Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy.

Background and Purpose: In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and Methods: For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. Results: sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were -1.1 ± 0.6 [-2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively. Conclusions: DLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.

Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL-based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with coherent or mixed guidelines was investigated. A supervised 3D squeeze-and-excite SENet-154 model was trained to classify two bowel cavity delineation guidelines. In addition, a pelvis CT dataset with manual delineations from prostate cancer patients (n = 1854) was used where data with an alternative guideline for femoral heads, rectum, and bladder were generated using commercial software. The model was evaluated on internal (n = 200) and external test data (n = 99). By using mixed, compared to coherent, delineation guideline training data mean DICE score decreased 3% units, mean Hausdorff distance (95%) increased 5 mm and mean surface distance (MSD) increased 1 mm. The classification of bowel cavity test data achieved 99.8% unweighted classification accuracy, 99.9% macro average precision, 97.2% macro average recall, and 98.5% macro average F1. Corresponding metrics for the pelvis internal test data were all 99% or above and for the external pelvis test data they were 96.3%, 96.6%, 93.3%, and 94.6%. Impaired segmentation performance was observed for training data with mixed guidelines. The DL delineation classification models achieved excellent results on internal and external test data. This can facilitate automated guideline-specific data extraction while avoiding the need for consistent and correct structure labels.

Geometric impact and dose estimation of on-patient placement of a lightweight receiver coil in a clinical magnetic resonance imaging-only radiotherapy workflow for prostate cancer.

Background and Purpose: For pelvic magnetic resonance imaging (MRI)-only radiotherapy the use of receiver coil bridges (CB) is recommended to avoid deformation of the patient. Development in coil technology has enabled lightweight, flexible coils. In this work we evaluate the effects of a lightweight coil in a pelvic MRI-only radiotherapy workflow. Materials and Methods: Twenty-one patients, referred to prostate MRI-only radiotherapy, were included. Images were acquired with and without CB. Anatomical deformation from the on-patient coil placement was measured in the anterior-posterior (AP) and left-right (LR) direction. The change in signal-to-noise ratio (SNR) was measured in phantom and in vivo.The clinical treatment plan, created on the image with CB, was transferred and recalculated on the image without the CB. Dose metrics for the targets (planning- and clinical target volume) and organs at risks (OAR) were analyzed. Results: There was a statistically significant increase in SNR in-vivo (median 21 %, p = 0.002) when removing the CB. Anatomical differences after removing the CB in patients were -1.5 mm in AP (median change) and + 2.5 mm in LR direction. Dosimetric differences for the target structures were clinically negligible, but statistically significant. The difference in target mean doses were 0.2 % (both p = 0.004) of the prescribed dose. No dosimetric differences were observed for the OAR, except for the penile bulb. Conclusions: We concluded that anatomical change and dosimetric differences, originating from scanning without CB were minor. The CB can thereby be removed from the workflow, enabling easier patient positioning and increased SNR when using lightweight coils.

MRI-only radiotherapy from an economic perspective: Can new techniques in prostate cancer treatment be cost saving?

Background and Purpose: The aim of this study was to analyze a magnetic resonance imaging (MRI)-only radiotherapy workflow from an economic perspective in terms of reduced time, costs and systematic uncertainties. Material/Methods: A documented Swedish clinical implementation of MRI-only radiotherapy was used as template for cost assessments compared to a combined computed tomography (CT)/MRI workflow. The costs were taken from official regional price lists from 2021. MRI-only specific quality assurance (QA) was assumed necessary in an initial phase. Treatment plans for target volumes with margins of 5-10 mm were created for ten prostate cancer patients prescribed 78 Gy in 39 fractions. The risk of Grade ≥ 2 rectal toxicity or rectal bleeding was calculated using the QUANTEC recommended NTCP model and costs estimated based on subsequent diagnostic examinations. Results: The exclusion of the CT-examination and faster target delineation were the main contributors to cost reductions. Additional QA procedures limited the initial cost reduction to 14 EUR/patient. Long-term MRI-only reduced the costs by 209 EUR/patient. Reducing margins resulted in Grade ≥ 2 rectal toxicity or rectal bleeding probability of 9.7 % for 7 mm margin and 6.0 % for 5 mm margin. This margin reduction resulted in an additional cost reduction of 46 EUR/patient. Conclusion: An MRI-only workflow implementation is associated with reduced costs when the workflow tasks are more time efficient and side effects are reduced as a result of margin reduction. The short-term economic benefits are limited due to extra costs of QA procedures. The economic benefits of MRI-only will make impact first when the workflow is well established, and margin reduction has been included.

Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network.

BACKGROUND: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. METHODS: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1-4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. RESULTS: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. CONCLUSIONS: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.

Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures. Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1-3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic. A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data. Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic.

Investigation of the clinical inter-observer bias in prostate fiducial marker image registration between CT and MR images.

BACKGROUND AND PURPOSE: Inter-modality image registration between computed tomography (CT) and magnetic resonance (MR) images is associated with systematic uncertainties and the magnitude of these uncertainties is not well documented. The purpose of this study was to investigate the potential uncertainty of gold fiducial marker (GFM) registration for localized prostate cancer and to estimate the inter-observer bias in a clinical setting. METHODS: Four experienced observers registered CT and MR images for 42 prostate cancer patients. Manual GFM identification was followed by a landmark-based registration. The absolute difference between observers in GFM identification and the displacement of the clinical target volume (CTV) was investigated. The CTV center of mass (CoM) vector displacements, DICE-index and Hausdorff distances for the observer registrations were compared against a clinical baseline registration. The time allocated for the manual registrations was compared. RESULTS: Absolute difference in GFM identification between observers ranged from 0.0 to 3.0 mm. The maximum CTV CoM displacement from the clinical baseline was 3.1 mm. Displacements larger than or equal to 1 mm, 2 mm and 3 mm were 46%, 18% and 4%, respectively. No statistically significant difference was detected between observers in terms of CTV displacement. Median DICE-index and Hausdorff distance for the CTV, with their respective ranges were 0.94 [0.70-1.00] and 2.5 mm [0.7-8.7]. CONCLUSIONS: Registration of CT and MR images using GFMs for localized prostate cancer patients was subject to inter-observer bias on an individual patient level. A CTV displacement as large as 3 mm occurred for individual patients. These results show that GFM registration in a clinical setting is associated with uncertainties, which motivates the removal of inter-modality registrations in the radiotherapy workflow and a transition to an MRI-only workflow for localized prostate cancer.

Sarcopenia and dosimetric parameters in relation to treatment-related leukopenia and survival in anal cancer.

BACKGROUND: Treatment-related white blood cell (WBC) toxicity has been associated with an inferior prognosis in different malignancies, including anal cancer. The aim of the present study was to investigate predictors of WBC grade ≥ 3 (G3+) toxicity during chemoradiotherapy (CRT) of anal cancer. METHODS: Consecutive patients with locally advanced (T2 ≥ 4 cm-T4 or N+) anal cancer scheduled for two cycles of concomitant 5-fluorouracil and mitomycin C chemotherapy were selected from an institutional database (n = 106). All received intensity modulated radiotherapy (IMRT; mean dose primary tumor 59.5 Gy; mean dose elective lymph nodes 45.1 Gy). Clinical data were extracted from medical records. The highest-grade WBC toxicity was recorded according to CTCAE version 5.0. Pelvic bone marrow (PBM) was retrospectively contoured and dose-volume histograms were generated. The planning CT was used to measure sarcopenia. Dosimetric, anthropometric, and clinical variables were tested for associations with WBC G3+ toxicity using the Mann-Whitney test and logistic regression. Cox proportional hazard regression was used to assess predictors for overall survival (OS) and anal cancer specific survival (ACSS). RESULTS: WBC G3+ was seen in 50.9% of the patients, and 38.7% were sarcopenic. None of the dosimetric parameters showed an association with WBC G3+ toxicity. The most significant predictor of WBC G3+ toxicity was sarcopenia (adjusted OR 4.0; P = 0.002). Sarcopenia was also associated with an inferior OS (adjusted HR 3.9; P = 0.01), but not ACSS (P = 0.07). Sensitivity analysis did not suggest that the inferior prognosis for sarcopenic patients was a consequence of reduced doses of chemotherapy or a prolonged radiation treatment time. Patients who experienced WBC G3+ toxicity had an inferior OS and ACSS, even after adjustment for sarcopenia. CONCLUSIONS: Sarcopenia was associated with increased risks of both WBC G3+ toxicity and death following CRT for locally advanced anal cancer. In this study, radiation dose to PBM was not associated with WBC G3+ toxicity. However, PBM was not used as an organ at risk for radiotherapy planning purposes and doses to PBM were high, which may have obscured any dose-response relationships.

