Impact of image quality on radiomics applications

Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.

The Role of a DirectDensity® CT Reconstruction in A Radiotherapy Workflow: A Phantom Study

The DirectDensity® CT reconstruction algorithm provides a reconstruction approach independent of the tube voltage, directly reconstructing the CT projection data into CT numbers related to the electron densities of the materials. This work examines the efficacy of DirectDensity® in the treatment planning process with both tissues and metallic materials. CT scans of a Cheese phantom were acquired at 80, 100, 120 and 140 kVp and reconstructed with different algorithms. Calibration curves were built for each kVp and reconstruction technique. To evaluate the flexibility of the DirectDensity® in dose calculations, a prostate cancer treatment plan was simulated on phantom images with and without metal inserts. Moreover, the robustness of the algorithm was tested by simulating a possible error in the selection of the calibration curve. As expected, the calibration curves related to DirectDensity® showed a tube voltage dependence only for densities above 1.82 g/cm3. The maximum percentage differences in dose distributions comparations never exceeded the 3% of tolerance and the 3D gamma analysis always returned indices greater than 90%. The results suggest that the DD reconstruction algorithm can be employed in most clinical cases and allows for a personalized radiotherapy cancer treatment workflow, maintaining its robustness and simplicity.

Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT

BACKGROUND

Fast and accurate multiorgans segmentation from computed tomography (CT) scans is essential for radiation treatment planning. Self-attention(SA)-based deep learning methodologies provide higher accuracies than standard methods but require memory and computationally intensive calculations, which restricts their use to relatively shallow networks.

PURPOSE

Our goal was to develop and test a new computationally fast and memory-efficient bidirectional SA method called nested block self-attention (NBSA), which is applicable to shallow and deep multiorgan segmentation networks.

METHODS

A new multiorgan segmentation method combining a deep multiple resolution residual network with computationally efficient SA called nested block SA (MRRN-NBSA) was developed and evaluated to segment 18 different organs from head and neck (HN) and abdomen organs. MRRN-NBSA combines features from multiple image resolutions and feature levels with SA to extract organ-specific contextual features. Computational efficiency is achieved by using memory blocks of fixed spatial extent for SA calculation combined with bidirectional attention flow. Separate models were trained for HN (n = 238) and abdomen (n = 30) and tested on set aside open-source grand challenge data sets for HN (n = 10) using a public domain database of computational anatomy and blinded testing on 20 cases from Beyond the Cranial Vault data set with overall accuracy provided by the grand challenge website for abdominal organs. Robustness to two-rater segmentations was also evaluated for HN cases using the open-source data set. Statistical comparison of MRRN-NBSA against Unet, convolutional network-based SA using criss-cross attention (CCA), dual SA, and transformer-based (UNETR) methods was done by measuring the differences in the average Dice similarity coefficient (DSC) accuracy for all HN organs using the Kruskall-Wallis test, followed by individual method comparisons using paired, two-sided Wilcoxon-signed rank tests at 95% confidence level with Bonferroni correction used for multiple comparisons.

RESULTS

MRRN-NBSA produced an average high DSC of 0.88 for HN and 0.86 for the abdomen that exceeded current methods. MRRN-NBSA was more accurate than the computationally most efficient CCA (average DSC of 0.845 for HN, 0.727 for abdomen). Kruskal-Wallis test showed significant difference between evaluated methods (p=0.00025). Pair-wise comparisons showed significant differences between MRRN-NBSA than Unet (p=0.0003), CCA (p=0.030), dual (p=0.038), and UNETR methods (p=0.012) after Bonferroni correction. MRRN-NBSA produced less variable segmentations for submandibular glands (0.82 ± 0.06) compared to two raters (0.75 ± 0.31).

CONCLUSIONS

MRRN-NBSA produced more accurate multiorgan segmentations than current methods on two different public data sets. Testing on larger institutional cohorts is required to establish feasibility for clinical use.

Effect of stomach size on organs at risk in pancreatic stereotactic body radiotherapy

BACKGROUND

In clinical practice, the organs at risk (OARs) should be carefully determined when performing pancreatic stereotactic body radiotherapy (SBRT). We conducted a simulation study to examine the effect of the stomach size on the radiation dose to the OARs when performing pancreatic SBRT.

METHODS

Twenty-five cases were included in this study. Pancreatic head and body tumors were 2-cm-sized pseudotumors, which were included as gross target volume (GTV) contours. The stomach, pancreas, small intestine, liver, kidneys, and spinal cord were considered as the OARs. The prescription dose for planning target volume (PTV) was 40 Gy/5fx, and the dose limit for the OARs was determined. The dose to X% of the OAR volume at X values of 0.1, 5.0, and 10.0 cc (DX) and the percentage of the OAR volume that received more than X Gy were recorded.

RESULTS

In terms of the radiation dose to the pancreatic body tumors, the stomach size was positively correlated with a dose of D10cc [correlation coefficient (r) = 0.5516) to the stomach. The r value between the radiation dose to the pancreatic head tumor and the stomach size was 0.3499. The stomach size and radiation dose to the head and body of the pancreas were positively correlated (pancreatic head D10cc: r = 0.3979, pancreatic body D10cc: r = 0.3209). The larger the stomach, the larger the radiation dose to the healthy portion of the pancreas outside the PTV.

CONCLUSIONS

When performing pancreatic SBRT, the dose to the OARs depends on the stomach size. Reducing the dose to the stomach and pancreas can be achieved by shrinking the stomach.

Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network

PURPOSE

Contouring clinical target volume (CTV) from medical images is an essential step for radiotherapy (RT) planning. Magnetic resonance imaging (MRI) is used as a standard imaging modality for CTV segmentation in cervical cancer due to its superior soft-tissue contrast. However, the delineation of CTV is challenging as CTV contains microscopic extensions that are not clearly visible even in MR images, resulting in significant contour variability among radiation oncologists depending on their knowledge and experience. In this study, we propose a fully automated deep learning-based method to segment CTV from MR images.

METHODS

Our method begins with the bladder segmentation, from which the CTV position is estimated in the axial view. The superior-inferior CTV span is then detected using an Attention U-Net. A CTV-specific region of interest (ROI) is determined, and three-dimensional (3-D) blocks are extracted from the ROI volume. Finally, a CTV segmentation map is computed using a 3-D U-Net from the extracted 3-D blocks.

