 cases, with only one patient requiring plan adaptation. An extended robustness analysis conducted on both cRO and aRO plan approaches considering weekly anatomical variations, setup and range errors, showed that the variations in anatomy were the most critical variable for loss in target coverage, while setup and range uncertainties played a minor role. The price of the increased plan robustness for the aRO approach was a significant larger integral dose to the healthy tissue, compared to the cRO plan. However, the increase in integral dose was not reflected on the planned dose to the OARs, which were comparable between both plans. Therefore, the price for a superior plan robustness can be considered as low. In the current clinical practice, the implementation of the aRO approach would be able to reduce the need of plan adaptation. For its application, the acquisition of additional planning CT datasets, considering a complete patient repositioning between scans is required, in order to simulate random non-rigid position variations as simulated in this study by the use of the first two weekly cCTs in the plan optimization. Further studies using multiple planning CT acquisition, including strategies to reduce the patient CT dose such as dual-energy CT and iterative reconstruction algorithms, are needed to confirm the presented findings. Additionally, the aRO approach applied to other body sites and entities might also be investigated. In near future, further in-room imaging methods such as cone-beam CT and magnetic resonance imaging, optimized for proton therapy, might be used to acquire additional datasets. Moreover, alternative approaches capable of modeling variations in patient positioning as biomechanical models and deep learning methods might be able to generate in silico additional image datasets for use in proton treatment planning. In summary, this thesis proposes an additional contribution for robust treatment planning in IMPT, with the generation of treatment plans robust against anatomy variations, together with setup and range uncertainties, which can benefit the clinical workflow by reducing the need of plan adaptation.:Contents List of Figures List of Tables List of Abbreviations 1 Introduction 2 Proton Therapy 2.1 Rationale for Proton Therapy 2.2 Beam Delivery Techniques 2.2.1 Passive Scattering 2.2.2 Pencil Beam Scanning 2.3 Uncertainties in Proton Therapy 2.3.1 Target Volume Definition 2.3.2 Range Uncertainty 2.3.3 Setup Uncertainty 2.3.4 Biological Uncertainty 2.3.5 Anatomical Variations 3 Robust Treatment Planning and Robustness Evaluation 3.1 Robust Treatment Planning 3.1.1 Including Uncertainties in the Optimization 3.1.2 Differences Between Approaches 3.2 Robustness Evaluation 3.2.1 Error Scenarios 3.2.2 Visual Evaluation of Plan Robustness 3.2.3 Summary 4 Illustration of Robust Treatment Planning in a Simple Geometry 4.1 Plan Design 4.2 Plan Results 4.2.1 Doses on Nominal Plan 4.2.2 Influence of Uncertainties in Plan Robustness 4.3 Discussion and Conclusion 5 Evaluation of Robust Treatment Plans in Unilateral Head and Neck Squamous Cell Carcinoma 5.1 Study Design 5.1.1 Calculation Parameters 5.1.2 Plan Robustness Evaluation 5.2 Results 5.2.1 Evaluation of Nominal Plan Doses 5.2.2 Evaluation of Plan Robustness Against Uncertainties 5.3 Discussion 5.4 Conclusions 6 Assessment of Anatomical Robustly Optimized Plans in Bilateral Head and Neck Squamous Cell Carcinoma 6.1 Anatomical Robust Optimization 6.2 Study Design 6.2.1 Calculation Parameters 6.2.2 Assessment of Plan Robustness 6.3 Results 6.3.1 Evaluation of Nominal Plan Doses 6.3.2 Evaluation of Plan Robustness Against Uncertainties 6.4 Discussion 6.4.1 Robustness Against Anatomical Variations 6.4.2 Robustness Against Additional Setup and Range Uncertainties 6.4.3 Study Limitations 6.5 Conclusions 7 Summary 8 Zusammenfassung Bibliography Appendi

Impact of Biomechanical Modeling of Anatomical Variations on the Uncertainties of the Delivery and Understanding of Radiation Therapy

External beam radiation therapy is an effective and widely used focal cancer therapy. However, due to anatomical changes during radiation therapy, both in the tumor and in the normal tissue, the delivered radiation dose can deviate from the planned radiation dose. These responses may compromise the delivery of the most effective treatment and lead to an increased risk of complications in normal tissues. The ability to estimate the delivered radiation to the tumor and normal tissues with high accuracy requires modeling the patient response to dose. Modern medical imaging, such as computed tomography (CT) and medical resonance imaging (MRI), provides a method to evaluate spatial and functional changes of the tumor and normal tissue over the course of radiation therapy. A comprehensive evaluation of these changes requires identification of the tumor and normal tissue, through image segmentation, and accurate alignment of images, through image registration. In the head and neck region, varying angles of neck flexion, rapid tumor response