Patterns of pathologic lymph nodes in anal cancer: a PET-CT-based analysis with implications for radiotherapy treatment volumes.

BACKGROUND: This study investigates the patterns of PET-positive lymph nodes (LNs) in anal cancer. The aim was to provide information that could inform future anal cancer radiotherapy contouring guidelines. METHODS: The baseline [18F]-FDG PET-CTs of 190 consecutive anal cancer patients were retrospectively assessed. LNs with a Deauville score (DS) of ≥3 were defined as PET-positive. Each PET-positive LN was allocated to a LN region and a LN sub-region; they were then mapped on a standard anatomy reference CT. The association between primary tumor localization and PET-positive LNs in different regions were analyzed. RESULTS: PET-positive LNs (n = 412) were identified in 103 of 190 patients (54%). Compared to anal canal tumors with extension into the rectum, anal canal tumors with perianal extension more often had inguinal (P < 0.001) and less often perirectal (P < 0.001) and internal iliac (P < 0.001) PET-positive LNs. Forty-two patients had PET-positive LNs confined to a solitary region, corresponding to first echelon nodes. The most common solitary LN region was inguinal (25 of 42; 60%) followed by perirectal (26%), internal iliac (10%), and external iliac (2%). No PET-positive LNs were identified in the ischiorectal fossa or in the inguinal area located posterolateral to deep vessels. Skip metastases above the bottom of the sacroiliac joint were quite rare. Most external iliac PET-positive LNs were located posterior to the external iliac vein; only one was located in the lateral external iliac sub-region. CONCLUSIONS: The results support some specific modifications to the elective clinical target volume (CTV) in anal cancer. These changes would lead to reduced volumes of normal tissue being irradiated, which could contribute to a reduction in radiation side-effects.

Multimodal soft tissue markers for bridging high-resolution diagnostic imaging with therapeutic intervention.

Diagnostic imaging often outperforms the surgeon's ability to identify small structures during therapeutic procedures. Smart soft tissue markers that translate the sensitivity of diagnostic imaging into optimal therapeutic intervention are therefore highly warranted. This paper presents a unique adaptable liquid soft tissue marker system based on functionalized carbohydrates (Carbo-gel). The liquid state of these markers allows for high-precision placement under image guidance using thin needles. Based on step-by-step modifications, the image features and mechanical properties of markers can be optimized to bridge diagnostic imaging and specific therapeutic interventions. The performance of Carbo-gel is demonstrated for markers that (i) have radiographic, magnetic resonance, and ultrasound visibility; (ii) are palpable and visible; and (iii) are localizable by near-infrared fluorescence and radio guidance. The study demonstrates encouraging proof of concept for the liquid marker system as a well-tolerated multimodal imaging marker that can improve image-guided radiotherapy and surgical interventions, including robotic surgery.

Patterns of recurrence in anal cancer: a detailed analysis.