RESULTS

We developed and evaluated our method using 213 MRI scans obtained from 125 patients (183 for training, 30 for test). Our method achieved (mean ± SD) Dice similarity coefficient of 0.85 ± 0.03 and the 95th percentile Hausdorff distance of 3.70 ± 0.35 mm on test cases, outperforming other state-of-the-art methods significantly (p-value < 0.05). Our method also produces an uncertainty map along with the CTV segmentation by employing the Monte Carlo dropout technique to draw physician's attention to the regions with high uncertainty, where careful review and manual correction may be needed.

CONCLUSIONS

Experimental results show that the developed method is accurate, fast, and reproducible for contouring CTV from MRI, demonstrating its potential to assist radiation oncologists in alleviating the burden of tedious contouring for RT planning in cervical cancer.

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

MRI-guided Radiotherapy (MRgRT) for treatment of Oligometastases: Review of clinical applications and challenges

PURPOSE

Early clinical results on the application of magnetic resonance imaging (MRI) coupled with a linear accelerator to deliver MR-guided radiation therapy (MRgRT) have demonstrated feasibility for safe delivery of stereotactic body radiotherapy (SBRT) in treatment of oligometastatic disease. Here we set out to review the clinical evidence and challenges associated with MRgRT in this setting.

METHODS AND MATERIALS

We performed a systematic review of the literature pertaining to clinical experiences and trials on the use of MRgRT primarily for the treatment of oligometastatic cancers. We reviewed the opportunities and challenges associated with the use of MRgRT.

RESULTS

Benefits of MRgRT pertaining to superior soft-tissue contrast, real-time imaging and gating, and online adaptive radiotherapy facilitate safe and effective dose escalation to oligometastatic tumors while simultaneously sparing surrounding healthy tissues. Challenges concerning further need for clinical evidence and technical considerations related to planning, delivery, quality assurance (QA) of hypofractionated doses, and safety in the MRI environment must be considered.

CONCLUSIONS

The promising early indications of safety and effectiveness of MRgRT for SBRT-based treatment of oligometastatic disease in multiple treatment locations should lead to further clinical evidence to demonstrate the benefit of this technology.

Repeatability of diffusion-weighted magnetic resonance imaging in head and neck cancer at a 1.5 T MR-Linac

BACKGROUND AND PURPOSE

Functional information acquired through diffusion-weighted magnetic resonance imaging (DW-MRI) may be beneficial for personalized head and neck cancer (HNC) radiotherapy. Technical validation is required before DW-MRI based radiotherapy interventions can be realized clinically. The aim of this study was to assess the repeatability of apparent diffusion coefficients (ADC) derived from DW-MRI in HNC using echo-planar imaging (EPI) on a 1.5 T MR-Linac.

MATERIAL AND METHODS

A total of eleven HNC patients underwent test/retest DW-MRI scans at least once per week during fractionated radiotherapy at the MR-Linac. An EPI DW-MRI test scan (b=0, 150, 500 s/mm²) was acquired before the start of adaptive MR-guided radiotherapy in addition to an identical retest scan after irradiation. Volumes-of-interest (VOI) were defined manually for parotid (PTs) and submandibular glands (SMs), gross tumor volume (GTV) and lymph nodes (LNs). Mean ADC was calculated for all VOI in all test/retest scans. Absolute/relative repeatability coefficients (RCs/relRCs) as well as intraclass correlation coefficients (ICCs) were determined for all VOI.

RESULTS

A total of 81 datasets were analyzed. Mean test ADC values were 1380/1416, 950/1010, 1520 and 1344·10^-6 mm²/s for left/right SM and PT, GTV and LNs, respectively. Accordingly, RC (relRC) values were determined as 271/281 (19.4/21.8%) and 138/155 (13.3/15.2%), 457 (31.3%) and 310·10^-6 mm²/s (23.5%). ICC resulted in 0.80/0.87, 0.97/0.94, 0.75 and 0.83 for left/right SM and PT, GTV and LNs, respectively.

CONCLUSION

The repeatability of ADC derived from EPI DW-MRI at the 1.5 T MR-Linac appears reasonable to be used for future biologically adapted MR-guided radiotherapy.

Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer

PURPOSE

Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC).

METHODS

Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity.

RESULTS

Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features.

CONCLUSION

Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models.

Dosimetry Audit in Modern Radiotherapy

The clinical specialty of radiotherapy is an essential part of the multidisciplinary process of treatment of malignant neoplasms. Modern radiotherapy is a very complex process of treatment planning and delivery of radiation dose. Radiotherapy reached a very high degree of complexity and sophistication and expected to represent an added value for the cancer patients in terms of clinical outcomes and improved radiation protection. The concept of verifying the realized dose in the medical applications of ionizing radiation was introduced in the early 20th century shortly after the first application of X-rays for the treatment of cancer. Dosimetry audit identify areas for improvement and provide confidence in safety and efficacy, which are essential to creating a clinical environment of continuous development and improvement. Over the years, the audits have contributed to good dosimetry practice and accuracy of dose measurements in modern radiotherapy. Dosimetry audit ensures, that the correct therapeutic dose is delivered to the patients undergoing radiotherapy and play a key role in activities to create a good radiation protection and safety culture. Patient safety is of paramount importance to medical staff in radiotherapy centers and safety considerations are an element in all aspects of the day-to-day clinical activities.

Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks

Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer's disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.

Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI

Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but instability in the feature selection step may reduce their reliability. Therefore, in this study, we aim to discriminate between early-stage T1 NPC and BH on MRI using radiomics and propose a method to improve the stability of the feature selection step in the radiomics pipeline. A radiomics model was trained using data from 442 patients (221 early-stage T1 NPC and 221 with BH) scanned at 3T and tested on 213 patients (99 early-stage T1 NPC and 114 BH) scanned at 1.5T. To verify the improvement in feature selection stability, we compared our proposed ensemble technique, which uses a combination of bagging and boosting (BB-RENT), with the well-established elastic net. The proposed radiomics model achieved an area under the curve of 0.85 (95% confidence interval (CI): 0.82−0.89) and 0.80 (95% CI: 0.74−0.86) in discriminating NPC and BH in the 3T training and 1.5T testing cohort, respectively, using 17 features selected from a pool of 422 features by the proposed feature selection technique. BB-RENT showed a better feature selection stability compared to the elastic net (Jaccard index = 0.39 ± 0.14 and 0.24 ± 0.06, respectively; p < 0.001).

