AbstractPurposeLiver cirrhosis disrupts liver function and tissue perfusion, detectable by magnetic resonance imaging (MRI). Assessing liver function at the voxel level with 13‐b value intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) could aid in radiation therapy liver‐sparing treatment for patients with early impairment. This study aimed to evaluate the feasibility of IVIM‐DWI for liver function assessment and correlate it with other multiparametric (mp) MRI methods at the voxel level.MethodThis study investigates the variability of apparent diffusion coefficient (ADC) derived from 13‐b value IVIM‐DWI and B1‐corrected dual flip angle (DFA) T1 mapping. Experiments were conducted in‐vitro with QIBA and NIST phantoms and in 10 healthy volunteers for IVIM‐DWI. Additionally, 12 patients underwent an mp‐MRI examination. The imaging protocol included a 13‐b value IVIM‐DWI sequence for generating IVIM parametric maps. B1‐corrected DFA T1 pulse sequence was used for generating T1 maps, and Gadoxatate low temporal resolution dynamic contrast‐enhanced (LTR‐DCE) MRI was used for generating the Hepatic extraction fraction (HEF) map. The Mann‐Whitney U test was employed to compare IVIM‐DWI parameters (Pure Diffusion, Dslow; Pseudo diffusion, Dfast; and Perfusion Fraction, Fp) between the healthy volunteer and patient groups. Furthermore, in the patient group, statistical correlations were assessed at a voxel level between LTR‐DCE MRI‐derived HEF, T1 post‐Gadoxetate administration, ΔT1%, and various IVIM parameters using Pearson correlation.ResultsFor‐vitro measurements, the maximum coefficient of variation of the ADC and T1 parameters was 12.4% and 16.1%, respectively. The results also showed that Fp and Dfast were able to distinguish between healthy liver function and mild liver function impairment at the global level, with p = 0.002 for Fp and p < 0.001 for Dfast. Within the patient group, these parameters also exhibited a moderate correlation with HEF at the voxel level.ConclusionOverall, the study highlighted the potential of Dfast and Fp for detecting liver function impairment at both global and pixel levels.

Purpose: To provide a metric that reflects the dosimetric utility of the synthetic CT (sCT) and can be rapidly determined.Methods: Retrospective CT and atlas-based sCT of 62 (53 IMRT and 9 VMAT) prostate cancer patients were used. For image similarity measurements, the sCT and reference CT (rCT) were aligned using clinical registration parameters. Conventional image similarity metrics including the mean absolute error (MAE) and mean error (ME) were calculated. The water equivalent depth (WED) was automatically determined for each patient on the rCT and sCT as the distance from the skin surface to the treatment plan isocentre at 36 equidistant gantry angles, and the mean WED difference (AWED) between the two scans was calculated. Doses were calculated on each scan pair for the clinical plan in the treatment planning system. The image similarity measurements and AWED were then compared to the isocentre dose difference (ADiso) between the two scans. Results: While no particular relationship to dose was observed for the other image similarity metrics, the ME results showed a linear trend against ADiso with R2 = 0.6, and the 95 % prediction interval for ADiso between-1.2 and 1 %. The AWED results showed an improved linear trend (R2 = 0.8) with a narrower 95 % prediction interval from-0.8 % to 0.8 %.Conclusion: AWED highly correlates with ADiso for the reference and synthetic CT scans. This is easy to calculate automatically and does not require time-consuming dose calculations. Therefore, it can facilitate the process of developing and evaluating new sCT generation algorithms.

