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Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning., (© 2023. The Author(s).)

Radiotherapy treatment planning based only on magnetic resonance imaging (MRI) has become clinically achievable. Though computed tomography (CT) is the gold standard for radiotherapy imaging, directly providing the electron density values needed for planning calculations, MRI has superior soft tissue visualisation to guide treatment planning decisions and optimisation. MRI-only planning removes the need for the CT scan, but requires generation of a substitute/synthetic/pseudo CT (sCT) for electron density information. Shortening the MRI imaging time would improve patient comfort and reduce the likelihood of motion artefacts. A volunteer study was previously carried out to investigate and optimise faster MRI sequences for a hybrid atlas-voxel conversion to sCT for prostate treatment planning. The aim of this follow-on study was to clinically validate the performance of the new optimised sequence for sCT generation in a treated MRI-only prostate patient cohort. 10 patients undergoing MRI-only treatment were scanned on a Siemens Skyra 3T MRI as part of the MRI-only sub-study of the NINJA clinical trial (ACTRN12618001806257). Two sequences were used, the standard 3D T2-weighted SPACE sequence used for sCT conversion which has been previously validated against CT, and a modified fast SPACE sequence, selected based on the volunteer study. Both were used to generate sCT scans. These were then compared to evaluate the fast sequence conversion for anatomical and dosimetric accuracy against the clinically approved treatment plans. The average Mean Absolute Error (MAE) for the body was 14.98 ± 2.35 HU, and for bone was 40.77 ± 5.51 HU. The external volume contour comparison produced a Dice Similarity Coefficient (DSC) of at least 0.976, and an average of 0.985 ± 0.004, and the bony anatomy contour comparison a DSC of at least 0.907, and an average of 0.950 ± 0.018. The fast SPACE sCT agreed with the gold standard sCT within an isocentre dose of -0.28% ± 0.16% and an average gamma pass rate of 99.66% ± 0.41% for a 1%/1 mm gamma tolerance. In this clinical validation study, the fast sequence, which reduced the required imaging time by approximately a factor of 4, produced an sCT with similar clinical dosimetric results compared to the standard sCT, demonstrating its potential for clinical use for treatment planning., (© 2023. Crown.)

This study quantified the performance of Intra-fraction Motion Review (IMR) during prostate Stereotactic Body Radiotherapy (SBRT) treatments. IMR was evaluated using prostate motion data from patients treated in an SBRT clinical trial (PROMETHEUS, NCT00587990).IMR measured prostate displacements were compared to those of two 3D motion management methods: Kilovoltage Intra-fraction Motion management (KIM) and MV/kV triangulation. A planning study assessing the impact of a defined prostate motion (2-5 mm) on the PTV  coverage with and without IMR was performed. A clinically relevant IMR search region for prostate cancer SBRT treatments was determined using a customised anthropomorphic pelvis phantom with implanted gold seeds and a motion platform. IMR showed submillimeter agreement with corresponding 2D projections from both KIM and MV/kV triangulation. However, IMR detected actual displacements consistently in considerably fewer frames than KIM (3D), with the actual numbers depending on the settings. The Default Search Region (DSR) method employing a circular search region proved superior to user-contoured structures in detecting clinically relevant prostate motion. Reducing the DSR search region radius can reduce the impact of the 2D nature of IMR and improve the detectability of actual motion (by 10% per 0.5 mm reduction) but must be balanced against increased beam interruptions from minor, clinically irrelevant motion. The use of IMR for SBRT prostate treatments has the potential to improve target dose coverage (minimum dose to 98% of the PTV, D98%) by > 20% compared to treatment without IMR. Calculated D98% of IMR monitored treatments with motion was within 1.5% of plans without motion., (© 2022. Australasian College of Physical Scientists and Engineers in Medicine.)

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning., (© 2021 Institute of Physics and Engineering in Medicine.)