Towards Accurate and Precise Image-Guided Radiotherapy: Clinical Applications of the MR-Linac

Advances in image-guided radiotherapy have brought about improved oncologic outcomes and reduced toxicity. The next generation of image guidance in the form of magnetic resonance imaging (MRI) will improve visualization of tumors and make radiation treatment adaptation possible. In this review, we discuss the role that MRI plays in radiotherapy, with a focus on the integration of MRI with the linear accelerator. The MR linear accelerator (MR-Linac) will provide real-time imaging, help assess motion management, and provide online adaptive therapy. Potential advantages and the current state of these MR-Linacs are highlighted, with a discussion of six different clinical scenarios, leading into a discussion on the future role of these machines in clinical workflows.

Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy

PURPOSE

To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm.

METHODS

A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases.

RESULTS

Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful.

CONCLUSION

The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.

First experimental demonstration of VMAT combined with MLC tracking for single and multi fraction lung SBRT on an MR-linac

BACKGROUND AND PURPOSE

VMAT is not currently available on MR-linacs but could maximize plan conformality. To mitigate respiration without compromising delivery efficiency, MRI-guided MLC tumour tracking was recently developed for the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) in combination with IMRT. Here, we provide a first experimental demonstration of VMAT+MLC tracking for several lung SBRT indications.

MATERIALS AND METHODS

We created central patient and phantom VMAT plans (8×7.5 Gy, 2 arcs) and we created peripheral phantom plans (3×18 & 1×34 Gy, 4 arcs). A motion phantom mimicked subject-recorded respiratory motion (A‾=11 mm, f‾=0.33 Hz, drift‾=0.3 mm/min). This was monitored using 2D-cine MRI at 4 Hz to continuously realign the beam with the target. VMAT+MLC tracking performance was evaluated using 2D film dosimetry and a novel motion-encoded and time-resolved pseudo-3D dosimetry approach.

RESULTS

We found an MLC leaf and jaw end-to-end latency of 328.05(±3.78) ms and 317.33(±4.64) ms, which was mitigated by a predictor. The VMAT plans required maximum MLC speeds of 12.1 cm/s and MLC tracking superimposes an additional 1.48 cm/s. A local 2%/1 mm gamma analysis with a static measurement as reference, revealed pass-rates of 28-46% without MLC tracking and 88-100% with MLC tracking for the 2D film analysis. Similarly the pseudo-3D gamma passing-rates increased from 22-77% to 92-100%. The dose area histograms show that MLC tracking increased the GTV D_98% by 5-20% and the PTV D_95% by 7-24%, giving similar target coverage as their respective static reference.

CONCLUSION

MRI-guided VMAT+MLC tracking is technically feasible on the MR-linac and results in highly conformal dose distribution.

Deep learning methods for enhancing cone-beam CT image quality towards adaptive radiation therapy: A systematic review

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings - with emphasis on study design and deep learning techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarised, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state of the art methods utilized in radiation oncology. This article is protected by copyright. All rights reserved.

Quality assurance of a breathing controlled four-dimensional computed tomography algorithm

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Initial quality assurance of a novel breathing-controlled four-dimensional computed tomography algorithm.

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Assessment of geometry, motion representation and image quality for regular and irregular breathing.

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No clinically relevant differences in results for regular and irregular breathing.

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Only minor differences in tumor geometry representation and image quality compared to static three-dimensional computed tomography.

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Table flexion has no clinically relevant impact on geometry representation.

Background & purpose

Material & methods

Results

Conclusions

Dose-volume analysis of planned versus accumulated dose as a predictor for late gastrointestinal toxicity in men receiving radiotherapy for high-risk prostate cancer

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Interfractional variations in organs at risk were observed in prostate radiotherapy.

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Rectal accumulated dose was significantly higher at the intermediate-high dose region.

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Rectal planned dose was significantly higher at the very high dose region.

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Dose>78.2 Gy to 0.03 cc of rectum was predictive of late Grade 2 toxicity.

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Patient age>72 years was predictive of late Grade 2 rectal toxicity.

Background and purpose

Significant dose deviations have been reported between planned (D_P) and accumulated (D_A) dose in prostate radiotherapy. This study aimed to develop multivariate analysis (MVA) models associating Grade 1 and 2 gastrointestinal (GI) toxicity with clinical and D_P or D_A dosimetric variables separately.

Materials and methods

Dose volume (DV) metrics were compared between D_A and D_P for 150 high-risk prostate cancer patients. MV models were generated from significant clinical and dosimetric variables (p < 0.05) at univariate level. Dose-based-region of interest (DB-ROI) metrics were included. Model performance was measured, and additional subgroup analysis were performed.

Results

Rectal D_A demonstrated a higher intermediate-high dose (V_{30-65 Gy} and DB-ROI at 15–50 mm) compared to D_P. Conversely, at the very high dose region, rectal D_A (V_{75 Gy} and DB-ROI at 5–10 mm) were significantly lower. In MVA, rectal DB-ROI at 10 mm was predictive for Grade ≥ 1 GI toxicity for D_A and D_P. Age, rectal D_A for D_{0.03 cc}, and rectal D_P for DB-ROI 10 mm were predictors for Grade 2 GI toxicity. Subgroup analysis revealed that patients ≥ 72 years old and a rectal D_A of ≥ 78.2 Gy were highly predictive of Grade 2 GI toxicity.

Conclusions

The dosimetric impact of a higher dose rectal dose in D_A due to volumetric changes was minimal and was not predictive of detrimental clinical toxicity apart from rectal D_{0.03 cc} ≥ 78.2 Gy for Grade 2 GI toxicity. The use of the DB-ROI method can provide equivalent predictive power as the DV method in toxicity prediction.

Explaining the dosimetric impact of contouring errors in head and neck radiotherapy

Objective. Auto-contouring of organs at risk (OAR) is becoming more common in radiotherapy. An important issue in clinical decision making is judging the quality of the auto-contours. While recent studies considered contour quality by looking at geometric errors only, this does not capture the dosimetric impact of the errors. In this work, we studied the relationship between geometrical errors, the local dose and the dosimetric impact of the geometrical errors.Approach. For 94 head and neck patients, unmodified atlas-based auto-contours and clinically used delineations of the parotid glands and brainstem were retrieved. VMAT plans were automatically optimized on the auto-contours and evaluated on both contours. We defined the dosimetric impact on evaluation (DIE) as the difference in the dosimetric parameter of interest between the two contours. We developed three linear regression models to predict the DIE using: (1) global geometric metrics, (2) global dosimetric metrics, (3) combined local geometric and dosimetric metrics. For model (3), we next determined the minimal amount of editing information required to produce a reliable prediction. Performance was assessed by the root mean squared error (RMSE) of the predicted DIE using 5-fold cross-validation.Main results. In model (3), the median RMSE of the left parotid was 0.4 Gy using 5% of the largest editing vectors. For the right parotid and brainstem the results were 0.5 Gy using 10% and 0.4 Gy using 1% respectively. The median RMS of the DIE was 0.6 Gy, 0.7 Gy and 0.9 Gy for the left parotid, the right parotid and the brainstem, respectively. Model (3), combining local dosimetric and geometric quantities, outperformed the models that used only geometric or dosimetric information.Significance. We showed that the largest local errors plus the local dose suffice to accurately predict the dosimetric impact, opening the door to automated dosimetric QA of auto-contours.

Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk

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Dice score and Hausdorff distance do not correlate with dose-volume-based results.

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Auto-contours close to the tumor or in entry/exit beams should be checked.

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Heart and esophagus must be checked for locally advanced non-small cell lung cancer.

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Bronchi must be checked for peripheral early-stage non-small cell lung cancer.

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Every treatment plan still passed the clinical goals for the manual organs at risk.

Background and purpose

The geometrical accuracy of auto-segmentation using convolutional neural networks (CNNs) has been demonstrated. This study aimed to investigate the dose-volume impact of differences between automatic and manual OARs for locally advanced (LA) and peripherally located early-stage (ES) non-small cell lung cancer (NSCLC).

Material and methods

A single CNN was created for automatic delineation of the heart, lungs, main left and right bronchus, esophagus, spinal cord and trachea using 55/10/40 patients for training/validation/testing. Dice score coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used for geometrical analysis. A new treatment plan based on the auto-segmented OARs was created for each test patient using 3D for ES-NSCLC (SBRT, 3–8 fractions) and IMRT for LA-NSCLC (24–35 fractions). The correlation between geometrical metrics and dose-volume differences was investigated.

Results

The average (±1 SD) DSC and HD95 were 0.82 ± 0.07 and 16.2 ± 22.4 mm, while the average dose-volume differences were 0.5 ± 1.5 Gy (ES) and 1.5 ± 2.8 Gy (LA). The geometrical metrics did not correlate with the observed dose-volume differences (average Pearson for DSC: −0.27 ± 0.18 (ES) and −0.09 ± 0.12 (LA); HD95: 0.1 ± 0.3 mm (ES) and 0.2 ± 0.2 mm (LA)).

Conclusions

After post-processing, manual adjustments of automatic contours are only needed for clinically relevant OARs situated close to the tumor or within an entry or exit beam e.g., the heart and the esophagus for LA-NSCLC and the bronchi for ES-NSCLC. The lungs do not need to be checked further in detail.

Skin Surface Dose for Whole Breast Radiotherapy Using Personalized Breast Holder: Comparison with Various Radiotherapy Techniques and Clinical Experiences

Purpose: Breast immobilization with personalized breast holder (PERSBRA) is a promising approach for normal organ protection during whole breast radiotherapy. The aim of this study is to evaluate the skin surface dose for breast radiotherapy with PERSBRA using different radiotherapy techniques. Materials and methods: We designed PERSBRA with three different mesh sizes (large, fine and solid) and applied them on an anthropomorphic(Rando) phantom. Treatment planning was generated using hybrid, intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques to deliver a prescribed dose of 5000 cGy in 25 fractions accordingly. Dose measurement with EBT3 film and TLD were taken on Rando phantom without PERSBRA, large mesh, fine mesh and solid PERSBRA for (a) tumor doses, (b) surface doses for medial field and lateral field irradiation undergoing hybrid, IMRT, VMAT techniques. Results: The tumor dose deviation was less than five percent between the measured doses of the EBT3 film and the TLD among the different techniques. The application of a PERSBRA was associated with a higher dose of the skin surface. A large mesh size of PERSBRA was associated with a lower surface dose. The findings were consistent among hybrid, IMRT, or VMAT techniques. Conclusions: Breast immobilization with PERSBRA can reduce heart toxicity but leads to a build-up of skin surface doses, which can be improved with a larger mesh design for common radiotherapy techniques.

The Cognitive Effects of Radiotherapy for Brain Metastases

Brain metastases are the most common intracranial neoplasm and are seen in upwards of 10-30% of patients with cancer. For decades, whole brain radiation therapy (WBRT) was the mainstay of treatment in these patients. While WBRT is associated with excellent rates of intracranial tumor control, studies have demonstrated a lack of survival benefit, and WBRT is associated with higher rates of cognitive deterioration and detrimental effects on quality of life. In recent years, strategies to mitigate this risk, such as the incorporation of memantine and hippocampal avoidance have been employed with improved results. Furthermore, stereotactic radiosurgery (SRS) has emerged as an appealing treatment option over the last decade in the management of brain metastases and is associated with superior cognitive preservation and quality of life when compared to WBRT. This review article evaluates the pathogenesis and impact of cranial irradiation on cognition in patients with brain metastases, as well as current and future risk mitigation techniques.

Challenges and Practical Solutions to MRI and Histology Matching and Measurements Using Available ImageJ Software Tools

Traditionally histology is the gold standard for the validation of imaging experiments. Matching imaging slices and histological sections and the precise outlining of corresponding tissue structures are difficult. Challenges are based on differences in imaging and histological slice thickness as well as tissue shrinkage and alterations after processing. Here we describe step-by-step instructions that might be used as a universal pathway to overlay MRI and histological images and for a correlation of measurements between imaging modalities. The free available (Fiji is just) ImageJ software tools were used for regions of interest transformation (ROIT) and alignment using a rat brain MRI as an example. The developed ROIT procedure was compared to a manual delineation of rat brain structures. The ROIT plugin was developed for ImageJ to enable an automatization of the image processing and structural analysis of the rodent brain.

Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT

Purpose

To determine the dosimetric impact of using unedited autocontours in daily plan adaptation of patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy using tumor tracking.

Materials and Methods

The study included 98 daily CT scans of 35 LAPC patients. All scans were manually contoured (MAN), and included the PTV and main organs-at-risk (OAR): stomach, duodenum and bowel. Precision and MIM deformable image registration (DIR) methods followed by contour propagation were used to generate autocontour sets on the daily CT scans. Autocontours remained unedited, and were compared to MAN on the whole organs and at 3, 1 and 0.5 cm from the PTV. Manual and autocontoured OAR were used to generate daily plans using the VOLO™ optimizer, and were compared to non-adapted plans. Resulting planned doses were compared based on PTV coverage and OAR dose-constraints.