and weight loss cause early changes in healthy tissue. In the abdominal region, motion due to breathing and digestion cause changes in the tumor position and normal structures. When the deviations between delivered and planned dose are great enough, the radiation treatment plan should be reoptimized, in order to ensure that the tumor is adequately treated and that the normal tissue is maximally avoided. Estimating the delivered dose to sufficient accuracy is therefore an important requirement for effective adaptive replanning. This dissertation work develops different techniques based on biomechanical models of the anatomical changes to improve estimates of delivered dose, which can ultimately lead to improvements in treatment adaptation strategies as well as a better understanding of toxicity. A series of experiments based on finite element modeling were conducted to model the uncertainties between planned and delivered dose, as well as the potential impact of modeling on different organ sites. Abdominal normal tissue complication probability models were developed based on estimated delivered dose and their accuracy compared to traditional models based on planned dose. Following this study, a predictive model was developed for the head and neck site, in order to find how early in treatment significant deviations in planned and delivered dose could be predicted. After seeing the large potential deviations between planned and delivered dose in the head and neck site, a comprehensive study was conducted to model the changes that potentially cause these large deviations. This comprehensive head and neck model was developed in two steps; first, the positional changes due to flexion were resolved and second, the dose response to the parotid glands was modeled using finite element modeling. Each clinical site poses different challenges, and this dissertation work highlights two areas in which modeling the deviations between planned and delivered dose will improve advanced adaptive radiation therapy.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149811/1/mollyma_1.pd

Bayesian modelling of organ deformations in radiotherapy

Moderne strålebehandling mot kreft er skreddarsydd for å gje ein høg stråledose tilpassa svulsten (målvolumet), mens så lite dose som mogleg vert gitt til det friske vevet omkring. Den totale dosen vert levert over nokre veker i daglege "fraksjonar", noko som reduserer biverknader. Under og mellom desse fraksjonane rører dei indre organa på seg heile tida på grunn av pust, fylling av blæra, tarmar si rørsle og ekstern påverknad. Likevel vert posisjonen til målvolumet og relevante risikoorgan bestemt på grunnlag av eit statisk 3D-skann som er tatt før behandlinga startar. Den vanlege måten å sikre seg mot konsekvensar av denne rørsla er å legge til marginar rundt svulsten. Slik sikrar ein å treffe målvolumet, men til gjengjeld får det friske vevet meir dose. Marginane sin storleik er fastsett ved hjelp av statistikk over tidlegare behandla pasienter. Dei statistiske metodane som vert brukte er ofte enkle, og tek berre omsyn til rigid rørsle, altså at heile kroppen rører seg i eitt. Dessutan vert det ikkje teke omsyn til rørsla til risikoorgan. For å berekne dose til risikoorgana er det vanleg å anta at forma til organa i planleggingsskannet er representative for forma deira under behandling. Arbeidet i denne avhandlinga handlar om å bruka teknikkar frå Bayesiansk statistikk for å modellere korleis organ rører og deformerer seg mellom fraksjonane. Målet er å estimere nøyaktig den statistiske fordelinga av rørsle for eit eller fleire organ til ein pasient. Fordelinga gjev innsikt i korleis organa forandre seg medan behandlinga går for seg. Denne innsikta er nyttig for evaluering av stråleterapiplanar, statistisk prediksjon av biverknader, såkalla robust planlegging og å berekna størrelsen på marginar. Metodane som vert presentert er evaluerte for endetarmen (rektum) sine rørsler hjå prostatakreftpasientar. For desse pasientane er rektum eit viktig risikoorgan, som kan bli ramma både av akutte og seine biverknader, som lekkasje, bløding og smerter. Samanlikna med eksisterande metodar har den Bayesianske tilnærminga to fordelar: For det første gir kombinasjonen av populasjonsstatistikk og individuelle data meir nøyaktige anslag av den pasientspesifikke fordelinga. For det andre estimerer dei nye metodane den såkalla systematiske feilen i tillegg til variasjonar frå fraksjon til fraksjon. Den systematiske feilen er forskjellen mellom den estimerte forma på organet under planlegging, og gjennomsnittsforma til organet under bestråling. Denne typen feil var tema for artikkel I. Her fekk vi til å redusere den systematiske feilen til rektum hjå 33 av 37 prostatakreftpasientar ved å bruke ein metode som kombinerer forma på rektum under planlegginga og gjennomsnittsforma i populasjonen. Vi vurderte og om denne forbetringa hadde påverknad på estimering av summert dose til rektum. Metoden gav ikkje signifikant forbetring for to antatt relevante parametrar (ekvivalent uniform