Purpose: Reducing margins during treatment planning to decrease dose to healthy organs surrounding the prostate can risk inadequate treatment of subclinical disease. This study aimed to investigate whether lack of dose to subclinical disease is associated with increased disease progression by using high-quality prostate radiation therapy clinical trial data to identify anatomically localized regions where dose variation is associated with prostate-specific antigen progression (PSAP)., Methods and Materials: Planned dose distributions for 683 patients of the Trans-Tasman Radiation Oncology Group 03.04 Randomized Androgen Deprivation and Radiotherapy (RADAR) trial were deformably registered onto a single exemplar computed tomography data set. These were divided into high-risk and intermediate-risk subgroups for analysis. Three independent voxel-based statistical tests, using permutation testing, Cox regression modeling, and least absolute shrinkage selection operator feature selection, were applied to identify regions where dose variation was associated with PSAP. Results from the intermediate-risk RADAR subgroup were externally validated by registering dose distributions from the RT01 (n = 388) and Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy for Prostate Cancer Trial (CHHiP) (n = 253) trials onto the same exemplar and repeating the tests on each of these data sets., Results: Voxel-based Cox regression revealed regions where reduced dose was correlated with increased prostate-specific androgen progression. Reduced dose in regions associated with coverage at the posterior prostate, in the immediate periphery of the posterior prostate, and in regions corresponding to the posterior oblique beams or posterior lateral beam boundary, was associated with increased PSAP for RADAR and RT01 patients, but not for CHHiP patients. Reduced dose to the seminal vesicle region was also associated with increased PSAP for RADAR intermediate-risk patients., Conclusions: Ensuring adequate dose coverage at the posterior prostate and immediately surrounding posterior region (including the seminal vesicles), where aggressive cancer spread may be occurring, may improve tumor control. It is recommended that particular care be taken when defining margins at the prostate posterior, acknowledging the trade-off between quality of life due to rectal dose and the preferences of clinicians and patients., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

Background: Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge., Purpose: To evaluate the efficacy of in vivo 3D 1 H MR spectroscopic imaging (3D 1 H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels., Study Type: Prospective., Subjects: Thirty-six patients with biopsy-proven central gland prostate cancer., Field Strength/sequence: 3T MRI / 3D 1 H MRSI using GOIA-sLASER., Assessment: Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1 H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing., Statistical Tests: Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset., Results: High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%., Data Conclusion: The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer., Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926-1936., (© 2019 International Society for Magnetic Resonance in Medicine.)

Magnetic resonance images (MRI) require intensity standardisation if they are used for the purpose of quantitative analysis as inherent variations in image intensity levels between different image sets are manifest due to technical factors. One approach is to standardise the image intensity values using a statistically applied biological reference tissue. The aim of this study is to compare the performance of differing candidate biological reference tissues for standardising T2WI intensity distributions. Fifty-one prostate cancer patients across two centres with different scanners were evaluated using the percentage interpatient coefficient of variation (%interCV) for four different biological references; femoral bone marrow, ischioanal fossa, obturator-internus muscle and bladder urine. The tissue with the highest reproducibility (lowest %interCV) in both centres was used for intensity standardisation of prostate T2WI using three different statistical measures (mean, Z-score, median + Interquartile Range). The performance of different standardisation methods was evaluated from the assessment of image intensity histograms and the percentage normalised root mean square error (%NRSME) of the healthy peripheral zone tissue. Ischioanal fossa as a reference tissue demonstrated the highest reproducibility with %interCV of 18.9 for centre1 and 11.2 for centre2. Using ischioanal fossa for statistical intensity standardisation and the median + Interquartile Range method demonstrated the lowest %NRMSE across centres for healthy peripheral zone tissues. This study demonstrates ischioanal fossa as a preferred reference tissue for standardising intensity values from T2WI of the prostate. Subsequent image standardisation using the median + Interquartile Range intensity of the reference tissue demonstrated a robust and reliable standardisation method for quantitative image assessment.

Purpose: This study is aimed at evaluating the potential role of quantitative magnetic resonance diffusion tensor imaging (DTI) and tractography parameters in the detection and characterization of peripheral zone prostate cancer with a particular attention for fiber tract density., Materials and Methods: DTI was acquired from eleven high risk, transrectal ultrasound (TRUS)-guided biopsy proven prostate cancers with perineural invasion (histological Gleason score ≥ 7) on a 3 T magnet. Twenty parameters derived from DTI were quantified in cancer and healthy regions of the prostate. In addition, fiber tract density in normal versus cancer tissues was also calculated using DTI tractography. Support vector machine with a radial basis function kernel and area under receiver operator characteristic (ROC) were used to describe and compare the diagnostic performance of combined fractional anisotropy (FA) and mean diffusivity (MD) and other statistically significant DTI parameters. Spearman correlation analysis between DTI parameters and Gleason scores was conducted., Results: Eighteen DTI parameters yielded statistically significant differences between cancer and healthy regions (p-value < 0.05). The ROC curve of all statistically significant DTI parameters between cancer and healthy regions was higher than the area under ROC curve using FA + MD alone (95% confidence interval = 0.988, range = 0.975-1.00) vs (95% confidence interval = 0.935, range = 0.898-0.999), respectively (p-value < 0.05). Fiber tract density was also found to be higher in cancer than in healthy tissues (+38.22%, p-value = 0.010) and may be related to the increase in nerve and vascular density reported in prostate cancer. The linear and relative anisotropy were highly correlated with Gleason score (Spearman correlation factor r = 0.655, p-value = 0.001 and r = 0.667, p-value < 0.001, respectively)., Conclusions: DTI has the potential to provide imaging biomarkers in the detection and characterization of prostate cancer. Novel quantitative parameters derived from DTI and DTI tractography, including fiber tract density, support the use of DTI in the assessment of high grade prostate cancer., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)