Results

Overall, both algorithms reported a high agreement between unclipped MAN and autocontours, but showed worse results when being evaluated on the clipped structures at 1 cm and 0.5 cm from the PTV. Replanning with unedited autocontours resulted in better OAR sparing than non-adapted plans for 95% and 84% plans optimized using Precision and MIM autocontours, respectively, and obeyed OAR constraints in 64% and 56% of replans.

Conclusion

For the majority of fractions, manual correction of autocontours could be avoided or be limited to the region closest to the PTV. This practice could further reduce the overall timings of adaptive radiotherapy workflows for patients with LAPC.

Dosimetric Effect of Injection Ports in Tissue Expanders on Post-Mastectomy Volumetric Modulated Arc Therapy (VMAT) Planning for Left-Sided Breast Cancer

This study aimed to compare the dosimetric effect of traditional metallic ports and radio frequency identification (RFID) ports (Motiva Flora®) on post-mastectomy volumetric modulated arc therapy (VMAT) planning for left-sided breast cancer. Computed tomography (CT) simulation was performed on an anthropomorphic torso phantom by attaching two types of tissue expander on the left chest wall. For the comparison of CT artifacts, five points of interest were selected and compared: point A = central chest wall, B = medial chest wall, point C = lateral chest wall, point D = axilla, and point E = left anterior descending artery. VMAT planning using two partial arcs with a single isocenter was generated, and dosimetric parameters were investigated. Compared to metallic ports, RFID ports tremendously decreased distortion on CT images, with the exception of point D. The V5Gy, V10Gy, V15Gy, V20Gy, V30Gy, and Dmean values of the heart in RFID ports were lower than those in metallic ports. The V5Gy, V15Gy, V20Gy, V30Gy, and Dmean values of the ipsilateral lung in RFID ports were also lower than those in metallic ports. RFID ports showed superior dosimetric results for doses to the heart and lungs as compared to that in metallic ports.

Target Definition in MR-Guided Adaptive Radiotherapy for Head and Neck Cancer

Simple Summary

Adaptive radiotherapy for head and neck cancer has become more routine due to an increase in imaging quality and improvement in radiation techniques. With the availability of faster adaptive workflows, it is possible to adapt more easily to (daily) changes. MRI offers besides great anatomical imaging, also functional information about the tumor and surrounding tissue. The aim of this review is to provide current state of evidence about target definition on MRI for adaptive strategies in the treatment of head and neck cancer.

Abstract

In recent years, MRI-guided radiotherapy (MRgRT) has taken an increasingly important position in image-guided radiotherapy (IGRT). Magnetic resonance imaging (MRI) offers superior soft tissue contrast in anatomical imaging compared to computed tomography (CT), but also provides functional and dynamic information with selected sequences. Due to these benefits, in current clinical practice, MRI is already used for target delineation and response assessment in patients with head and neck squamous cell carcinoma (HNSCC). Because of the close proximity of target areas and radiosensitive organs at risk (OARs) during HNSCC treatment, MRgRT could provide a more accurate treatment in which OARs receive less radiation dose. With the introduction of several new radiotherapy techniques (i.e., adaptive MRgRT, proton therapy, adaptive cone beam computed tomography (CBCT) RT, (daily) adaptive radiotherapy ensures radiation dose is accurately delivered to the target areas. With the integration of a daily adaptive workflow, interfraction changes have become visible, which allows regular and fast adaptation of target areas. In proton therapy, adaptation is even more important in order to obtain high quality dosimetry, due to its susceptibility for density differences in relation to the range uncertainty of the protons. The question is which adaptations during radiotherapy treatment are oncology safe and at the same time provide better sparing of OARs. For an optimal use of all these new tools there is an urgent need for an update of the target definitions in case of adaptive treatment for HNSCC. This review will provide current state of evidence regarding adaptive target definition using MR during radiotherapy for HNSCC. Additionally, future perspectives for adaptive MR-guided radiotherapy will be discussed.

Functional Liver Imaging in Radiotherapy for Liver Cancer: A Systematic Review and Meta-Analysis

Backgrounds

Functional liver imaging can identify functional liver distribution heterogeneity and integrate it into radiotherapy planning. The feasibility and clinical benefit of functional liver-sparing radiotherapy planning are currently unknown.

Methods

A comprehensive search of several primary databases was performed to identify studies that met the inclusion criteria. The primary objective of this study was to evaluate the dosimetric and clinical benefits of functional liver-sparing planning radiotherapy. Secondary objectives were to assess the ability of functional imaging to predict the risk of radiation-induced liver toxicity (RILT), and the dose-response relationship after radiotherapy.

Results

A total of 20 publications were enrolled in descriptive tables and meta-analysis. The meta-analysis found that mean functional liver dose (f-MLD) was reduced by 1.0 Gy [95%CI: (-0.13, 2.13)], standard mean differences (SMD) of functional liver volume receiving ≥20 Gy (fV20) decreased by 0.25 [95%CI: (-0.14, 0.65)] when planning was optimized to sparing functional liver (P >0.05). Seven clinical prospective studies reported functional liver-sparing planning-guided radiotherapy leads to a low incidence of RILD, and the single rate meta-analysis showed that the RILD (defined as CTP score increase ≥2) incidence was 0.04 [95%CI: (0.00, 0.11), P <0.05]. Four studies showed that functional liver imaging had a higher value to predict RILT than conventional anatomical CT. Four studies established dose-response relationships in functional liver imaging after radiotherapy.

Conclusion

Although functional imaging modalities and definitions are heterogeneous between studies, but incorporation into radiotherapy procedures for liver cancer patients may provide clinical benefits. Further validation in randomized clinical trials will be required in the future.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PURPOSE

Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus.

METHODS

A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes.

RESULTS

A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness.

CONCLUSION

Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Proton Therapy in the Management of Luminal Gastrointestinal Cancers: Esophagus, Stomach, and Anorectum

Simple Summary

Radiation treatment plays a major role in the management of luminal gastrointestinal cancers, mainly esophageal and anorectal cancers. There is a growing interest in the application of protons for gastrointestinal cancers, mainly owing to its dosimetric characteristics in decreasing dose to nearby organs at risk. We present here an up-to-date comprehensive review of the dosimetric and clinical literature on the use of proton therapy in the management of luminal gastrointestinal cancers.

Abstract

While the role of proton therapy in gastric cancer is marginal, its role in esophageal and anorectal cancers is expanding. In esophageal cancer, protons are superior in sparing the organs at risk, as shown by multiple dosimetric studies. Literature is conflicting regarding clinical significance, but the preponderance of evidence suggests that protons yield similar or improved oncologic outcomes to photons at a decreased toxicity cost. Similarly, protons have improved sparing of the organs at risk in anorectal cancers, but clinical data is much more limited to date, and toxicity benefits have not yet been shown clinically. Large, randomized trials are currently underway for both disease sites.