Abstract Purpose Multi‐leaf‐collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is to test the sensitivity of machine log files in comparison to electronic portal imaging device (EPID)‐based measurements to MLC position errors caused by leaf backlash. The secondary aim is to investigate the effect of MLC leaf backlash on MLC leaf motion during clinical dynamic plan delivery. Methods The sensitivity of machine log files and two EPID‐based measurements were assessed via a controlled experiment, whereby the length of the “T” section of a series of 12 MLC leaf T‐nuts in a Varian Millennium MLC for a Trilogy C‐series type linac was reduced by sandpapering the top of the “T” to introduce backlash. The built‐in machine MLC leaf backlash test as well as measurements for two EPID‐based dynamic MLC positional tests along with log files were recorded pre‐ and post‐T‐nut modification. All methods were investigated for sensitivity to the T‐nut change by assessing the effect on measured MLC leaf positions. A reduced version of the experiment was repeated on a TrueBeam type linac with Millennium MLC. Results No significant differences before and after T‐nut modification were detected in any of the log file data. Both EPID methods demonstrated sensitivity to the introduced change at approximately the expected magnitude with a strong dependence observed with gantry angle. EPID‐based data showed MLC positional error in agreement with the micrometer measured T‐nut length change to 0.07 ± 0.05 mm (1 SD) using the departmental routine QA test. Backlash results were consistent between linac types. Conclusion Machine log files appear insensitive to MLC position errors caused by MLC leaf backlash introduced via the T‐nut. The effect of backlash on clinical MLC motions is heavily gantry angle dependent.

Thermal drift of optical systems employed for surface guided radiation therapy (SGRT) adds uncertainty to patient setup and monitoring. This work describes methods to measure the drift of individual camera pods as well as the drift of the combined clinical signal. It presents results for four clinical C-Rad Catalyst+ HD systems. Based on the measured clinical drift, recipes are provided on how to calculate relevant uncertainties in patient setup and patient position monitoring with SGRT. Strategies to reduce the impact of drift are explained. While the results are specific to the systems investigated, the methodology is transferable and the clinical recipes are universally applicable.

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

Radiotherapy treatment (RT) irradiates a patient's tumour volume while minimising damage to healthy tissue and surrounding critical organs at risk (OAR). In the conventional RT planning process, the RT planner has to iteratively adjust either the planning objectives (tumour or OAR dose levels) or the weights of the planning objectives until an acceptable plan is obtained that satisfies the minimum requirements. At the end of this iterative process, it remains unknown whether this plan is the best that can be obtained for the patient. The oncologist reviews each plan and decides to either treat using this plan or request further plan development, which may or may not lead to an actual improvement of the reviewed plan. We describe how Data Envelopment Analysis (DEA) is used as a real-time decision support tool to assess quality of RT plans for head and neck cancer patients by applying a knowledge-based comparison of each new plan to a library of previous clinically approved plans. This library allows benchmarking, which gives planners and oncologists a better idea of the relative quality of their plan and its improvement potential, resulting in improved use of resources and better quality treatments for patients. Our DEA-based approach provides a novel way of capturing multiple measures of plan quality as well as anatomical differences between patients in the benchmarking process. We present the developed DEA model and results for a set of benchmark instances. Initial results of integrating DEA-based quality feedback into the RT planning process are presented showing that operations research can contribute significantly to planning quality in this setting. (c) 2021 Elsevier B.V. All rights reserved.

Radiotherapy planning for lung cancer typically requires both 3D and 4D Computed Tomography (CT) to account for respiratory related movement. 4D Magnetic Resonance Imaging (MRI) with self-navigation offers a potential alternative with greater reliability in patients with irregular breathing patterns and improved soft tissue contrast. In this study 4D-CT and a 4D-MRI Radial Volumetric Interpolated Breath-hold Examination (VIBE) sequence was evaluated with a 4D phantom and 13 patient respiratory patterns, simulating tumour motion. Quantification of motion related tumour displacement in 4D-MRI and 4D-CT found no statistically significant difference in mean motion range. The results demonstrated the potential viability of 4D-MRI for lung cancer treatment planning.

Background and purpose: Magnetic resonance imaging (MRI)-only treatment planning is gaining in popularity in radiation oncology, with various methods available to generate a synthetic computed tomography (sCT) for this purpose. The aim of this study was to validate a sCT generation software for MRI-only radiotherapy planning of male and female pelvic cancers. The secondary aim of this study was to improve dose agreement by applying a derived relative electron and mass density (RED) curve to the sCT. Method and materials: Computed tomography (CT) and MRI scans of forty patients with pelvic neoplasms were used in the study. Treatment plans were copied from the CT scan to the sCT scan for dose comparison. Dose difference at reference point, 3D gamma comparison and dose volume histogram analysis was used to validate the dose impact of the sCT. The RED values were optimised to improve dose agreement by using a linear plot. Results: The average percentage dose difference at isocentre was 1.2% and the mean 3D gamma comparison with a criteria of 1%/1 mm was 84.0% +/- 9.7%. The results indicate an inherent systematic difference in the dosimetry of the sCT plans, deriving from the tissue densities. With the adapted REDmod table, the average percentage dose difference was reduced to -0.1% and the mean 3D gamma analysis improved to 92.9% +/- 5.7% at 1%/1 mm. Conclusions: CT generation software is a viable solution for MRI-only radiotherapy planning. The option makes it relatively easy for departments to implement a MRI-only planning workflow for cancers of male and female pelvic anatomy.

