Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy.

PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single "optimal" plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n = 12) and cervix brachytherapy (n = 36). We demonstrate the possible differences in dose distribution for optimized plans with equal dose-volume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution.

Radiation-Induced Retinopathy and Optic Neuropathy after Radiation Therapy for Brain, Head, and Neck Tumors: A Systematic Review.

BACKGROUND: Patients with brain, head, and neck tumors experience a decline in their quality of life due to radiation retinopathy and optic neuropathy. Little is known about the dose-response relationship and patient characteristics. We aimed to systematically review the prevalence of radiation retinopathy and optic neuropathy. METHOD: The primary outcome was the pooled prevalence of radiation retinopathy and optic neuropathy. The secondary outcome included the effect of the total radiation dose prescribed for the tumor according to the patient's characteristics. Furthermore, we aimed to evaluate the radiation dose parameters for organs at risk of radiation retinopathy and optic neuropathy. RESULTS: The pooled prevalence was 3.8%. No retinopathy was reported for the tumor's prescribed dose of <50 Gy. Optic neuropathy was more prevalent for a prescribed dose of >50 Gy than <50 Gy. We observed a higher prevalence rate for retinopathy (6.0%) than optic neuropathy (2.0%). Insufficient data on the dose for organs at risk were reported. CONCLUSION: The prevalence of radiation retinopathy was higher compared to optic neuropathy. This review emphasizes the need for future studies considering retinopathy and optic neuropathy as primary objective parameters.

Automatic landmark correspondence detection in medical images with an application to deformable image registration.

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of computed tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated ( p = 0 e 0 ) as well as clinical deformations ( p = 0.030 ). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to magnetic resonance imaging scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.

Towards artificial intelligence-based automated treatment planning in clinical practice: A prospective study of the first clinical experiences in high-dose-rate prostate brachytherapy.

PURPOSE: This prospective study evaluates our first clinical experiences with the novel ``BRachytherapy via artificial Intelligent GOMEA-Heuristic based Treatment planning'' (BRIGHT) applied to high-dose-rate prostate brachytherapy. METHODS AND MATERIALS: Between March 2020 and October 2021, 14 prostate cancer patients were treated in our center with a 15Gy HDR-brachytherapy boost. BRIGHT was used for bi-objective treatment plan optimization and selection of the most desirable plans from a coverage-sparing trade-off curve. Selected BRIGHT plans were imported into the commercial treatment planning system Oncentra Brachy . In Oncentra Brachy a dose distribution comparison was performed for clinical plan choice, followed by manual fine-tuning of the preferred BRIGHT plan when deemed necessary. The reasons for plan selection, clinical plan choice, and fine-tuning, as well as process speed were monitored. For each patient, the dose-volume parameters of the (fine-tuned) clinical plan were evaluated. RESULTS: In all patients, BRIGHT provided solutions satisfying all protocol values for coverage and sparing. In four patients not all dose-volume criteria of the clinical plan were satisfied after manual fine-tuning. Detailed information on tumour coverage, dose-distribution, dwell time pattern, and insight provided by the patient-specific trade-off curve, were used for clinical plan choice. Median time spent on treatment planning was 42 min, consisting of 16 min plan optimization and selection, and 26 min undesirable process steps. CONCLUSIONS: BRIGHT is implemented in our clinic and provides automated prostate high-dose-rate brachytherapy planning with trade-off based plan selection. Based on our experience, additional optimization aims need to be implemented to further improve direct clinical applicability of treatment plans and process efficiency.

Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans.