The purpose of this study was to investigate performance of the couch and coil mounts designed for MR-simulation prostate scanning using data from ten volunteers. Volunteers were scanned using the standard MR scanning protocol with the MR coil directly strapped on the external body and the volunteer lying on the original scanner table. They also were scanned using a MR-simulation table top and pelvic coil mounts. MR images from both setups were compared in terms of body contour variation and image quality effects within particular organs of interest. Six-field conformal plans were generated on the two images with assigned bulk density for dose calculation. With the MR-simulation devices, the anterior skin deformation was reduced by up to 1.7 cm. The hard tabletop minimizes the posterior body deformation which can be up to 2.3 cm on the standard table, depending on the weight of volunteer. The image signal-to-noise ratio reduced by 14% and 25% on large field of view (FOV) and small FOV images, respectively, after using the coil mount; the prostate volume contoured on two images showed difference of 1.05 ± 0.66 cm3. The external body deformation caused a mean dose reduction of 0.6 ± 0.3 Gy, while the coverage reduced by 22% ± 13% and 27% ± 6% in V98 and V100, respectively. A dedicated MR simulation setup for prostate radiotherapy is essential to ensure the agreement between planning anatomy and treatment anatomy. The image signal was reduced after applying the coil mount, but no significant effect was found on prostate contouring.

Accurate localization of the prostate and its surrounding tissue is essential in the treatment of prostate cancer. This paper presents a novel approach to fully automatically segment the prostate, including its seminal vesicles, within a few minutes of a magnetic resonance (MR) scan acquired without an endorectal coil. Such MR images are important in external beam radiation therapy, where using an endorectal coil is highly undesirable. The segmentation is obtained using a deformable model that is trained on-the-fly so that it is specific to the patient's scan. This case specific deformable model consists of a patient specific initialized triangulated surface and image feature model that are trained during its initialization. The image feature model is used to deform the initialized surface by template matching image features (via normalized cross-correlation) to the features of the scan. The resulting deformations are regularized over the surface via well established simple surface smoothing algorithms, which is then made anatomically valid via an optimized shape model. Mean and median Dice's similarity coefficients (DSCs) of 0.85 and 0.87 were achieved when segmenting 3T MR clinical scans of 50 patients. The median DSC result was equal to the inter-rater DSC and had a mean absolute surface error of 1.85 mm. The approach is showed to perform well near the apex and seminal vesicles of the prostate.

Purpose: To assess the accuracy of the initial CT plan dose-volume histograms (DVH's) for prostate, rectum and bladder by comparison to delivered doses determined from cone beam CT (CBCT) scans acquired during image-guided treatment., Materials and Methods: Twelve prostate patients were treated using daily implanted fiducial guidance and following local protocol for bladder and rectal preparation. CBCT scans were acquired twice weekly and contoured for prostate, rectum and bladder. The planned beams were applied to all CBCT scans to determine the delivered doses. Prostate dose coverage was assessed by the proportion of the CTV fully encompassed by the 95% and 98% isodose lines. Rectal and bladder volumes receiving 40 Gy, 60 Gy and 70 Gy at treatment were compared to the initial plan, with significance determined using the one-sample t-test., Results: Four patients showed marginally compromised CTV coverage by the 95% isodose at all CBCT plans. For nine patients the initial plan rectal DVH was significantly outside the range of the treatment DVH's., Conclusions: Dose coverage of the prostate was not achieved for all patients. Observed rectal and bladder doses were higher than predicted. The initial treatment plan cannot be assumed to represent accurate normal tissue doses., (Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.)

