Imaging Assessment of Radiation Therapy-Related Normal Tissue Injury in Children: A PENTEC Visionary Statement.

The Pediatric Normal Tissue Effects in the Clinic (PENTEC) consortium has made significant contributions to understanding and mitigating the adverse effects of childhood cancer therapy. This review addresses the role of diagnostic imaging in detecting, screening, and comprehending radiation therapy-related late effects in children, drawing insights from individual organ-specific PENTEC reports. We further explore how the development of imaging biomarkers for key organ systems, alongside technical advancements and translational imaging approaches, may enhance the systematic application of imaging evaluations in childhood cancer survivors. Moreover, the review critically examines knowledge gaps and identifies technical and practical limitations of existing imaging modalities in the pediatric population. Addressing these challenges may expand access to, minimize the risk of, and optimize the real-world application of, new imaging techniques. The PENTEC team envisions this document as a roadmap for the future development of imaging strategies in childhood cancer survivors, with the overarching goal of improving long-term health outcomes and quality of life for this vulnerable population.

Knowledge-Based Assessment of Focal Dose Escalation Treatment Plans in Prostate Cancer.

PURPOSE: In a randomized focal dose escalation radiation therapy trial for prostate cancer (FLAME), up to 95 Gy was prescribed to the tumor in the dose-escalated arm, with 77 Gy to the entire prostate in both arms. As dose constraints to organs at risk had priority over dose escalation and suboptimal planning could occur, we investigated how well the dose to the tumor was boosted. We developed an anatomy-based prediction model to identify plans with suboptimal tumor dose and performed replanning to validate our model. METHODS AND MATERIALS: We derived dose-volume parameters from planned dose distributions of 539 FLAME trial patients in 4 institutions and compared them between both arms. In the dose-escalated arm, we determined overlap volume histograms and derived features representing patient anatomy. We predicted tumor D98% with a linear regression on anatomic features and performed replanning on 21 plans. RESULTS: In the dose-escalated arm, the median tumor D50% and D98% were 93.0 and 84.7 Gy, and 99% of the tumors had a dose escalation greater than 82.4 Gy (107% of 77 Gy). In both arms organs at risk constraints were met. Five out of 73 anatomic features were found to be predictive for tumor D98%. Median predicted tumor D98% was 4.4 Gy higher than planned D98%. Upon replanning, median tumor D98% increased by 3.0 Gy. A strong correlation between predicted increase in D98% and realized increase upon replanning was found (ρ = 0.86). CONCLUSIONS: Focal dose escalation in prostate cancer was feasible with a dose escalation to 99% of the tumors. Replanning resulted in an increased tumor dose that correlated well with the prediction model. The model was able to identify tumors on which a higher boost dose could be planned. The model has potential as a quality assessment tool in focal dose escalated treatment plans.

RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning.

PURPOSE: Detailed and accurate absorbed dose calculations from radiation interactions with the human body can be obtained with the Monte Carlo (MC) method. However, the MC method can be slow for use in the time-sensitive clinical workflow. The aim of this study was to provide a solution to the accuracy-time trade-off for 192Ir-based high-dose-rate brachytherapy by using deep learning. METHODS AND MATERIALS: RapidBrachyDL, a 3-dimensional deep convolutional neural network (CNN) model, is proposed to predict dose distributions calculated with the MC method given a patient's computed tomography images, contours of clinical target volume (CTV) and organs at risk, and treatment plan. Sixty-one patients with prostate cancer and 10 patients with cervical cancer were included in this study, with data from 47 patients with prostate cancer being used to train the model. RESULTS: Compared with ground truth MC simulations, the predicted dose distributions by RapidBrachyDL showed a consistent shape in the dose-volume histograms (DVHs); comparable DVH dosimetric indices including 0.73% difference for prostate CTV D90, 1.1% for rectum D2cc, 1.45% for urethra D0.1cc, and 1.05% for bladder D2cc; and substantially smaller prediction time, acceleration by a factor of 300. RapidBrachyDL also demonstrated good generalization to cervical data with 1.73%, 2.46%, 1.68%, and 1.74% difference for CTV D90, rectum D2cc, sigmoid D2cc, and bladder D2cc, respectively, which was unseen during the training. CONCLUSION: Deep CNN-based dose estimation is a promising method for patient-specific brachytherapy dosimetry. Desired radiation quantities can be obtained with accuracies arbitrarily close to those of the source MC algorithm, but with much faster computation times. The idea behind deep CNN-based dose estimation can be safely extended to other radiation sources and tumor sites by following a similar training process.