Purpose: Our purpose was to validate and compare the performance of 4 organ dose reconstruction approaches for historical radiation treatment planning based on 2-dimensional radiographs. Methods and Materials: We considered 10 patients with Wilms tumor with planning computed tomography images for whom we developed typical historic Wilms tumor radiation treatment plans, using anteroposterior and posteroanterior parallel-opposed 6 MV flank fields, normalized to 14.4 Gy. Two plans were created for each patient, with and without corner blocking. Regions of interest (lungs, heart, nipples, liver, spleen, contralateral kidney, and spinal cord) were delineated, and dose-volume metrics including organ mean and minimum dose (Dmean and Dmin) were computed as the reference baseline for comparison. Dosimetry for the 20 plans was then independently reconstructed using 4 different approaches. Three approaches involved surrogate anatomy, among which 2 used demographic-matching criteria for phantom selection/building, and 1 used machine learning. The fourth approach was also machine learning-based, but used no surrogate anatomies. Absolute differences in organ dose-volume metrics between the reconstructed and the reference values were calculated. Results: For Dmean and Dmin (average and minimum point dose) all 4 dose reconstruction approaches performed within 10% of the prescribed dose (≤1.4 Gy). The machine learning-based approaches showed a slight advantage for several of the considered regions of interest. For Dmax (maximum point dose), the absolute differences were much higher, that is, exceeding 14% (2 Gy), with the poorest agreement observed for near-beam and out-of-beam organs for all approaches. Conclusions: The studied approaches give comparable dose reconstruction results, and the choice of approach for cohort dosimetry for late effects studies should still be largely driven by the available resources (data, time, expertise, and funding).

Interactive Education on Sleep Hygiene with a Social Robot at a Pediatric Oncology Outpatient Clinic: Feasibility, Experiences, and Preliminary Effectiveness.

OBJECTIVES: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness. METHODS: Researchers approached children (8 to 12 years old) who were receiving anticancer treatment and who were visiting the outpatient clinic with their parents during the two-week study period. The researchers completed observation forms regarding feasibility, and parents completed the Children's Sleep Hygiene Scale before and two weeks after the educational regimen. The experiences of children and parents were evaluated in semi-structured interviews. We analyzed open answers by labeling each answer with a topic reflecting the content and collapsed these topics into categories. We used descriptive statistics to describe the feasibility and experiences, and a dependent-samples t-test to evaluate the preliminary effectiveness. RESULTS: Twenty-eight families participated (58% response rate) and all interactions with the robot were completed. The children and parents reported that they learned something new (75% and 50%, respectively), that they wanted to learn from the robot more often (83% and 75%, respectively), and that they applied the sleeping tips from the robot afterwards at home (54%). Regarding the preliminary effectiveness, children showed a statistically significant improvement in their sleep hygiene (p = 0.047, d = 0.39). CONCLUSIONS: Providing an educational regimen on sleep hygiene in a novel, interactive way by using a social robot at the outpatient clinic seemed feasible, and the children and parents mostly exhibited positive reactions. We found preliminary evidence that the sleep hygiene of children with cancer improved.

Therapeutic applications of radioactive sources: from image-guided brachytherapy to radio-guided surgical resection.

It is well known nowadays that radioactivity can destroy the living cells it interacts with. It is therefore unsurprising that radioactive sources, such as iodine-125, were historically developed for treatment purposes within radiation oncology with the goal of damaging malignant cells. However, since then, new techniques have been invented that make creative use of the same radioactivity properties of these sources for medical applications. Here, we review two distinct kinds of therapeutic uses of radioactive sources with applications to prostate, cervical, and breast cancer: brachytherapy and radioactive seed localization. In brachytherapy (BT), the radioactive sources are used for internal radiation treatment. Current approaches make use of real-time image guidance, for instance by means of magnetic resonance imaging, ultrasound, computed tomography, and sometimes positron emission tomography, depending on clinical availability and cancer type. Such image-guided BT for prostate and cervical cancer presents a promising alternative and/or addition to external beam radiation treatments or surgical resections. Radioactive sources can also be used for radio-guided tumor localization during surgery, for which the example of iodine-125 seed use in breast cancer is given. Radioactive seed localization (RSL) is increasingly popular as an alternative tumor localization technique during breast cancer surgery. Advantages of applying RSL include added flexibility in the clinical scheduling logistics, an increase in tumor localization accuracy, and higher patient satisfaction; safety measures do however have to be employed. We exemply the implementation of RSL in a clinic through our experiences at the Netherlands Cancer Institute.

Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty.