BACKGROUND: Anal cancer is a rare disease, which might be the reason for the "one size fits all" approach still used for radiotherapy target contouring. To refine and individualize future guidelines, detailed and contemporary pattern of recurrence studies are needed. METHODS: Consecutive anal cancer patients, all treated with curative intent intensity-modulated radiotherapy (IMRT), were retrospectively studied (n = 170). Data was extracted from medical records and radiological images. Radiotherapy planning CT's and treatment plans were reviewed, and recurrences were mapped and categorized according to radiation dose. RESULTS: The mean dose to the primary tumor was 59.0 Gy. With a median follow-up of 50 months (range 14-117 months), 5-year anal cancer specific survival was 86.1%. Only 1 of 20 local recurrences was located outside the high dose (CTVT) volume. More patients experienced a distant recurrence (n = 34; 20.0%) than a locoregional recurrence (n = 24; 14.1%). Seven patients (4.2%) had a common iliac and/or para-aortic (CI/PA) recurrence. External iliac lymph node involvement (P = 0.04), and metastases in ≥3 inguinal or pelvic lymph node regions (P = 0.02) were associated with a 15-18% risk of CI/PA recurrence. Following chemoradiotherapy, 6 patients with recurrent or primary metastatic CI/PA lymph nodes were free of recurrence at last follow-up. The overall rate of ano-inguinal lymphatic drainage (AILD) recurrence was 2 of 170 (1.2%), and among patients with inguinal metastases at initial diagnosis it was 2 of 65 (3.1%). CONCLUSIONS: We conclude that other measures than increased margins around the primary tumor are needed to improve local control. Furthermore, metastatic CI/PA lymph nodes, either at initial diagnosis or in the recurrent setting, should be considered potentially curable. Patients with certain patterns of metastatic pelvic lymph nodes might be at an increased risk of harboring tumor cells also in the CI/PA lymph nodes.

Analysis of early respiratory-related mortality after radiation therapy of non-small-cell lung cancer: feasibility of automatic data extraction for dose-response studies.

Purpose: To examine the feasibility of automatic data extraction from clinical radiation therapy (RT) databases at four hospitals to investigate the impact of mean lung dose (MLD) and age on the risk of early respiratory-related death and early overall death for patients treated with RT for non-small-cell lung cancer (NSCLC).Material and methods: We included adult patients with NSCLC receiving curatively intended RT between 2002 and 2017 at four hospitals. A script was developed to automatically extract RT-related data. The cause of death for patients deceased within 180 days of the start of RT was retrospectively assessed. Using logistic regression, the risks of respiratory-related death and of overall death within 90 and 180 days were investigated using MLD and age as variables.Results: Altogether, 1785 patients were included in the analysis of early overall mortality and 1655 of early respiratory-related mortality. The respiratory-related mortalities within 90 and 180 days were 0.9% (15/1655) and 3.6% (60/1655). The overall mortalities within 90 and 180 days were 2.5% (45/1785) and 10.6% (190/1785). Higher MLD and older age were associated with an increased risk of respiratory-related death within 180 days and overall death within 90 and 180 days (all p<.05). For example, the risk of respiratory-related death within 180 days and their 95% confidence interval for patients aged 65 and 75 years with MLDs of 20 Gy was according to our logistic model 3.8% (2.6-5.0%) and 7.7% (5.5-10%), respectively.Conclusions: Automatic data extraction was successfully used to pool data from four hospitals. MLD and age were associated with the risk of respiratory-related death within 180 days of the start of RT and with overall death within 90 and 180 days. A model quantifying the risk of respiratory-related death within 180 days was formulated.

Incorporating NTCP into Randomized Trials of Proton Versus Photon Therapy.

PURPOSE: We propose and simulate a model-based methodology to incorporate heterogeneous treatment benefit of proton therapy (PrT) versus photon therapy into randomized trial designs. We use radiation-induced pneumonitis (RP) as an exemplar. The aim is to obtain an unbiased estimate of how predicted difference in normal tissue complications probability (ΔNTCP) converts into clinical outcome on the patient level. MATERIALS AND METHODS: ΔNTCP data from in silico treatment plans for photon therapy and PrT for patients with locally advanced lung cancer as well as randomly sampled clinical risk factors were included in simulations of trial outcomes. The model used at point of analysis of the trials was an iQUANTEC model. Trial outcomes were examined with Cox proportional hazards models, both in case of a correctly specified model and in a scenario where there is discrepancy between the dose metric used for ΔNTCP and the dose metric associated with the "true" clinical outcome, that is, when the model is misspecified. We investigated how outcomes from such a randomized trial may feed into a model-based estimate of the patient-level benefit from PrT, by creating patient-specific predicted benefit probability distributions. RESULTS: Simulated trials showed benefit in accordance with that expected when the NTCP model was equal to the model for simulating outcome. When the model was misspecified, the benefit changed and we observed a reversal when the driver of outcome was high-dose dependent while the NTCP model was mean-dose dependent. By converting trial results into probability distributions, we demonstrated large heterogeneity in predicted benefit, and provided a randomized measure of the precision of individual benefit estimates. CONCLUSIONS: The design allows for quantifying the benefit of PrT referral, based on the combination of NTCP models, clinical risk factors, and traditional randomization. A misspecified model can be detected through a lower-than-expected hazard ratio per predicted ΔNTCP.