Imaging issues specific to hadrontherapy (proton, carbon, helium therapy and other charged particles) for radiotherapy planning, setup, dose monitoring and tissue response assessment.

Imaging is critical to each step of precision radiation therapy, i.e. planning, setup, delivery and assessment of response. Hadrontherapy can be considered to deliver more precise dose distribution that may better spare normal tissues from intermediate low doses of radiation. In addition, hadrontherapy using high linear energy transfer ions may also be used for dose escalation on biological target volumes defined by functional imaging. However, the physical characteristics of hadrontherapy also make it more demanding in terms of imaging accuracy and image-based dose calculation. Some of the developments needed in imaging are specific to hadrontherapy. The current review addresses current status of imaging in proton therapy and the drawbacks of photon-based imaging for hadrons. It also addresses requirements in hadrontherapy planning with respect to multimodal imaging for proper target and organ at risk definition as well as to target putative radioresistant areas such as hypoxic ones, and with respect to dose calculation using dual energy CT, MR-proton therapy, proton radiography. Imaging modalities, such as those used in photon-based radiotherapy (intensity modulated and stereotactic radiotherapy), are somewhat already implemented or should be reaching "routine" hadrontherapy (at least proton therapy) practice in planning, repositioning and response evaluation optimizable within the next five years. Online monitoring imaging by PET, as currently developed for hadrontherapy, is already available. Its spatiotemporal limits restrict its use but similar to prompt gamma detection, represents an area of active research for the next 5 to 10 years. Because of the more demanding and specific dose deposit characteristics, developments image-guided hadrontherapy, such as specific proton imaging using tomography or ionoacoustics, as well as delivery with MR-proton therapy, may take another 10 years to reach the clinics in specific applications. Other aspects are briefly described such as range monitoring. Finally, the potential of imaging normal tissue changes and challenges to assess tumour response are discussed.

Dosimetric comparison of artificial walls of bladder and rectum with real walls in common prostate IMRT techniques: Patient and Monte Carlo study.

BACKGROUND: Rectum and bladder are hallow structures and considered as critical organs in prostate cancer intensity modulated radiotherapy (IMRT). Therefore, dose received by these organ walls must be considered for prediction of radiobiological effects. Contouring the real organ walls is quite difficult and time consuming in CT/MRI images, so the easy contouring artificial walls with uniform thickness could be appropriated alternatives. OBJECTIVE: To compare reconstructed artificial walls with real walls of bladder and rectum in common prostate IMRT techniques based on dose volume-histograms (DVHs) derived from artificial and real walls. METHODS: Artificial walls were reconstructed with 2-10 mm and 2-8 mm thicknesses for bladder and rectum, respectively. Four common IMRT techniques were applied to each patient. Spearman correlation was used to find the relation between the DVHs of true walls with artificial walls and whole organs. Monte Carlo (MC) simulations of the IMRT techniques and dosimetric comparison were also performed on a standard patient data. RESULTS: The 2 mm thickness artificial walls showed the minimum differences with the true bladder and rectum walls based on absolute evaluations (the maximum difference < 10cc and standard deviation < 15cc). However, relative evaluations showed that all the artificial walls had high correlations with real walls for selecting dose volume parameters. There was also good agreement between the treatment planning system and MC simulations results. CONCLUSION: The DVH of whole organs was not a good surrogate of the true wall. The 2 mm artificial walls can be regarded as good alternatives for both of rectum and bladder. However, in relative dose evaluations all studied artificial walls were appropriate.

NCINM: organ dose calculator for patients undergoing nuclear medicine procedures.