Proton Therapy in the Management of Pancreatic Cancer

Simple Summary

Radiation treatment plays a pivotal a role in the management of pancreatic cancer, mainly in the neoadjuvant setting for borderline resectable tumors and in the definitive setting for unresectable localized disease. Most of the studies on pancreatic cancer use photon-based radiation therapy modalities. However, there is a growing interest in the application of protons therapy for gastrointestinal cancers. This review summarizes the literature on the use of proton therapy in the management of pancreatic cancer.

Abstract

Radiation therapy plays a central role in the treatment of pancreatic cancer. While generally shown to be feasible, proton irradiation, particularly when an ablative dose is planned, remains a challenge, especially due to tumor motion and the proximity to organs at risk, like the stomach, duodenum, and bowel. Clinically, standard doses of proton radiation treatment have not been shown to be statistically different from photon radiation treatment in terms of oncologic outcomes and toxicity rates as per non-randomized comparative studies. Fractionation schedules and concurrent chemotherapy combinations are yet to be optimized for proton therapy and are the subject of ongoing trials.

ESTRO-ACROP guideline on surface guided radiation therapy

Surface guidance systems enable patient positioning and motion monitoring without using ionising radiation. Surface Guided Radiation Therapy (SGRT) has therefore been widely adopted in radiation therapy in recent years, but guidelines on workflows and specific quality assurance (QA) are lacking. This ESTRO-ACROP guideline aims to give recommendations concerning SGRT roles and responsibilities and highlights common challenges and potential errors. Comprehensive guidelines for procurement, acceptance, commissioning, and QA of SGRT systems installed on computed tomography (CT) simulators, C-arm linacs, closed-bore linacs, and particle therapy treatment systems are presented that will help move to a consensus among SGRT users and facilitate a safe and efficient implementation and clinical application of SGRT.

The Promise of Magnetic Resonance Imaging in Radiation Oncology Practice in the Management of Brain, Prostate, and GI Malignancies

Magnetic resonance imaging (MRI) has a key role to play at multiple steps of the radiotherapy (RT) treatment planning and delivery process. Development of high-precision RT techniques such as intensity-modulated RT, stereotactic ablative RT, and particle beam therapy has enabled oncologists to escalate RT dose to the target while restricting doses to organs at risk (OAR). MRI plays a critical role in target volume delineation in various disease sites, thus ensuring that these high-precision techniques can be safely implemented. Accurate identification of gross disease has also enabled selective dose escalation as a means to widen the therapeutic index. Morphological and functional MRI sequences have also facilitated an understanding of temporal changes in target volumes and OAR during a course of RT, allowing for midtreatment volumetric and biological adaptation. The latest advancement in linear accelerator technology has led to the incorporation of an MRI scanner in the treatment unit. MRI-guided RT provides the opportunity for MRI-only workflow along with online adaptation for either target or OAR or both. MRI plays a key role in post-treatment response evaluation and is an important tool for guiding decision making. In this review, we briefly discuss the RT-related applications of MRI in the management of brain, prostate, and GI malignancies.

Case Report: Treatment Planning Study to Demonstrate Feasibility of Transthoracic Ultrasound Guidance to Facilitate Ventricular Tachycardia Ablation With Protons

Background

Cardiac arrhythmias, such as ventricular tachycardia, are disruptions in the normal cardiac function that originate from problems in the electrical conduction of signals inside the heart. Recently, a non-invasive treatment option based on external photon or proton beam irradiation has been used to ablate the arrhythmogenic structures. Especially in proton therapy, based on its steep dose gradient, it is crucial to monitor the motion of the heart in order to ensure that the radiation dose is delivered to the correct location. Transthoracic ultrasound imaging has the potential to provide guidance during this treatment delivery. However, it has to be noted that the presence of an ultrasound probe on the chest of the patient introduces constraints on usable beam angles for both protons and photon treatments. This case report investigates the possibility to generate a clinically acceptable proton treatment plan while the ultrasound probe is present on the chest of the patient.

Case

A treatment plan study was performed based on a 4D cardiac-gated computed tomography scan of a 55 year-old male patient suffering from refractory ventricular tachycardia who underwent cardiac radioablation. A proton therapy treatment plan was generated for the actual treatment target in presence of an ultrasound probe on the chest of this patient. The clinical acceptability of the generated plan was confirmed by evaluating standard target dose-volume metrics, dose to organs-at-risk and target dose conformity and homogeneity.

Conclusion

The generation of a clinically acceptable proton therapy treatment plan for cardiac radioablation of ventricular tachycardia could be performed in the presence of an ultrasound probe on the chest of the patient. These results establish a basis and justification for continued research and product development for ultrasound-guided cardiac radioablation.

Validation of accordance of ArcCHECK diode detector output with Monte Carlo simulation in brachytherapy

There are several accepted methods to verify External Beam Radiation Therapy (EBRT) treatment plans, but there is no standard way to check the quality of a brachytherapy treatment plan.

PURPOSE

This feasibility study assesses whether the ArcCHECK EBRT radiation detector can also be used to verify Treatment Planning System software quality check procedures for brachytherapy.

METHODS AND MATERIALS

ArcCHECK is a three-dimensional matrix of 1386 semiconductor diodes, arranged spirally around an internal cylindrical space that is 32 cm long and 15 cm in diameter. The detector makes it possible to reproduce the distribution of sources in a planned EBRT procedure (energy range 6-22 MeV) using an appropriate phantom. Detector responses are displayed as a two-dimensional dose distribution map on the diode surface. In this pilot brachytherapy study, we determined values that characterized the output of the detectors to a simulated Ir-192 radiation source with an energy range of approximately 9-1378 keV, and compared this to the actual signal recorded by an ArcCHECK detector. Experimental treatment plan measurement was performed using a standard Elekta micro-Selectron-v2 unit equipped with an iridium-192 source. To avoid unit inconsistencies, the signal from each of the diodes and the simulation results were normalized to the maximum value, with similar statistical parameters.

RESULTS

The difference between diode indications in the simulation and the actual measurement was analyzed statistically to show the degree of general inconsistency between them. The average difference for diode pairs here is equal 1,07%, with standard deviation 3, 95%.

CONCLUSION

The results obtained represent the first quantitative evidence of potential usefulness of the ArcCHECK detector in brachytherapy Treatment Planning System software QC verification.