Deep inspiration breath hold in locally advanced lung cancer radiotherapy: validation of intrafractional geometric uncertainties in the INHALE trial.

OBJECTIVES: Patients with locally advanced non-small cell lung cancer (NSCLC) were included in a prospective trial for radiotherapy in deep inspiration breath hold (DIBH). We evaluated DIBH compliance and target position reproducibility. METHODS: Voluntary, visually guided DIBHs were performed with optical tracking. Patients underwent three consecutive DIBH CT scans for radiotherapy planning. We evaluated the intrafractional uncertainties in the position of the peripheral tumour, lymph nodes and differential motion between them, enabling PTV margins calculation. Patients who underwent all DIBH imaging and had tumour position reproducibility <8 mm were up-front DIBH compliant. Patients who performed DIBHs throughout the treatment course were overall DIBH compliant. Clinical parameters and DIBH-related uncertainties were validated against our earlier pilot study. RESULTS: 69 of 88 included patients received definitive radiotherapy. 60/69 patients (87%) were up-front DIBH compliant. DIBH plan was not superior in seven patients and three lost DIBH ability during the treatment, leaving 50/69 patients (72%) overall DIBH compliant.The systematic and random errors between consecutive DIBHs were small but differed from the pilot study findings. This led to slightly different PTV margins between the two studies. CONCLUSIONS: DIBH compliance and reproducibility was high. Still, this validation study highlighted the necessity of designing PTV margins in larger, representative patient cohorts. ADVANCES IN KNOWLEDGE: We demonstrated high DIBH compliance in locally advanced NSCLC patients. DIBH does not eliminate but mitigates the target position uncertainty, which needs to be accounted for in treatment margins. Margin design should be based on data from larger representative patient groups.

The dosimetric effect of residual breath-hold motion in pencil beam scanned proton therapy - An experimental study.

BACKGROUND AND PURPOSE: Motion management in the treatment of lung cancer is necessary to assure highest quality of the delivered radiation therapy. In this study, the breath-hold technique is experimentally investigated for pencil beam scanned (PBS) proton therapy, with respect to the dosimetric effect of residual breath-hold motion. MATERIAL AND METHODS: Three-dimensional (3D)-printed tumours extracted from CT scans of three patients were inserted into a dynamic anthropomorphic breathing phantom. The target was set up to move with the individual patient's tumour motion during breath-hold as previously assessed on fluoroscopy. Target dose was measured with radio-chromic film, and both single field uniform dose (SFUD) and intensity-modulated proton therapy (IMPT) plans were delivered. Experiments were repeated for each patient without any motion, to compute the relative dose deviation between static and breath-hold cases. RESULTS: SFUD plans showed small dose deviations between static and breath-hold cases, as evidenced by the gamma pass rate (3%, 3 mm) of 85% or higher. Dose deviation was more evident for IMPT plans, with gamma pass rate reduced to 50-70%. CONCLUSIONS: The breath-hold technique is robust to residual intra-breath-hold motion for SFUD treatment plans, based on our experimental study. IMPT was less robust with larger detected dose deviations.

Injectable colloidal gold in a sucrose acetate isobutyrate gelating matrix with potential use in radiation therapy.