Nuclear medicine is the second largest source of medical radiation exposure to the general population after computed tomography imaging. Informed decisions regarding the use of nuclear medicine procedures require a better understanding of the magnitude of radiation dose and associated health risks. However, existing model-based organ dose estimation tools rely on simplified human anatomy models or commercial programs. Therefore, we developed a publicly-available dose calculation tool based on more sophisticated human anatomy models. We calculated a comprehensive library of photon and electron specific absorbed fractions (SAF) for multiple combinations of source and target regions within a series of pediatric and adult computational human phantoms matching the International Commission on Radiological Protection (ICRP)'s reference data, combined with a Monte Carlo radiation transport code. Then, we derived a library of S values from these SAFs and the nuclear decay data from ICRP Publication 107. Finally, we created a graphical user interface, named National Cancer Institute Dosimetry System for Nuclear Medicine (NCINM), to facilitate the dosimetry process. Approximately 13 million S values were derived from 2 million SAFs computed in this work. Comprehensive comparisons were conducted at different steps of the dosimetry chain with data available in software OLINDA/EXM 1.0 and IDAC 2.1. For instance, median ratios of photon self-absorption SAFs available from OLINDA/EXM 1.0 and IDAC 2.1 to those calculated in this study were 1.3 (interquartile range = 1.1-1.6) and 1.0 (interquartile range = 0.98-1.0), respectively. SAF differences between NCINM and OLINDA/EXM 1.0 were explained by the large inter-phantom anatomical variability. Our results illustrate the importance of realistic human anatomy models for use in dosimetry software. More phantoms and radionuclides, as well as a biokinetic module, will soon be added. Applications of the NCINM program include computation of absorbed doses for use in radiation epidemiologic studies and patient dose monitoring in nuclear medicine.

Treatment planning for proton therapy: what is needed in the next 10 years?

Treatment planning is the process where the prescription of the radiation oncologist is translated into a deliverable treatment. With the complexity of contemporary radiotherapy, treatment planning cannot be performed without a computerized treatment planning system. Proton therapy (PT) enables highly conformal treatment plans with a minimum of dose to tissues outside the target volume, but to obtain the most optimal plan for the treatment, there are a multitude of parameters that need to be addressed. In this review areas of ongoing improvements and research in the field of PT treatment planning are identified and discussed. The main focus is on issues of immediate clinical and practical relevance to the PT community highlighting the needs for the near future but also in a longer perspective. We anticipate that the manual tasks performed by treatment planners in the future will involve a high degree of computational thinking, as many issues can be solved much better by e.g. scripting. More accurate and faster dose calculation algorithms are needed, automation for contouring and planning is required and practical tools to handle the variable biological efficiency in PT is urgently demanded just to mention a few of the expected improvements over the coming 10 years.

Dosimetric Considerations for Ytterbium-169, Selenium-75, and Iridium-192 Radioisotopes in High-Dose-Rate Endorectal Brachytherapy.

PURPOSE: To investigate differences between prescribed and postimplant calculated dose in 192Ir high-dose-rate endorectal brachytherapy (HDR-EBT) by evaluating dose to clinical target volume (CTV) and organs at risk (OARs) calculated with a Monte Carlo-based dose calculation software, RapidBrachyMC. In addition, dose coverage, conformity, and homogeneity were compared among the radionuclides 192Ir, 75Se, and 169Yb for use in HDR-EBT. METHODS AND MATERIALS: Postimplant dosimetry was evaluated using 23 computed tomography (CT) images from patients treated with HDR-EBT using the 192Ir microSelectron v2 (Elekta AB, Stockholm, Sweden) source and the Intracavitary Mold Applicator Set (Elekta AB, Stockholm, Sweden), which is a flexible applicator capable of fitting a tungsten rod for OAR shielding. Four tissue segmentation schemes were evaluated: (1) TG-43 formalism, (2) materials and nominal densities assigned to contours of foreign objects, (3) materials and nominal densities assigned to contoured organs in addition to foreign objects, and (4) materials specified as in (3) but with voxel mass densities derived from CT Hounsfield units. Clinical plans optimized for 192Ir were used, with the results for 75Se and 169Yb normalized to the D90 of the 192Ir clinical plan. RESULTS: In comparison to segmentation scheme 4, TG-43-based dosimetry overestimates CTV D90 by 6% (P = .00003), rectum D50 by 24% (P = .00003), and pelvic bone D50 by 5% (P = .00003) for 192Ir. For 169Yb, CTV D90 is overestimated by 17% (P = .00003) and rectum D50 by 39% (P = .00003), and pelvic bone D50 is significantly underestimated by 27% (P = .007). Postimplant dosimetry calculations also showed that a 169Yb source would give 20% (P = .00003) lower rectum V60 and 17% (P = .00008) lower rectum D50. CONCLUSIONS: Ignoring high-Z materials in dose calculation contributes to inaccuracies that may lead to suboptimal dose optimization and disagreement between prescribed and calculated dose. This is especially important for low-energy radionuclides. Our results also show that with future magnetic resonance imaging-based treatment planning, loss of CT density data will only affect calculated dose in nonbone OARs by 2% or less and bone OARs by 13% or less across all sources if material composition and nominal mass densities are correctly assigned.

Sequential simulation computed tomography allows assessment of internal rectal movements during preoperative chemoradiotherapy in rectal cancer.

PURPOSES: The purpose of this study was to assess the internal rectal movement and to determine the factors related to extensive internal rectal movement using sequential simulation computed tomography (CT) images. MATERIALS AND METHODS: From 2010 to 2015, 96 patients receiving long-course preoperative chemoradiotherapy were included in our retrospective study. The initial simulation CT (Isim-CT) and follow-up simulation CT (Fsim-CT) for a boost were registered according to the isocenters and bony structure. The rectums on Isim-CT and Fsim-CT were compared on four different axial planes as follows: (1) lower pubis symphysis (AXVERYLOW), (2) upper pubis symphysis (AXLOW), (3) superior rectum (AXHIGH), and (4) middle of AXLOW and AXHIGH (AXMID). The involved rectum in the planning target volume was evaluated. The maximal radial distances (MRD), the necessary radius from the end of Isim-CT rectum to cover entire Fsim-CT rectum, and the common area rate (CAR) of the rectum (CAR, (Isim-CT∩Fsim-CT)/(Isim-CT)) were measured. Linear regression tests for the MRDs and logistic regression tests for the CARs were conducted. RESULTS: The mean ± standard deviation (mm) of MRDs and CAR <80% for AXVERYLOW, AXLOW, AXMID, and AXHIGH were 2.3 ± 2.5 and 8.9%, 3.0 ± 3.7 and 17.4%, 4.0 ± 5.2 and 27.1%, and 4.1 ± 5.2 and 25%, respectively. For MRDs and CARs, a higher axial level (AXVERYLOW/AXMID-HIGH, P = 0.018 and P = 0.034, respectively), larger bladder volume (P = 0.054 and P = 0.017, respectively), smaller bowel gas extent (small/marked, P = 0.014 and P = 0.001, respectively), and increased bowel gas change (decrease/increase, both P < 0.001) in rectum were associated with extensive internal rectal movement in multivariate analyses. CONCLUSIONS: As a result of following internal rectal movement through sequential simulation CT, the rectum above the pubis symphysis needs a larger margin, and bladder volume and bowel gas should be closely observed.

Normal Tissue Complication Probability Modeling of Pulmonary Toxicity After Stereotactic and Hypofractionated Radiation Therapy for Central Lung Tumors.