PURPOSE: Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction. METHODS: Studied reconstruction settings were: interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cost), and in the robust model (8 organ reconstructions, the difference determines the benefit). RESULTS: Robust optimization increased the run time from 3 to 6 min. The median cost for robust optimization as observed in the original model was -0.25% in the dose-volume indices with a range of [-0.01%, -1.03%]. The median benefit of robust optimization as observed in the robust model was 0.93% with a range of [0.19%, 4.16%]. For 4 patients, selected plans that appeared good when optimized in the original model, violated the clinical protocol with more than 1% when considering different settings. This was not the case for robustly optimized plans. CONCLUSIONS: Plans of high quality, irrespective of 3D organ reconstruction settings, can be obtained using our robust optimization approach. With its limited effect on total runtime, our approach therefore offers a way to account for dosimetry uncertainties that result from choices in organ reconstruction settings that is viable in clinical practice.

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations.

It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.

Bi-objective optimization of catheter positions for high-dose-rate prostate brachytherapy.

PURPOSE: Bi-objective simultaneous optimization of catheter positions and dwell times for high-dose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising results. However, optimization with the state-of-the-art evolutionary algorithm MO-RV-GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm. METHODS: Bi-objective simultaneous optimization was performed with a GPU-parallelized version of MO-RV-GOMEA. This optimization of catheter positions and dwell times was retrospectively applied to the data of 26 patients previously treated with HDR prostate brachytherapy for 8-16 catheters (steps of 2). Optimization of catheter positions using CVT was performed in seconds, after which optimization of only the dwell times using MO-RV-GOMEA was performed in 1 min. RESULTS: Simultaneous optimization of catheter positions and dwell times using MO-RV-GOMEA was performed in 5 min. For 16 down to 8 catheters (steps of 2), MO-RV-GOMEA found plans satisfying the planning-aims for 20, 20, 18, 14, and 11 out of the 26 patients, respectively. CVT achieved this for 19, 17, 13, 9, and 2 patients, respectively. The P-value for the difference between MO-RV-GOMEA and CVT was 0.023 for 16 catheters, 0.005 for 14 catheters, and <0.001 for 12, 10, and 8 catheters. CONCLUSIONS: With bi-objective simultaneous optimization on a GPU, high-quality catheter positions can now be obtained within 5 min, which is clinically acceptable, but slower than CVT. For 16 catheters, the difference between MO-RV-GOMEA and CVT is clinically irrelevant. For 14 catheters and less, MO-RV-GOMEA outperforms CVT in finding plans satisfying all planning-aims.

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction.

Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼ 8 mm for liver LR, IS, and spleen AP, IS; ∼ 5 mm for liver AP and spleen LR; ∼ 80 % for abdomen sDSC; and ∼ 60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially ( + 5 - mm error for spleen IS, - 10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients.

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average < 5 mm difference in plan isocenter/borders, and < 0.9 Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.

Microscopic tumor spread beyond (echo)endoscopically determined tumor borders in esophageal cancer.

OBJECTIVE: The microscopic tumor spread (MS) beyond the macroscopic tumor borders of esophageal tumors is crucial for determining the clinical target volume (CTV) in radiotherapy. The question arises whether current voluminous CTV margins of 3-5 cm around the macroscopic gross tumor volume (GTV) to account for MS are still accurate when fiducial markers are used for GTV determination. We aimed to pathologically validate the use of fiducial markers placed on the (echo)endoscopically determined tumor border (EDTB) as a surrogate for macroscopic tumor borders and to analyse the MS beyond EDTBs. METHODS: Thirty-three consecutive esophageal cancer patients treated with neo-adjuvant chemoradiotherapy after (echo)endoscopic fiducial marker implantation at cranial and caudal EDTB were included in this study. Fiducial marker positions were detected in the surgical specimens under CT guidance and demarcated with beads, and subsequently analysed for macroscopic tumor spread and MS beyond the demarcations. A logistic regression analysis was performed to determine predicting factors for MS beyond EDTB. RESULTS: A total of 60 EDTBs were examined in 32 patients. In 50% of patients no or only partial regression of tumor in response to therapy (≥Mandard 3) or higher was seen (i.e., residual tumor group) and included for MS analysis. None had macroscopic tumor spread beyond EDTBs. In the residual tumor group, only 20 and 21% of the cranial and caudal EDTBs were crossed with a maximum of 9 mm and 16 mm MS, respectively. This MS was corrected for each individual determined contraction rate (mean: 93%). Presence of MS beyond EDTB was significantly associated with initial tumor length (p = 0.028). CONCLUSION: Our results validate the use of fiducial markers on EDTB as a surrogate for macroscopic tumor and indicate that CTV margins around the GTV to compensate for MS along the esophageal wall can be limited to 1-1.5 cm, when the GTV is determined with fiducial markers.