Background: During prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correction alone. Purpose: The purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees‐of‐freedom tumor motion monitoring technology. Methods: The delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5‐fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as Tres$T_{res}$. Prostate intrafractional rotational motion Runcorr$R_{uncorr}$ was recorded but not corrected. The dose differences from the planned dose due to Tres$T_{res}$ + Runcorr$R_{uncorr}$, ΔD(Tres$T_{res}$ + Runcorr$R_{uncorr}$) and due to Tres$T_{res}$ alone, ΔD(Tres$T_{res}$), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation, Runcorr$R_{uncorr}$ was then quantified: ΔDResidual$\Delta D_{Residual}$ = ΔD(Tres$T_{res}$ + Runcorr$R_{uncorr}$) ‐ ΔD(Tres${T}_{\textit{res}}$). Results: Fractional data analysis shows that the dose differences from the plan (both ΔD(Tres$T_{res}$ + Runcorr$R_{uncorr}$) and ΔD(Tres$T_{res}$)) for CTV D98 was less than 5% in all treatment fractions. ΔD(Tres$T_{res}$ + Runcorr$R_{uncorr}$) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation, Runcorr$R_{uncorr}$ induced residual dose error, ΔDResidual$\Delta D_{Residual}$, resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D(Tres$T_{res}$ + Runcorr$R_{uncorr}$) and ∑D(Tres$T_{res}$), was always within 5% of the planned dose for all four structures for every patient. Conclusions: The dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3–5 mm CTV‐PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetric accuracy. [ABSTRACT FROM AUTHOR]

Introduction: The prostatic urethra is an organ at risk for prostate radiotherapy with genitourinary toxicities a common side effect. Many external beam radiation therapy protocols call for urethral sparing, and with modulated radiotherapy techniques, the radiation dose distribution can be controlled so that maximum doses do not fall within the prostatic urethral volume. Whilst traditional diagnostic MRI sequences provide excellent delineation of the prostate, uncertainty often remains as to the true path of the urethra within the gland. This study aims to assess if a high‐resolution isotropic 3D T2 MRI series can reduce inter‐observer variability in urethral delineation for radiotherapy planning. Methods: Five independent observers contoured the prostatic urethra for ten patients on three data sets; a 2 mm axial CT, a diagnostic 3 mm axial T2 TSE MRI and a 0.9 mm isotropic 3D T2 SPACE MRI. The observers were blinded from each other's contours. A Dice Similarity Coefficient (DSC) score was calculated using the intersection and union of the five observer contours vs an expert reference contour for each data set. Results: The mean DSC of the observer vs reference contours was 0.47 for CT, 0.62 for T2 TSE and 0.78 for T2 SPACE (P < 0.001). Conclusions: The introduction of a 0.9 mm isotropic 3D T2 SPACE MRI for treatment planning provides improved urethral visualisation and can lead to a significant reduction in inter‐observer variation in prostatic urethral contouring. [ABSTRACT FROM AUTHOR]

In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12. [ABSTRACT FROM AUTHOR]

Background: Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge.Purpose: To evaluate the efficacy of in vivo 3D 1 H MR spectroscopic imaging (3D 1 H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels.Study Type: Prospective.Subjects: Thirty-six patients with biopsy-proven central gland prostate cancer.Field Strength/sequence: 3T MRI / 3D 1 H MRSI using GOIA-sLASER.Assessment: Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1 H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing.Statistical Tests: Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset.Results: High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%.Data Conclusion: The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926-1936. [ABSTRACT FROM AUTHOR]