PURPOSE: To evaluate clinical pulmonary and radiographic bronchial toxicity after stereotactic ablative radiation therapy and hypofractionated radiation therapy for central lung tumors, and perform normal tissue complication probability modeling and multivariable analyses to identify predictors for toxicity. METHODS AND MATERIALS: A pooled analysis was performed of patients with a central lung tumor treated using ≤12 fractions at 2 centers between 2006 and 2015. Airways were manually contoured on planning computed tomography scans, and doses were recalculated to an equivalent dose of 2 Gy per fraction with an α/ß ratio of 3. Grade ≥3 (≥G3) clinical pulmonary toxicity was evaluated by 2 or more physicians. Radiographic toxicity was defined as a stenosis or an occlusion with or without atelectasis using follow-up computed tomography scans. Logistic regression analyses were used for statistical analyses. RESULTS: A total of 585 bronchial structures were studied in 195 patients who were mainly treated using 5 or 8 fractions (60%). Median patient survival was 27.9 months (95% confidence interval 22.3-33.6 months). Clinical ≥G3 toxicity was observed in 24 patients (12%) and radiographic bronchial toxicity in 55 patients (28%), both mainly manifesting ≤12 months after treatment. All analyzed dosimetric parameters correlated with clinical and lobar bronchial radiographic toxicity, with V130Gy,EQD having the highest odds ratio. Normal tissue complication probability modeling showed a volume dependency for the development of both clinical and radiographic toxicity. On multivariate analyses, significant predictors for ≥G3 toxicity were a planning target volume overlapping the trachea or main stem bronchus (P = .005), chronic obstructive pulmonary disease (P = .034), and the total V130Gy,EQD (P = .012). Radiographic bronchial toxicity did not significantly correlate with clinical toxicity (P = .663). CONCLUSIONS: We identified patient and dosimetric factors associated with clinical and radiographic toxicity after high-dose radiation therapy for central lung tumors. Additional data from prospective studies are needed to validate these findings.

Evaluation of Stopping-Power Prediction by Dual- and Single-Energy Computed Tomography in an Anthropomorphic Ground-Truth Phantom.

PURPOSE: To determine the accuracy of particle range prediction for proton and heavier ion radiation therapy based on dual-energy computed tomography (DECT) in a realistic inhomogeneous geometry and to compare it with the state-of-the-art clinical approach. METHODS AND MATERIALS: A 3-dimensional ground-truth map of stopping-power ratios (SPRs) was created for an anthropomorphic head phantom by assigning measured SPR values to segmented structures in a high-resolution CT scan. This reference map was validated independently comparing proton transmission measurements with Monte Carlo transport simulations. Two DECT-based methods for direct SPR prediction via the Bethe formula (DirectSPR) and 2 established approaches based on Hounsfield look-up tables (HLUTs) were chosen for evaluation. The SPR predictions from the 4 investigated methods were compared with the reference, using material-specific voxel statistics and 2-dimensional gamma analysis. Furthermore, range deviations were analyzed in an exemplary proton treatment plan. RESULTS: The established reference SPR map was successfully validated for the discrimination of SPR and range differences well below 0.3% and 1 mm, respectively, even in complex inhomogeneous settings. For the phantom materials of larger volume (mainly brain, soft tissue), the investigated methods were overall able to predict SPR within 1% median deviation. The DirectSPR methods generally performed better than the HLUT approaches. For smaller phantom parts (such as cortical bone, air cavities), all methods were affected by image smoothing, leading to considerable SPR under- or overestimation. This effect was superimposed on the general SPR prediction accuracy in the exemplary treatment plan. CONCLUSIONS: DirectSPR predictions proved to be more robust, with high accuracy in particular for larger volumes. In contrast, HLUT approaches exhibited a fortuitous component. The evaluation of accuracy in a realistic phantom with validated ground-truth SPR represents a crucial step toward possible clinical application of DECT-based SPR prediction methods.

Evaluating Intensity Modulated Proton Therapy Relative to Passive Scattering Proton Therapy for Increased Vertebral Column Sparing in Craniospinal Irradiation in Growing Pediatric Patients.