A Novel Liquid Fiducial Marker in Esophageal Cancer Image Guided Radiation Therapy: Technical Feasibility and Visibility on Imaging.

PURPOSE: To assess the technical feasibility of injection, visibility on imaging modalities, and positional stability of a novel liquid fiducial marker (ie, BioXmark) for radiation therapy in patients with esophageal cancer. METHODS: First, the visibility on imaging of different volumes of the liquid marker was analyzed ex vivo in porcine tissue (ie, on computed tomography [CT], cone beam CT (CBCT), and magnetic resonance imaging [MRI]). Next, for the in vivo part, the liquid fiducial markers were injected under endoscopic (ultrasound) guidance in 10 patients with curable esophageal cancer. The technical feasibility of the injection procedure and the clinical performance (ie, visibility and positional stability on imaging) were evaluated. Planning CT, daily CBCT, and serial MRI images (before, during, and after chemoradiation therapy in a subset of 3 patients) were acquired. RESULTS: Ex vivo, the optimal volume for good visibility without artifacts was 0.1 mL per injected marker. In vivo, a total of 28 markers were injected in 10 patients (range, 0.025-0.1 mL). No adverse effects were identified. The first 2 cases (4 markers) were considered as learning cases. A total of 19 of 24 of the liquid markers (79%) were visible on CT, 3 of 4 (75%) on MRI, and 19 of 24 (79%) on the first CBCT. All markers with an injected volume of >0.05 mL were visible on the different imaging modalities. Positional stability analysis on CBCT identified no time trend during the radiation therapy course. No artifacts could be detected for liquid marker volumes of 0.05 and 0.025 mL in CT or CBCT. CONCLUSIONS: Injection of a liquid fiducial marker for esophageal cancer radiation therapy is technically feasible with no adverse events identified. Volumes of >0.05 mL have an appropriate visibility on CT, CBCT, and MRI, with an excellent positional stability. Liquid fiducial markers are therefore promising for use in image guided radiation therapy.

GPU-accelerated bi-objective treatment planning for prostate high-dose-rate brachytherapy.

PURPOSE: The purpose of this study is to improve upon a recently introduced bi-objective treatment planning method for prostate high-dose-rate (HDR) brachytherapy (BT), both in terms of resulting plan quality and runtime requirements, to the extent that its execution time is clinically acceptable. METHODS: Bi-objective treatment planning is done using a state-of-the-art multiobjective evolutionary algorithm, which produces a large number of potential treatment plans with different trade-offs between coverage of the target volumes and sparing organs at risk. A graphics processing unit (GPU) is used for large-scale parallelization of dose calculations and the calculation of the dose-volume (DV) indices of potential treatment plans. Moreover, the objectives of the previously used bi-objective optimization model are modified to produce better results. RESULTS: We applied the GPU-accelerated bi-objective treatment planning method to a set of 18 patients, resulting in a set containing a few hundred potential treatment plans with different trade-offs for each of these patients. Due to accelerations introduced in this article, results previously achieved after 1 hour are now achieved within 30 seconds of optimization. We found plans satisfying the clinical protocol for 15 of 18 patients, whereas this was the case for only 4 of 18 clinical plans. Higher quality treatment plans are obtained when the accuracy of DV index calculation is increased using more dose calculation points, requiring still no more than 3 minutes of optimization for 100 000 points. CONCLUSIONS: Large sets of high-quality treatment plans that trade-off coverage and sparing are now achievable within 30 seconds, due to the GPU-acceleration of a previously introduced bi-objective treatment planning method for prostate HDR brachytherapy. Higher quality plans can be achieved when optimizing for 3 minutes, which we still consider to be clinically acceptable. This allows for more insightful treatment plan selection in a clinical setting.