Accumulated bladder wall dose is correlated with patient-reported acute urinary toxicity in prostate cancer patients treated with stereotactic, daily adaptive MR-guided radiotherapy

BACKGROUND AND PURPOSE

Magnetic resonance (MR)-guided linear accelerators (MR-Linac) enable accurate estimation of delivered doses through dose accumulation using daily MR images and treatment plans. We aimed to assess the association between the accumulated bladder (wall) dose and patient-reported acute urinary toxicity in prostate cancer (PCa) patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

One-hundred-and-thirty PCa patients treated on a 1.5T MR-Linac were included. Patients filled out International Prostate Symptom Scores (IPSS) questionnaires at baseline, 1 month, and 3 months post-treatment. Deformable image registration-based dose accumulation was performed to reconstruct the delivered dose. Dose parameters for both bladder and bladder wall were correlated with a clinically relevant increase in IPSS (≥10 points) and/or start of alpha-blockers within 3 months using logistic regression.

RESULTS

Thirty-nine patients (30%) experienced a clinically relevant IPSS increase and/or started with alpha-blockers. Bladder D5cm³, V_10-35Gy (in %), and D_mean and Bladder wall V_10-35Gy (cm³ and %) and D_mean were correlated with the outcome (odds ratios 1.04-1.33, p-values 0.001-0.044). Corrected for baseline characteristics, bladder V_10-35Gy (in %) and D_mean and bladder wall V_10-35Gy (cm³ and %) and D_mean were still correlated with the outcome (odds ratios 1.04-1.30, p-values 0.001-0.028). Bladder wall parameters generally showed larger AUC values.

CONCLUSION

This is the first study to assess the correlation between accumulated bladder wall dose and patient-reported urinary toxicity in PCa patients treated with MR-guided SBRT. The dose to the bladder wall is a promising parameter for prediction of patient-reported urinary toxicity and therefore warrants prospective validation and consideration in treatment planning.

Compact bunker shielding assessment for 1.5 T MR-Linac

This study evaluated the effect of the 1.5 T magnetic field of the magnetic resonance-guided linear accelerator (MR-Linac) on the radiation leakage doses penetrating the bunker radiation shielding wall. The evaluated 1.5 T MR-Linac Unity system has a bunker of the minimum recommended size. Unlike a conventional Linac, both primary beam transmission and secondary beam leakage were considered independently in the design and defined at the machine boundary away from the isocenter. Moreover, additional shielding was designed considering the numerous ducts between the treatment room and other rooms. The Linac shielding was evaluated by measuring the leakage doses at several locations. The intrinsic vibration and magnetic field were inspected at the proposed isocenter of the system. For verification, leakage doses were measured before and after applying the magnetic field. The intrinsic vibration and magnetic field readings were below the permitted limit. The leakage dose (0.05–12.2 µSv/week) also complied with internationally stipulated limits. The special shielding achieved a five-fold reduction in leakage dose. Applying the magnetic field increased the leakage dose by 0.12 to 4.56 µSv/week in several measurement points, although these values fall within experimental uncertainty. Thus, the effect of the magnetic field on the leakage dose could not be ascertained.

Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT

OBJECTIVE

To quantify the clinical performance of a machine learning (ML) algorithm for organ-at-risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions.

METHODS

ML models were trained using multi-center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54 Gy in three fractions. Models were generated using a gradient-boosted regression tree algorithm using grid searching with fivefold cross-validation. Twenty patients not included in the training set were used to test OAR dose prediction performance, ten for each prescription. We also performed blinded re-planning based on OAR dose predictions but without access to clinically delivered plans. Differences between predicted and delivered doses were assessed by root-mean square deviation (RMSD), and statistical differences between predicted, delivered, and re-planned doses were evaluated with one-way analysis of variance (ANOVA) tests.

RESULTS

ANOVA tests showed no significant differences between predicted, delivered, and replanned OAR doses (all p ≥ 0.36). The RMSD was 2.9, 3.9, 4.3, and 1.7Gy for max dose to the spinal cord, great vessels, heart, and trachea, respectively, for 50 Gy in five fractions. Average improvements of 1.0, 1.4, and 2.0 Gy were seen for spinal cord, esophagus, and trachea max doses in blinded replans compared to clinically delivered plans with 54 Gy in three fractions, and 1.8, 0.7, and 1.5 Gy, respectively, for the esophagus, heart and bronchus max doses with 50 Gy in five fractions. Target coverage was similar with an average PTV V100% of 94.7% for delivered plans compared to 97.3% for blinded re-plans for 50 Gy in five fractions, and respectively 98.4% versus 99.2% for 54 Gy in three fractions.

CONCLUSION

This study validated ML-based OAR dose prediction for lung SBRT, showing potential for improved OAR dose sparing and more consistent plan quality using dose predictions for patient-specific planning guidance.

Technical note: Evaluation of Artificial 120-kilovolt computed tomography images for radiation therapy applications

PURPOSE

The purpose of this work is to evaluate the scaled CT number accuracy of an artificial 120 kV reconstruction technique based on phantom experiments in the context of radiation therapy planning.

METHODS

An abdomen-shaped electron density phantom was scanned on a clinical CT scanner capable of artificial 120 kV reconstruction using different tube potentials from 70 kV to 150 kV. A series of tissue equivalent phantom inserts (lung, adipose, breast, solid water, liver, inner bone, 30%/50% CaCO3, cortical bone) were placed inside the phantom. Images were reconstructed using a conventional quantitative reconstruction kernel as well as the artificial 120 kV reconstruction kernel. Scaled CT numbers of inserts were measured from images acquired at different kVs and compared with those acquired at 120 kV, which were deemed as the ground truth. The relative error was quantified as the percentage deviation of scaled CT numbers acquired at different tube potentials from their ground truth values acquired at 120 kV.

RESULTS

Scaled CT numbers measured from images reconstructed using the conventional reconstruction demonstrated a strong kV-dependence. The relative error in scaled CT number ranged from 0.6% (liver insert) to 31.1% (cortical bone insert). The artificial 120 kV reconstruction reduced the kV-dependence, especially for bone tissues. The relative error in scaled CT number was reduced to 0.4% (liver insert) and 2.6% (30% CaCO₃ insert) using this technique. When tube potential selection was limited to the range of 90 kV to 150 kV, the relative error was further restrained to <1.2% for all tissue types.