PURPOSE: At present, proton craniospinal irradiation (CSI) for growing children is delivered to the whole vertebral body (WVB) to avoid asymmetric growth. We aimed to demonstrate the feasibility and potential clinical benefit of delivering vertebral body sparing (VBS) versus WVB CSI with passively scattered (PS) and intensity modulated proton therapy (IMPT) in growing children treated for medulloblastoma. METHODS AND MATERIALS: Five plans were generated for medulloblastoma patients, who had been previously treated with CSI PS proton radiation therapy: (1) single posteroanterior (PA) PS field covering the WVB (PS-PA-WVB); (2) single PA PS field that included only the thecal sac in the target volume (PS-PA-VBS); (3) single PA IMPT field covering the WVB (IMPT-PA-WVB); (4) single PA IMPT field, target volume including thecal sac only (IMPT-PA-VBS); and (5) 2 posterior-oblique (-35°, +35°) IMPT fields, with the target volume including the thecal sac only (IMPT2F-VBS). For all cases, 23.4 Gy (relative biologic effectiveness [RBE]) was prescribed to 95% of the spinal canal. The dose, linear energy transfer, and variable-RBE-weighted dose distributions were calculated for all plans using the tool for particle simulation, version 2, Monte Carlo system. RESULTS: IMPT VBS techniques efficiently spared the anterior vertebral bodies (AVBs), even when accounting for potential higher variable RBE predicted by linear energy transfer distributions. Assuming an RBE of 1.1, the V10 Gy(RBE) decreased from 100% for the WVB techniques to 59.5% to 76.8% for the cervical, 29.9% to 34.6% for the thoracic, and 20.6% to 25.1% for the lumbar AVBs, and the V20 Gy(RBE) decreased from 99.0% to 17.8% to 20.0% for the cervical, 7.2% to 7.6% for the thoracic, and 4.0% to 4.6% for the lumbar AVBs when IMPT VBS techniques were applied. The corresponding percentages for the PS VBS technique were higher. CONCLUSIONS: Advanced proton techniques can sufficiently reduce the dose to the vertebral body and allow for vertebral column growth for children with central nervous system tumors requiring CSI. This was true even when considering variable RBE values. A clinical trial is planned for VBS to the thoracic and lumbosacral spine in growing children.

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image. RESULTS: The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm. CONCLUSION: We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

Large-scale Retrospective Monte Carlo Dosimetric Study for Permanent Implant Prostate Brachytherapy.

PURPOSE: To retrospectively compare water-based and full tissue model Monte Carlo dose calculations in a large cohort of patients undergoing 125I permanent implant prostate brachytherapy. METHODS AND MATERIALS: For 613 patients, EGSnrc BrachyDose dose calculations were performed in 2 virtual patient models: TG43sim (simulated American Association of Physicists in Medicine Task Group Report 43 conditions) and MCref (computed tomography-derived heterogeneous tissue model with interseed effects). A sensitivity analysis was performed in a patient subset (25 with and 25 without prostatic calcifications) to explore dose calculation dependence on organ-at-risk (OAR) and calcification tissue elemental compositions and modelling approach. RESULTS: In the target volume, the minimum radiation dose delivered to 90% of prostate (D90) (volume of prostate receiving at least 100% of prescription dose [V100]) was lower with MCref than with TG43sim by 5.9% ± 1.6% (2.6% ± 1.7%), on average. Patients with prostatic calcifications can have substantial underdosed volumes due to calcification shielding, lowering the D90 by ≤25%. In the urethra, the average D5 (D30) was lower with MCref than with TG43sim by 4.4% ± 1.8% (4.7% ± 1.9%). In the rectum (bladder), the minimum dose to the hottest 0.1 cm3 (D_0.1cm3) of the contoured organ was lower (higher) with MCref than with TG43sim by 5.2% ± 1.8% (1.3% ± 1.8%). Doses to the target and OARs can increase or decrease by several percentages, depending on the assumed tissue elemental composition. In patients with calcifications, differences between approaches to model calcifications can change the target and OAR dose metrics by upward of 10%. CONCLUSIONS: TG43sim typically overestimates the target and OAR doses by several percentages, on average, compared with MCref. The considerable variation in the relative TG43sim and MCref doses between patients, and the larger dose differences for patients with calcification, suggests that clinical adoption of Monte Carlo dose calculations for permanent implant prostate brachytherapy should be pursued. The substantial sensitivity of the Monte Carlo dose calculations to the patient modelling approach supports the adoption of a consensus modelling scheme, such as MCref described in the present study, to ensure consistency of practice.

Proton Treatment Techniques for Posterior Fossa Tumors: Consequences for Linear Energy Transfer and Dose-Volume Parameters for the Brainstem and Organs at Risk.