Optical coherence tomography to detect acute esophageal radiation-induced damage in mice: A validation study.

Radiation therapy for patients with non-small-cell lung cancer is hampered by acute radiation-induced toxicity in the esophagus. This study aims to validate that optical coherence tomography (OCT), a minimally invasive imaging technique with high resolution (~10 µm), is able to visualize and monitor acute radiation-induced esophageal damage (ARIED) in mice. We compare our findings with histopathology as the gold standard. Irradiated mice receive a single dose of 40 Gy at proximal and distal spots of the esophagus of 10.0 mm in diameter. We scan mice using OCT at two, three, and seven days post-irradiation. In OCT analysis, we define ARIED as a presence of distorted esophageal layering, change in backscattering signal properties, or change in the esophageal wall thickness. The average esophageal wall thickness is 0.53 mm larger on OCT when ARIED is present based on histopathology. The overall sensitivity and specificity of OCT to detect ARIED compared to histopathology are 94% and 47%, respectively. However, the overall sensitivity of OCT to assess ARIED is 100% seven days post-irradiation. We validate the capability of OCT to detect ARIED induced by high doses in mice. Nevertheless, clinical studies are required to assess the potential role of OCT to visualize ARIED in humans.

How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms' tumor radiation treatment plans when using a surrogate patient's CT scan?

In retrospective radiation treatment (RT) dosimetry, a surrogate anatomy is often used for patients without 3D CT. To gain insight in what the crucial aspects in a surrogate anatomy are to enable accurate dose reconstruction, we investigated the relation of patient characteristics and internal anatomical features with deviations in reconstructed organ dose using surrogate patient's CT scans. Abdominal CT scans of 35 childhood cancer patients (age: 2.1-5.6 yr; 17 boys, 18 girls) undergoing RT during 2004-2016 were included. Based on whether an intact right or left kidney is present in the CT scan, two groups were formed each containing 24 patients. From each group, four CTs associated with Wilms' tumor RT plans with an anterior-posterior-posterior-anterior field setup were selected as references. For each reference, a 2D digitally reconstructed radiograph was computed from the reference CT to simulate a 2D radiographic image and dose reconstruction was performed on the other CTs in the respective group. Deviations in organ mean dose (DEmean) of the reconstructions versus the references were calculated, as were deviations in patient characteristics (i.e. age, height, weight) and in anatomical features including organ volume, location (in 3D), and spatial overlaps. Per reference, the Pearson's correlation coefficient between deviations in DEmean and patient characteristics/features were studied. Deviation in organ locations and DEmean for the liver, spleen, and right kidney were moderately correlated (R2 > 0.5) for 8/8, 5/8, and 3/4 reference plans, respectively. Deviations in organ volume or spatial overlap and DEmean for the right and left kidney were weakly correlated (0.3 < R2 < 0.5) in 4/4 and 1/4 reference plans. No correlations (R2 < 0.3) were found between deviations in age or height and DEmean. Therefore, the performance of organ dose reconstruction using surrogate patients' CT scans is primarily related to deviation in organ location, followed by volume and spatial overlap. Further, results were plan dependent.

Sensitivity of dose-volume indices to computation settings in high-dose-rate prostate brachytherapy treatment plan evaluation.

PURPOSE: To investigate the variation in computed dose-volume (DV) indices for high-dose-rate (HDR) prostate brachytherapy that can result from typical differences in computation settings in treatment planning systems (TPSs). METHODS: Five factors were taken into account: number of dose-calculation points, radioactive source description, interpolation between delineated contours, intersections between delineated organ contours, and organ shape at the top and bottom contour using either full or partial slice thickness. Using in-house developed software, the DV indices of the treatment plans of 26 patients were calculated with different settings, and compared to a baseline setting that closely followed the default settings of the TPS used in our medical center. Studied organs were prostate and seminal vesicles, denoted as targets, and bladder, rectum, and urethra, denoted as organs at risk (OARs), which were delineated on MRI scans with a 3.3 mm slice thickness. RESULTS: When sampling a fixed number of points in each organ, in order to achieve a width of the 95% confidence interval over all patients of the DV indices of 1% or less, only 32,000 points had to be sampled per target, but 256,000 points had to be sampled per OAR. For the remaining factors, DV indices changed up to 0.4% for rectum, 1.3% for urethra, and 2.6% for prostate. DV indices of the bladder changed especially if the high-dose-region was (partly) located at the most caudal contour, up to 8.5%, and DV indices of the vesicles changed especially if there were few delineated contours, up to 9.8%, both due to the use of full slice thickness for the top and bottom contour. CONCLUSIONS: The values of DV indices used in prostate HDR brachytherapy treatment planning are influenced by the computation settings in a TPS, especially at the most caudal part of the bladder, as well as in the seminal vesicles.