Prostate cancer treatment planning can be performed using magnetic resonance imaging (MRI) only with sCT scans. However, sCT scans are computer generated from MRI data and therefore robust, efficient, and accurate patient-specific quality assurance methods for dosimetric verification are required. Bulk anatomical density (BAD) maps can be generated based on anatomical contours derived from the MRI image. This study investigates and optimizes the BAD map approach for sCT quality assurance with a large patient CT and MRI dataset. 3D T2-weighted MRI and full density CT images of 54 patients were used to create BAD maps with different tissue class combinations. Mean Hounsfield units (HU) of Fat (F: below −30 HU), the entire Tissue [T: excluding bone (B)], and Muscle (M: excluding bone and fat) were derived from the CT scans. CT based BAD maps (BADBT,CT and BADBMF,CT) and a conventional bone and water bulk-density method (BADBW,CT) were compared to full CT calculations with bone assignments to 366 HU (measured) and 288 HU (obtained from literature). Optimal bulk densities of Tissue for BADBT,CT and Bone for BADBMF,CT were derived to provide zero mean isocenter dose agreement to the CT plan. Using the optimal densities, the dose agreement of BADBT,CT and BADBMF,CT to CT was redetermined. These maps were then created for the MRI dataset using auto-generated contours and dose calculations compared to CT. The average mean density of Bone, Fat, Muscle, and Tissue were 365.5 ± 62.2, −109.5 ± 12.9, 23.3 ± 9.7, and −46.3 ± 15.2 HU, respectively. Comparing to other bulk-density maps, BADBMF,CT maps provided the closest dose to CT. Calculated optimal mean densities of Tissue and Bone were −32.7 and 323.7 HU, respectively. The isocenter dose agreement of the optimal density assigned BADBT,CT and BADBMF,CT to full density CT were 0.10 ± 0.65% and 0.01 ± 0.45%, respectively. The isocenter dose agreement of MRI generated BADBT,MR and BADBMF,MR to full density CT were −0.15 ± 0.90% and −0.16 ± 0.65%, respectively. The BAD method with optimal bulk densities can provide robust, accurate and efficient patient-specific quality assurance for dose calculations in MRI-only radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: This project investigates the feasibility of implementation of MRI-only prostate planning in a prospective multi-center study. Method and Materials: A two-phase implementation model was utilized where centers performed retrospective analysis of MRI-only plans for five patients followed by prospective MRI-only planning for subsequent patients. Feasibility was assessed if at least 23/25 patients recruited to phase 2 received MRI-only treatment workflow. Whole-pelvic MRI scans (T2 weighted, isotropic 1.6 mm voxel 3D sequence) were converted to pseudo-CT using an established atlas-based method. Dose plans were generated using MRI contoured anatomy with pseudo-CT for dose calculation. A conventional CT scan was acquired subsequent to MRI-only plan approval for quality assurance purposes (QA-CT). 3D Gamma evaluation was performed between pseudo-CT calculated plan dose and recalculation on QA-CT. Criteria was 2%, 2 mm criteria with 20% low dose threshold. Gold fiducial marker positions for image guidance were compared between pseudo-CT and QA-CT scan prior to treatment. Results: All 25 patients recruited to phase 2 were treated using the MRI-only workflow. Isocenter dose differences between pseudo-CT and QA-CT were −0.04 ± 0.93% (mean ± SD). 3D Gamma dose comparison pass-rates were 99.7% ± 0.5% with mean gamma 0.22 ± 0.07. Results were similar for the two centers using two different scanners. All gamma comparisons exceeded the 90% pass-rate tolerance with a minimum gamma pass-rate of 98.0%. In all cases the gold fiducial markers were correctly identified on MRI and the distances of all seeds to centroid were within the tolerance of 1.0 mm of the distances on QA-CT (0.07 ± 0.41 mm), with a root-mean-square difference of 0.42 mm. Conclusion: The results support the hypothesis that an MRI-only prostate workflow can be implemented safely and accurately with appropriate quality assurance methods. [ABSTRACT FROM AUTHOR]

Introduction: This study quantified inter-observer contouring variations for multiple male pelvic structures, many of which are of emerging relevance for prostate cancer radiotherapy progression and toxicity response studies.Methods: Five prostate cancer patient datasets (CT and T2-weighted MR) were distributed to 13 observers for contouring. CT structures contoured included the clinical target volume (CTV), seminal vesicles, rectum, colon, bowel bag, bladder and peri-rectal space (PRS). MR contours included CTV, trigone, membranous urethra, penile bulb, neurovascular bundle and multiple pelvic floor muscles. Contouring variations were assessed using the intraclass correlation coefficient (ICC), Dice similarity coefficient (DSC), and multiple additional metrics.Results: Clinical target volume (CT and MR), bladder, rectum and PRS contours showed excellent inter-observer agreement (median ICC = 0.97; 0.99; 1.00; 0.95; 0.90, DSC = 0.83 ± 0.05; 0.88 ± 0.05; 0.93 ± 0.03; 0.81 ± 0.07; 0.80 ± 0.06, respectively). Seminal vesicle contours were more variable (ICC = 0.75, DSC = 0.73 ± 0.14), while colon and bowel bag contoured volumes were consistent (ICC = 0.97; 0.97), but displayed poor overlap (DSC = 0.58 ± 0.22; 0.67 ± 0.21). Smaller MR structures showed significant inter-observer variations, with poor overlap for trigone, membranous urethra, penile bulb, and left and right neurovascular bundles (DSC = 0.44 ± 0.22; 0.41 ± 0.21; 0.66 ± 0.21; 0.16 ± 0.17; 0.15 ± 0.15). Pelvic floor muscles recorded moderate to strong inter-observer agreement (ICC = 0.50-0.97), although large outlier variations were observed.Conclusions: Inter-observer contouring variation was significant for multiple pelvic structures contoured on MR. [ABSTRACT FROM AUTHOR]