External beam radiation therapy relies on the ability to deliver high radiation doses to tumor cells with minimal exposure to surrounding healthy tissue. Advanced irradiation techniques, including image-guided radiation therapy (IGRT), rely on the ability to locate tumors to optimize the therapeutic benefit of these techniques. Today, radiopaque fiducial tissue markers are placed in or around tumors, for example, in prostate cancer patients to enhance the precision of daily and/or real-time IGRT. A liquid injectable fiducial marker (nanogel) is developed based on PEGylated gold nanoparticles and sucrose acetate isobutyrate (SAIB) with improved properties compared to current solid fiducial markers. The developed nanogel is investigated in vitro and subsequently evaluated in vivo in immunocompetent NMRI mice. The nanogel shows high CT-contrast and excellent stability in vivo over a period of 12 weeks. The nanogel is found to be biocompatible and well tolerated. No induction of the inflammatory cytokines INF-γ, IL-6, or TNF-α is observed throughout the study period. The developed nanogel seems to be a safe injectable fiducial marker ideally suited for IGRT that may further enhance the effect of radiation.

Patient QA systems for rotational radiation therapy: a comparative experimental study with intentional errors.

PURPOSE: The purpose of the present study was to investigate the ability of commercial patient quality assurance (QA) systems to detect linear accelerator-related errors. METHODS: Four measuring systems (Delta(4®), OCTAVIUS(®), COMPASS, and Epiqa™) designed for patient specific quality assurance for rotational radiation therapy were compared by measuring four clinical rotational intensity modulated radiation therapy plans as well as plans with introduced intentional errors. The intentional errors included increasing the number of monitor units, widening of the MLC banks, and rotation of the collimator. The measurements were analyzed using the inherent gamma evaluation with 2% and 2 mm criteria and 3% and 3 mm criteria. When applicable, the plans with intentional errors were compared with the original plans both by 3D gamma evaluation and by inspecting the dose volume histograms produced by the systems. RESULTS: There was considerable variation in the type of errors that the various systems detected; the failure rate for the plans with errors varied between 0% and 72%. When using 2% and 2 mm criteria and 95% as a pass rate the Delta(4®) detected 15 of 20 errors, OCTAVIUS(®) detected 8 of 20 errors, COMPASS detected 8 of 20 errors, and Epiqa™ detected 20 of 20 errors. It was also found that the calibration and measuring procedure could benefit from improvements for some of the patient QA systems. CONCLUSIONS: The various systems can detect various errors and the sensitivity to the introduced errors depends on the plan. There was poor correlation between the gamma evaluation pass rates of the QA procedures and the deviations observed in the dose volume histograms.

RapidArc treatment verification in 3D using polymer gel dosimetry and Monte Carlo simulation.

The aim of this study was to verify the advanced inhomogeneous dose distribution produced by a volumetric arc therapy technique (RapidArc) using 3D gel measurements and Monte Carlo (MC) simulations. The TPS (treatment planning system)-calculated dose distribution was compared with gel measurements and MC simulations, thus investigating any discrepancy between the planned dose delivery and the actual delivery. Additionally, the reproducibility of the delivery was investigated using repeated gel measurements. A prostate treatment plan was delivered to a 1.3 liter nPAG gel phantom using one single arc rotation and a target dose of 3.3 Gy. Magnetic resonance imaging of the gel was carried out using a 1.5 T scanner. The MC dose distributions were calculated using the VIMC-Arc code. The relative absorbed dose differences were calculated voxel-by-voxel, within the volume enclosed by the 90% isodose surface (VOI(90)), for the TPS versus gel and TPS versus MC. The differences between the verification methods, MC versus gel, and between two repeated gel measurements were investigated in the same way. For all volume comparisons, the mean value was within 1% and the standard deviation of the differences was within 2.5% (1SD). A 3D gamma analysis between the dose matrices were carried out using gamma criteria 3%/3 mm and 5%/5 mm (% dose difference and mm distance to agreement) within the volume enclosed by the 50% isodose surface (VOI(50)) and the 90% isodose surface (VOI(90)), respectively. All comparisons resulted in very high pass rates. More than 95% of the TPS points were within 3%/3 mm of both the gel measurement and MC simulation, both inside VOI(50) and VOI(90). Additionally, the repeated gel measurements showed excellent consistency, indicating reproducible delivery. Using MC simulations and gel measurements, this verification study successfully demonstrated that the RapidArc plan was both accurately calculated and delivered as planned.