Reduced inter-observer and intra-observer delineation variation in esophageal cancer radiotherapy by use of fiducial markers.

Objective: Delineation variation of esophageal tumors remains a large source of geometric uncertainty. In the present study, we investigated the inter- and intra-observer variation in esophageal gross tumor volume (GTV) delineation and the impact of endoscopically implanted fiducial markers on these variations. Material/Methods: Ten esophageal cancer patients with at least two markers endoscopically implanted at the cranial and caudal tumor borders and visible on the planning computed tomography (pCT) were included in this study. Five dedicated gastrointestinal radiation oncologists independently delineated GTVs on the pCT without markers and with markers. The GTV was first delineated on pCTs where markers were digitally removed and next on the original pCT with markers. Both delineation series were executed twice to determine intra-observer variation. For both the inter- and intra-observer analyses, the generalized conformity index (CIgen), and the standard deviation (SD) of the distances between delineated surfaces (i.e., overall, longitudinal, and radial SDs) were calculated. Linear mixed-effect models were used to compare the without and with markers series (α = 0.05). Results: Both the inter- and intra-observer CIgen were significantly larger in the series with markers than in the series without markers (p < .001). For the series without markers vs. with markers, the inter-observer overall SD, longitudinal SD, and radial SD was 0.63 cm vs. 0.22 cm, 1.44 cm vs. 0.42 cm, and 0.26 cm vs. 0.18 cm, respectively (p < .05); moreover, the intra-observer overall SD, longitudinal SD, and radial SD was 0.45 cm vs. 0.26 cm, 1.10 cm vs. 0.41 cm, and 0.22 cm vs. 0.15 cm, respectively (p < .05). Conclusion: The presence of markers at the cranial and caudal tumor borders significantly reduced both inter- and intra-observer GTV delineation variation, especially in the longitudinal direction. Our results endorse the use of markers in GTV delineation for esophageal cancer patients.

Evaluation of bi-objective treatment planning for high-dose-rate prostate brachytherapy-A retrospective observer study.

PURPOSE: Bi-objective treatment planning for high-dose-rate prostate brachytherapy is a novel treatment planning method with two separate objectives that represent target coverage and organ-at-risk sparing. In this study, we investigated the feasibility and plan quality of this method by means of a retrospective observer study. METHODS AND MATERIALS: Current planning sessions were recorded to configure a bi-objective optimization model and to assess its applicability to our clinical practice. Optimization software, GOMEA, was then used to automatically generate a large set of plans with different trade-offs in the two objectives for each of 18 patients treated with high-dose-rate prostate brachytherapy. From this set, five plans per patient were selected for comparison to the clinical plan in terms of satisfaction of planning criteria and in a retrospective observer study. Three brachytherapists were asked to evaluate the blinded plans and select the preferred one. RESULTS: Recordings demonstrated applicability of the bi-objective optimization model to our clinical practice. For 14/18 patients, GOMEA plans satisfied all planning criteria, compared with 4/18 clinical plans. In the observer study, in 53/54 cases, a GOMEA plan was preferred over the clinical plan. When asked for consensus among observers, this ratio was 17/18 patients. Observers highly appreciated the insight gained from comparing multiple plans with different trade-offs simultaneously. CONCLUSIONS: The bi-objective optimization model adapted well to our clinical practice. GOMEA plans were considered equal or superior to the clinical plans. In addition, presenting multiple high-quality plans provided novel insight into patient-specific trade-offs.