Although radiation dose escalation improves prostate cancer disease control, it can cause increased toxicity. Genitourinary (GU) symptoms after prostate radiation therapy affect patient health-related quality of life (QoL). We compared patient-reported GU QoL outcomes following 2 alternative urethral sparing stereotactic body radiation therapy regimens. Expanded Prostate Cancer Index Composite (EPIC)–26 GU scores were compared between 2 urethral sparing stereotactic body radiation therapy trials. The SPARK trial prescribed a "Monotherapy" dose of 36.25 Gy in 5 fractions to the prostate. The PROMETHEUS trial prescribed 2 phases: a 19- to 21-Gy in 2 fractions "Boost" to the prostate, followed by 46 Gy in 23 fractions or 36 Gy in 12 fractions. The biological effective dose (BED) for urethral toxicity was 123.9 Gy for Monotherapy and 155.8 to 171.2 Gy for Boost. Mixed effects logistic regression models were utilized to estimate the difference in the odds of a minimal clinically important change from baseline EPIC-26 GU score between regimens at each follow-up. 46 Monotherapy and 149 Boost patients completed baseline EPIC-26 scoring. Mean EPIC-26 GU scores revealed statistically superior urinary incontinence outcomes for Monotherapy at 12 months (mean difference, 6.9; 95% confidence interval [CI], 1.6-12.1; P =.01) and 36 months (mean difference, 9.6; 95% CI, 4.1-15.1; P <.01). Monotherapy also revealed superior mean urinary irritative/obstructive outcomes at 12 months (mean difference, 6.9; 95% CI, 2.0-12.9; P <.01) and 36 months (mean difference, 6.3; 95% CI, 1.9-10.8; P <.01). For both domains and at all time points, the absolute differences were <10%. There were no significant differences in the odds of reporting a minimal clinically important change between regimens at any time point. Even in the presence of urethral sparing, the higher BED delivered in the Boost schedule may have a small adverse effect on GU QoL compared with Monotherapy. However, this did not translate to statistically significant differences in minimal clinically important changes. Whether the higher BED of the boost arm offers an efficacy advantage is being investigated in the Trans Tasman Radiation Oncology Group 18.01 NINJA randomized trial. [ABSTRACT FROM AUTHOR]

Abstract: Background and purpose: A multi-centre clinical trial for prostate cancer patients provided an opportunity to introduce conformal radiotherapy with dose escalation. To verify adequate treatment accuracy prior to patient recruitment, centres submitted details of a set-up accuracy study (SUAS). We report the results of the SUAS, the variation in clinical practice and the strategies used to help centres improve treatment accuracy. Materials and methods: The SUAS required each of the 24 participating centres to collect data on at least 10 pelvic patients imaged on a minimum of 20 occasions. Software was provided for data collection and analysis. Support to centres was provided through educational lectures, the trial quality assurance team and an information booklet. Results: Only two centres had recently carried out a SUAS prior to the trial opening. Systematic errors were generally smaller than those previously reported in the literature. The questionnaire identified many differences in patient set-up protocols. As a result of participating in this QA activity more than 65% of centres improved their treatment delivery accuracy. Conclusions: Conducting a pre-trial SUAS has led to improvement in treatment delivery accuracy in many centres. Treatment techniques and set-up accuracy varied greatly, demonstrating a need to ensure an on-going awareness for such studies in future trials and with the introduction of dose escalation or new technologies. [Copyright &y& Elsevier]