PURPOSE: In proton therapy of posterior fossa tumors, at least partial inclusion of the brainstem in the target is necessary because of its proximity to the tumor and required margins. Additionally, the preferred beam geometry results in directing the field distal edge toward this critical structure, raising concerns for brainstem toxicity. Some treatment techniques place the beam's distal edge within the brainstem (dose-sparing techniques), and others avoid elevated linear energy transfer (LET) of the proton field by placing the distal edge beyond it (LET-sparing techniques). Hybrid approaches are also being used. We examine the dosimetric efficacy of these techniques, accounting for LET-dependent and dose-dependent variable relative biologic effectiveness (RBE) distributions. METHODS: Six techniques were applied in ependymoma cases: (a) 3-field dose-sparing; (b) 3-field LET-sparing; (c) 2-field dose-sparing, wide angles; (d) 2-field LET-sparing, wide angles; (e) 2-field LET-sparing, steep angles; and (f) 2-field LET-sparing with feathered distal end. Monte Carlo calculated dose, LET, and RBE-weighted dose distributions were compared. RESULTS: Decreased LET values in the brainstem by LET-sparing techniques were accompanied by higher, not statistically significant, median dose: 53.6 Gy(RBE), 53.4 Gy(RBE), and 54.3 Gy(RBE) for techniques (b), (d), and (e) versus 52.1 Gy(RBE) for technique (a). Accounting for variable RBE distributions, the brainstem volume receiving at least 55 Gy(RBE) increased from 72.5% for technique (a) to 80.3% for (b) (P<.01) and from 70.7% for technique (c) to 77.6% for (d) (P<.01). Less than 2%, but statistically significant, decrease in maximum variable RBE-weighted brainstem dose was observed for the LET-sparing techniques compared with the corresponding dose-sparing (P=.03 and .004). CONCLUSIONS: Extending the proton range beyond the brainstem to reduce LET results in clinically comparable maximum radiobiologic effective dose to this sensitive structure. However this method significantly increasing the brainstem volume receiving RBE-weighted dose higher than 55 Gy(RBE) with possible consequences based on known dose-volume parameters for increased toxicity.

Reoptimization of Intensity Modulated Proton Therapy Plans Based on Linear Energy Transfer.

PURPOSE: We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution. METHODS AND MATERIALS: To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET. RESULTS: The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives. CONCLUSION: LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.

Concomitant Imaging Dose and Cancer Risk in Image Guided Thoracic Radiation Therapy.

PURPOSE: Kilovoltage cone beam computed tomography (CT) (kVCBCT) imaging guidance improves the accuracy of radiation therapy but imposes an extra radiation dose to cancer patients. This study aimed to investigate concomitant imaging dose and associated cancer risk in image guided thoracic radiation therapy. METHODS AND MATERIALS: The planning CT images and structure sets of 72 patients were converted to CT phantoms whose chest circumferences (Cchest) were calculated retrospectively. A low-dose thorax protocol on a Varian kVCBCT scanner was simulated by a validated Monte Carlo code. Computed doses to organs and cardiac substructures (for 5 selected patients of various dimensions) were regressed as empirical functions of Cchest, and associated cancer risk was calculated using the published models. The exposures to nonthoracic organs in children were also investigated. RESULTS: The structural mean doses decreased monotonically with increasing Cchest. For all 72 patients, the median doses to the heart, spinal cord, breasts, lungs, and involved chest were 1.68, 1.33, 1.64, 1.62, and 1.58 cGy/scan, respectively. Nonthoracic organs in children received 0.6 to 2.8 cGy/scan if they were directly irradiated. The mean doses to the descending aorta (1.43 ± 0.68 cGy), left atrium (1.55 ± 0.75 cGy), left ventricle (1.68 ± 0.81 cGy), and right ventricle (1.85 ± 0.84 cGy) were significantly different (P<.05) from the heart mean dose (1.73 ± 0.82 cGy). The blade shielding alleviated the exposure to nonthoracic organs in children by an order of magnitude. CONCLUSIONS: As functions of patient size, a series of models for personalized estimation of kVCBCT doses to thoracic organs and cardiac substructures have been proposed. Pediatric patients received much higher doses than did the adults, and some nonthoracic organs could be irradiated unexpectedly by the default scanning protocol. Increased cancer risks and disease adverse events in the thorax were strongly related to higher imaging doses and smaller chest dimensions.