dose og D5%), men gav signifikant reduksjon av bias på det estimerte dose-volum-histogrammet i intervallet 52.5 Gy til 65 Gy. Hovudarbeidet i dette prosjektet er publisert i artikkel II. Der presenterer vi to modellar for organrørsle basert på Bayesianske metodar. Inndata til desse metodane er organformer som er henta frå 3D-skanningar. Metodane kan ta ulikt tal slike former, og produserer meir nøyaktige resultat jo fleire former dei får. Dei gjev anslag av gjennomsnittsforma og kor stor uvissa om denne forma er, i tillegg til anslag av fordelinga av variasjon av former frå fraksjon til fraksjon. Vi evaluerte metodane etter kor godt dei kunne berekne "dekningssannsyn", altså sannsynet for at organet skal dekke eit gitt punkt i pasientkoordinatsystemet til ei gitt tid. For denne berekninga måtte titusenvis av organformer gjerast om til såkalla binærmasker, som er 3D-matriser av punkter i pasient-koordinatsystemet der verdien til eit punkt er 1 dersom punktet er inne i organet, og 0 elles. Denne berekninga var mogleg på grunn av programvare som blei implementert for dette prosjektet, og som er presentert i artikkel III. Også her var det prostatakreftpasientar sitt rektum som vart brukt til evaluering. Berekningane til dei nye metodane var likare det sanne dekningssannsynet enn tilsvarande berekningar frå tidlegare metodar, i signifikant grad, i alle fall opp til tre input. Forskjellen mellom dei to nye algoritmane er i hovudsak kompleksiteten og nøyaktigheita, og valet mellom algoritmane i ein gitt bruk vil vere ei avveging mellom desse faktorane. Vi viste ein måte modellane kan verte brukte i artikkel IV, som handlar om pasientar som får re-bestråling for tilbakefall av prostatakreft. Her brukte vi modellane til å berekne forventa akkumulert dose til rektum frå dei to behandlingane, og også uvissa rundt den forventa dosen. Metoden er basert på representative former" av rektum, altså former som rektum kan ta som er sannsynlege, men lite fordelaktige. Desse formene kan brukast som visuell hjelp for onkologar og doseplanleggjarar, og metoden kan implementerast ved hjelp av eksisterande funksjonar i programvaren for behandlingsplanlegging. Overordna gir denne avhandlinga nye løysingar for den sentrale utfordringa med å redusere konsekvensar av organrørsle i stråleterapi. Dei presenterte modellane er dei første som utnyttar statistikk for populasjonen og data frå den enkelte pasienten samstundes, og som tar omsyn til både systematiske og tilfeldige feil.Modern radiotherapy tends to be highly conformal, meaning that a high and uniform dose is delivered to the target volume and as little dose as possible to the surrounding normal tissue. The total radiation dose is delivered across several smaller daily fractions, typically spanning several weeks. During and between these fractions, internal organs are constantly in motion due to factors such as breathing, changes to bladder filling state, intestinal movement and external influences. Nevertheless, the position of the target and relevant organs at risk (OARs) are determined based on a static 3D scan acquired before start of treatment. A common safeguard which is used to take such motion into account is the addition of margins around the target. These margins reduce the chance of missing parts of the target, yet increases dose to the healthy tissue surrounding the target. The margin size is based on statistics from previous patients. However, for the most part, the statistical methods used are very simple, and typically based on an assumption of rigid patient motion. Similarly, motion of the OARs is commonly neglected. For estimation of dose to the OARs, it is common to assume that the organ shape at the static scan is representative for its shape during treatment. The work in this thesis concerns the use of techniques from Bayesian statistics for modelling inter-fraction organ motion and deformation. The goal is to estimate accurately the statistical distribution of shapes for one or more organs for a given patient. The distribution provides knowledge of how the patient's organs might move and deform during the radiotherapy course. This information is useful for the evaluation of radiotherapy plans, prediction of adverse effects, so-called motion-robust radiotherapy planning, the generation of margins and more. The methods presented in this thesis have been evaluated for predicting deformations of the rectum of prostate cancer patients. For these patients, the rectum is a crucial OAR that is affected by both early and late side effects including leakage, bleeding and pain. Compared to existing methods, the Bayesian approach developed and implemented in this thesis offers two advantages: first, combining population statistics and individual data leads to more accurate estimates of the patient-specific distribution. Secondly, the new methods estimate the distribution of the so-called systematic error in addition to variations from fraction to fraction. The systematic error is the difference between the estimated shape/position of an organ at the planning stage and its average shape/position during therapy, and was the subject of paper I. Here, we were able to reduce the systematic error of the rectum in 33 out of 37 prostate cancer patients using a straightforward method to combine the shape of the rectum at the planning CT with the population mean shape. We also evaluated the impact of this improvement on the estimation of dose to the rectum. We found no significant improvement on the estimation of two presumably relevant dose parameters (equivalent uniform dose and D5%). However, we did find significant reduction in the bias of the estimated dose-volume histogram in the range from 52.5 Gy to 65 Gy. Paper II contains the central work of this project. It presents two organ deformation models based on Bayesian methods. The input data to these algorithms are organ shapes derived from 3D scans. The methods can take a varying number of such inputs from a given patient, and will produce more accurate results the more inputs they are given. They provide an estimate of the mean shape of the organ, as well as the uncertainty of this mean, in addition to the distribution of the variation of shapes from fraction to fraction. The methods were evaluated in the task of estimating coverage probabilities, i.e. the probability that the organ will cover a certain point in the patient coordinate system, for the rectum of prostate cancer patients. For this evaluation, tens of thousands of organ shapes needed to be converted to so-called binary masks, which are 3D arrays of points in the patient coordinate system where the value of each point is 1 if the point is inside the organ and 0 if it is outside. This was enabled by the highly efficient point-in-polyhedron software presented in paper III, which was developed for this project. The models were given varying number of scans, from 1 to 10, as input, and compared to two existing (non-Bayesian) models. The estimates of the coverage probability produced by the new models were significantly more similar to the ground truth than those produced by the existing models, at least up to three input scans. The main differences between the two new algorithms are their of conceptual complexity and accuracy, and the choice of method in a given application will therefore come down to a trade-off between these qualities. An application for the models derived in paper II, concerning patients receiving re-irradiation for recurrent prostate cancer, is presented in paper IV. We introduce a way of estimating the expectation and uncertainty of the accumulated dose to the rectum from the two treatment courses. The method is based on "representative shapes" of the rectum, that is, shapes that are probable and also particularly favourable or unfavourable in terms of dose. The advantage is that these shapes can be used as a visual aid for the oncologist or dose planner, and that the method can be implemented using existing features of treatment planning systems. Overall, this thesis provides novel solutions to the central challenge of organ motion mitigation in RT. The presented models are the first to simultaneously exploit population and patient specific organ motion and addressing both systematic and random errors.Doktorgradsavhandlin

A quantitative and qualitative analysis of Positron Emission Tomography (PET) in Yttrium-90 radioembolization; investigating the utility of PET dosimetry in identifying sites of necrosis and viable tumor

Purpose: PET imaging is becoming more common for verifying the location of 90Y microspheres during liver cancer treatment. The work aims to predict which patients will likely to have remaining viable tumors based on the 90Y PET image taken right after the radioembolization. Methods: 10 hepatocellular carcinoma (HCC) patients treated by radioembolization with 90Y glass microspheres were included in this study. Post-treatment PET was coregistered with the follow-up image to investigate the correlation between the isodose contours based on the post-treatment PET image and the necrosis and viable tumor on the follow-up image. To evaluate the similarity quantitatively, isodose contours derived from 90Y PET and necrosis area on the follow-up image were compared using the Dice similarity coefficient. In addition to the quantitative assessment, a qualitative assessment of a 1–5-point scale was utilized to rate the correlation of underdose regions on the post-treatment PET and the viable tumor on the follow-up. The study thereby provided insights into the interpretation and analysis of post-radioembolization imaging in HCC patients. Results: The findings in this retrospective study with 10 patients included for quantitative assessment suggest an isodose range of 250 Gy to 300 Gy yields the best match for the necrosis site. Also, the qualitative assessment of these 10 patients shows a median agreement of 4 on a 1–5-point scale. Conclusion: 90Y PET/CT evaluation and dosimetry add clinical benefit to patient treatments by locating untreated tumors and potential sites of recurrence