Abstract Purpose The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. Materials and methods In this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. Results The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. Conclusions Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

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.

BackgroundDue to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge.PurposeTo evaluate the efficacy of in vivo 3D 1H MR spectroscopic imaging (3D 1H 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 TypeProspective.SubjectsThirty‐six patients with biopsy‐proven central gland prostate cancer.Field Strength/Sequence3T MRI / 3D 1H MRSI using GOIA‐sLASER.AssessmentCancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1H 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 TestsFour 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.ResultsHigh‐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 ConclusionThe 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: 2Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2019;50:1926–1936.

Intrafraction motion review (IMR), a real-time 2D, motion management feature of the Varian Truebeam™ incorporates triggered imaging, automatic fiducial marker detection and automatic beam hold. With the increasing adoption of high dose per fraction stereotactic body radiotherapy (SBRT) this system provides a potential means to ensure treatment accuracy. The goal of this study was therefore to investigate and quantify key performance characteristics of IMR for prostate treatment guidance. Phantom experiments were performed with a custom Computerized Imaging Reference Systems, Inc (CIRS) pelvis phantom with implanted gold seeds and the Hexamotion™ 5D motion platform. The system accuracy was assessed statically and under typical prostate motion trajectories. The IMR functionality and marker detectability was tested under different anatomical conditions and with different imaging acquisition modes. Imaging dose for triggered imaging modes was determined using an ionisation chamber based on IPEMB dose calibration protocol for kV energies. For zero displacement, the IMR demonstrated submillimeter agreement with the known position. Similarly, dynamic motion differences between the IMR reported position and 2D trajectory displacement were within 1 mm. Static displacement in the anterior direction was reported by IMR as sinusoidal motion on the x-axis (kV angle). The 2D nature of IMR limits the ability to detect motion out of the plane of the kV image detector. Using typical clinical imaging settings, imaging dose determined at the patient surface was 2.58 mGy/frame and the corresponding IMR displayed dose was 2.63 mGy/frame. The methodology used was able to quantify the accuracy of the IMR system. The IMR was able to accurately and consistently report fiducial positions within the limitations inherent of a 2D system. IMR is fully integrated with the Truebeam system with an easy to use and efficient workflow and is clinically beneficial especially within the context of SBRT.

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 3T 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.

Aims Understanding the molecular mechanisms of underlying disease has led to a movement away from the one-drug-fits-all paradigm towards treatment tailored to the genetic profile of the patient. The Biocartis Idylla platform is a novel fully automated, real-time PCR–based in vitro diagnostic system. The Idylla NRAS-BRAF mutation test has been developed for the qualitative detection of mutations in NRAS and BRAF oncogenes, facilitating genetic profiling of patients with cancer. The aim of this study was to carry out a formal clinical performance evaluation. Methods Two-hundred and forty-two formalin-fixed paraffin-embedded (FFPE) human malignant colorectal cancer (CRC) tissue samples were identified in departmental archives and tested with both the Idylla NRAS-BRAF mutation test and the Agena Bioscience MassARRAY test. Results The overall concordance between the Idylla NRAS-BRAF mutation test and the MassARRAY comparator reference test result was 241/242 (99.59%, lower bound of one-sided 95% CI=98.1%) for NRAS and 242/242 (lower bound of 95% one-sided 95% CI=98.89%) for BRAF. The Idylla NRAS-BRAF test detected one NRAS mutation that had not been reported by the MassARRAY comparator reference test. Reanalysis of this sample by droplet digital PCR confirmed that the mutation was present, but at an allelic frequency below the stated sensitivity level of the MassARRAY system. Conclusion These results confirm that the Idylla NRAS-BRAF mutation test has high concordance with a widely used NRAS-BRAF test, and is therefore suitable for use as an in vitro diagnostic device for this application.