Near Real-Time Assessment of Anatomic and Dosimetric Variations for Head and Neck Radiation Therapy via Graphics Processing Unit-based Dose Deformation Framework.

PURPOSE: The purpose of this study was to systematically monitor anatomic variations and their dosimetric consequences during intensity modulated radiation therapy (IMRT) for head and neck (H&N) cancer by using a graphics processing unit (GPU)-based deformable image registration (DIR) framework. METHODS AND MATERIALS: Eleven IMRT H&N patients undergoing IMRT with daily megavoltage computed tomography (CT) and weekly kilovoltage CT (kVCT) scans were included in this analysis. Pretreatment kVCTs were automatically registered with their corresponding planning CTs through a GPU-based DIR framework. The deformation of each contoured structure in the H&N region was computed to account for nonrigid change in the patient setup. The Jacobian determinant of the planning target volumes and the surrounding critical structures were used to quantify anatomical volume changes. The actual delivered dose was calculated accounting for the organ deformation. The dose distribution uncertainties due to registration errors were estimated using a landmark-based gamma evaluation. RESULTS: Dramatic interfractional anatomic changes were observed. During the treatment course of 6 to 7 weeks, the parotid gland volumes changed up to 34.7%, and the center-of-mass displacement of the 2 parotid glands varied in the range of 0.9 to 8.8 mm. For the primary treatment volume, the cumulative minimum and mean and equivalent uniform doses assessed by the weekly kVCTs were lower than the planned doses by up to 14.9% (P=.14), 2% (P=.39), and 7.3% (P=.05), respectively. The cumulative mean doses were significantly higher than the planned dose for the left parotid (P=.03) and right parotid glands (P=.006). The computation including DIR and dose accumulation was ultrafast (∼45 seconds) with registration accuracy at the subvoxel level. CONCLUSIONS: A systematic analysis of anatomic variations in the H&N region and their dosimetric consequences is critical in improving treatment efficacy. Nearly real-time assessment of anatomic and dosimetric variations is feasible using the GPU-based DIR framework. Clinical implementation of this technology may enable timely plan adaptation and improved outcome.

Quantification of proton dose calculation accuracy in the lung.

PURPOSE: To quantify the accuracy of a clinical proton treatment planning system (TPS) as well as Monte Carlo (MC)-based dose calculation through measurements and to assess the clinical impact in a cohort of patients with tumors located in the lung. METHODS AND MATERIALS: A lung phantom and ion chamber array were used to measure the dose to a plane through a tumor embedded in the lung, and to determine the distal fall-off of the proton beam. Results were compared with TPS and MC calculations. Dose distributions in 19 patients (54 fields total) were simulated using MC and compared to the TPS algorithm. RESULTS: MC increased dose calculation accuracy in lung tissue compared with the TPS and reproduced dose measurements in the target to within ±2%. The average difference between measured and predicted dose in a plane through the center of the target was 5.6% for the TPS and 1.6% for MC. MC recalculations in patients showed a mean dose to the clinical target volume on average 3.4% lower than the TPS, exceeding 5% for small fields. For large tumors, MC also predicted consistently higher V5 and V10 to the normal lung, because of a wider lateral penumbra, which was also observed experimentally. Critical structures located distal to the target could show large deviations, although this effect was highly patient specific. Range measurements showed that MC can reduce range uncertainty by a factor of ~2: the average (maximum) difference to the measured range was 3.9 mm (7.5 mm) for MC and 7 mm (17 mm) for the TPS in lung tissue. CONCLUSION: Integration of Monte Carlo dose calculation techniques into the clinic would improve treatment quality in proton therapy for lung cancer by avoiding systematic overestimation of target dose and underestimation of dose to normal lung. In addition, the ability to confidently reduce range margins would benefit all patients by potentially lowering toxicity.

Linear energy transfer-guided optimization in intensity modulated proton therapy: feasibility study and clinical potential.

PURPOSE: To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT). METHODS AND MATERIALS: A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose-volume and LET-volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs. RESULTS: The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation. CONCLUSIONS: We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in identifying the clinically optimal solution.