• Voxel-wise analysis correlated rectal toxicities with dose in the pelvic anatomy. • Grade ≥2 bleeding correlated with high doses to a small rectal volume. • This confirmed the serial response of the rectum with respect to rectal bleeding. • Grade ≥2 tenesmus correlated with low-intermediate doses to the perirectal fat space. • This demonstrated the parallel response of the PRFS with respect to tenesmus. This study aimed to identify anatomically-localised regions where planned radiotherapy dose is associated with gastrointestinal toxicities in healthy tissues throughout the pelvic anatomy. Planned dose distributions for up to 657 patients of the Trans Tasman Radiation Oncology Group 03.04 RADAR trial were deformably registered onto a single exemplar computed tomography dataset. Voxel-based multiple comparison permutation dose difference testing, Cox regression modelling and LASSO feature selection were used to identify regions where dose-increase was associated with grade ≥2 rectal bleeding (RB) or tenesmus, according to the LENT/SOMA scale. This was externally validated by registering dose distributions from the RT01 (n = 388) and CHHiP (n = 241) trials onto the same exemplar and repeating the tests on each of these data sets, and on all three datasets combined. Voxel-based Cox regression and permutation dose difference testing revealed regions where increased dose was correlated with gastrointestinal toxicity. Grade ≥2 RB was associated with posteriorly extended lateral beams that manifested high doses (>55 Gy) in a small rectal volume adjacent to the clinical target volume. A correlation was found between grade ≥2 tenesmus and increased low-intermediate dose (∼25 Gy) at the posterior beam region, including the posterior rectum and perirectal fat space (PRFS). The serial response of the rectum with respect to RB has been demonstrated in patients with posteriorly extended lateral beams. Similarly, the parallel response of the PRFS with respect to tenesmus has been demonstrated in patients treated with the posterior beam. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM).Methods and Materials: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy). T2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CTref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed.Results: Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM.Conclusions: Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time. [ABSTRACT FROM AUTHOR]

• A clinical target volume delineation quality assurance (QA) framework for MRI-guided prostate radiotherapy was proposed. • The framework adopted a deep learning network with Monte Carlo dropout for uncertainty estimation. • Spatial correlation between the manual delineation and the area of uncertainty generated by the network was incorporated in the QA criteria. • The framework was evaluated on a multicentre MRI-only prostate radiotherapy trial dataset and achieved promising performance in QA classification. Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials. [ABSTRACT FROM AUTHOR]

Background and purpose Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2 w ) MR images of the pelvis. Materials and methods This study developed a generalized dual model HU conversion method to convert standard T2 w MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. Results For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 ± 6 HUs (34 HUs) and −46 ± 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was −0.6 ± 0.4% (−1.3%). Conclusions The study provides a generalized method for sCT creation from standard T2 w images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences. [ABSTRACT FROM AUTHOR]

Purpose: To validate automatic substitute computed tomography CT (sCT) scans generated from standard T2-weighted (T2w) magnetic resonance (MR) pelvic scans for MR-Sim prostate treatment planning.Patients and Methods: A Siemens Skyra 3T MR imaging (MRI) scanner with laser bridge, flat couch, and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole-pelvis MRI scan (1.6 mm 3-dimensional isotropic T2w SPACE [Sampling Perfection with Application optimized Contrasts using different flip angle Evolution] sequence) was acquired. Three additional small field of view scans were acquired: T2w, T2*w, and T1w flip angle 80° for gold fiducials. Patients received a routine planning CT scan. Manual contouring of the prostate, rectum, bladder, and bones was performed independently on the CT and MR scans. Three experienced observers contoured each organ on MRI, allowing interobserver quantification. To generate a training database, each patient CT scan was coregistered to their whole-pelvis T2w using symmetric rigid registration and structure-guided deformable registration. A new multi-atlas local weighted voting method was used to generate automatic contours and sCT results.Results: The mean error in Hounsfield units between the sCT and corresponding patient CT (within the body contour) was 0.6 ± 14.7 (mean ± 1 SD), with a mean absolute error of 40.5 ± 8.2 Hounsfield units. Automatic contouring results were very close to the expert interobserver level (Dice similarity coefficient): prostate 0.80 ± 0.08, bladder 0.86 ± 0.12, rectum 0.84 ± 0.06, bones 0.91 ± 0.03, and body 1.00 ± 0.003. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same dose prescription was found to be 0.3% ± 0.8%. The 3-dimensional γ pass rate was 1.00 ± 0.00 (2 mm/2%).Conclusions: The MR-Sim setup and automatic sCT generation methods using standard MR sequences generates realistic contours and electron densities for prostate cancer radiation therapy dose planning and digitally reconstructed radiograph generation. [ABSTRACT FROM AUTHOR]