CONCLUSION

Phantom results demonstrated that using the artificial 120 kV technique, it was feasible to acquire raw projection data at a desired tube potential and then reconstruct images with scaled CT numbers comparable to those obtained directly at 120 kV. In radiotherapy applications, this technique may allow optimization of tube potential without complicating clinical workflow by eliminating the necessity of maintaining multiple sets of CT calibration curves. This article is protected by copyright. All rights reserved.

Experimental investigation of dynamic real-time rotation-including dose reconstruction during prostate tracking radiotherapy

BACKGROUND

Hypofractionation in prostate radiotherapy is of increasing interest. Steep dose gradients and a large weight on each individual fraction emphasize the need for motion management. Real-time motion management techniques such as multi-leaf collimator (MLC) tracking or couch tracking typically adjust for translational motion while rotations remain uncompensated with unknown dosimetric impact.

PURPOSE

The purpose of this study is to demonstrate and validate dynamic real-time rotation-including dose reconstruction during radiotherapy experiments with and without MLC and couch tracking.

METHODS

Real-time dose reconstruction was performed using the in-house developed software DoseTracker. DoseTracker receives streamed target positions and accelerator parameters during treatment delivery and uses a pencil beam algorithm with water density assumption to reconstruct the dose in a moving target. DoseTracker's ability to reconstruct motion-induced dose errors in a dynamically rotating and translating target was investigated during three different scenarios: (1) no motion compensation and translational motion correction with (2) MLC tracking and (3) couch tracking. In each scenario, dose reconstruction was performed online and in real-time during delivery of two dual-arc volumetric modulated arc therapy (VMAT) prostate plans with a prescribed fraction dose of 7 Gy to the prostate and simultaneous intraprostatic lesion boosts with doses of at least 8 Gy, but up to 10 Gy as long as the organs-at-risk dose constraints were fulfilled. The plans were delivered to a pelvis phantom that replicated three patient-measured motion traces using a rotational insert with 21 layers of EBT3 film spaced 2.5 mm apart. DoseTracker repeatedly calculated the actual motion-including dose increment and the planned static dose increment since the last calculation in 84500 points in the film stack. The experiments were performed with a TrueBeam accelerator with MLC and couch tracking based on electromagnetic transponders embedded in the film stack. The motion-induced dose error was quantified as the difference between the final cumulative dose with motion and without motion using the 2D 2%/2mm γ-failure rate and the difference in dose to 95% of the clinical target volume (CTV ΔD_95% ) and the gross target volume (GTV ΔD_95% ) as well as the difference in dose to 0.1 cm³ of the urethra, bladder, and rectum (ΔD_0.1CC ). The motion-induced errors were compared between dose reconstructions and film measurements.

RESULTS

The dose was reconstructed in all calculation points at a mean frequency of 4.7 Hz. The root-mean-square difference between real-time reconstructed and film measured motion-induced errors was 3.1%-points (γ-failure rate), 0.13 Gy (CTV ΔD_95% ), 0.23 Gy (GTV ΔD_95% ), 0.19 Gy (urethra ΔD_0.1CC ), 0.09 Gy (bladder ΔD_0.1CC ), and 0.07 Gy (rectum ΔD_0.1CC ).

CONCLUSIONS

In a series of phantom experiments, online real-time rotation-including dose reconstruction was performed for the first time. The calculated motion-induced errors agreed well with film measurements. The dose reconstruction provides a valuable tool for monitoring dose delivery and investigating the efficacy of advanced motion-compensation techniques in the presence of translational and rotational motion. This article is protected by copyright. All rights reserved.

Versatile Detection and Monitoring of Ionizing Radiation Treatment Using Radiation-Responsive Gel Nanosensors

Modern radiation therapy workflow involves complex processes intended to maximize the radiation dose delivered to tumors while simultaneously minimizing excess radiation to normal tissues. Safe and accurate delivery of radiation doses is critical to the successful execution of these treatment plans and effective treatment outcomes. Given extensive differences in existing dosimeters, the choice of devices and technologies for detecting biologically relevant doses of radiation has to be made judiciously, taking into account anatomical considerations and modality of treatment (invasive, e.g., interstitial brachytherapy vs noninvasive, e.g., external-beam therapy radiotherapy). Rapid advances in versatile radiation delivery technologies necessitate new detection platforms and devices that are readily adaptable into a multitude of form factors in order to ensure precision and safety in dose delivery. Here, we demonstrate the adaptability of radiation-responsive gel nanosensors as a platform technology for detecting ionizing radiation using three different form factors with an eye toward versatile use in the clinic. In this approach, ionizing radiation results in the reduction of monovalent gold salts leading to the formation of gold nanoparticles within gels formulated in different morphologies including one-dimensional (1D) needles for interstitial brachytherapy, two-dimensional (2D) area inserts for skin brachytherapy, and three-dimensional (3D) volumetric dose distribution in tissue phantoms. The formation of gold nanoparticles can be detected using distinct but complementary modes of readout including optical (visual) and photothermal detection, which further enhances the versatility of this approach. A linear response in the readout was seen as a function of radiation dose, which enabled straightforward calibration of each of these devices for predicting unknown doses of therapeutic relevance. Taken together, these results indicate that the gel nanosensor technology can be used to detect ionizing radiation in different morphologies and using different detection methods for application in treatment planning, delivery, and verification in radiotherapy and in trauma care.

Dose escalation to hypoxic subvolumes in head and neck cancer: A randomized phase II study using dynamic [18F]FMISO PET/CT

BACKGROUND AND PURPOSE

Tumor hypoxia is a major cause of resistance to radiochemotherapy in locally advanced head-and-neck cancer (LASCCHN). We present results of a randomized phase II trial on hypoxia dose escalation (DE) in LASCCHN based on dynamic [¹⁸F]FMISO (dynFMISO) positron emission tomography (PET). The purpose was to confirm the prognostic value of hypoxia PET and assess feasibility, toxicity and efficacy of hypoxia-DE.

MATERIALS AND METHODS

Patients with LASCCHN underwent baseline dynFMISO PET/CT. Hypoxic volumes (HV) were derived from dynFMISO data. Patients with hypoxic tumors (HV>0) were randomized into standard radiotherapy (ST: 70Gy/35fx) or dose escalation (DE: 77Gy/35fx) to the HV. Patients with non-hypoxic tumors were treated with ST. After a minimum follow-up of 2 years, feasibility, acute/late toxicity and local control (LC) were analyzed.

RESULTS

The study was closed prematurely due to slow accrual. Between 2009 and 2017, 53 patients were enrolled, 39 (74%) had hypoxic tumors and were randomized into ST or DE. For non-hypoxic patients, 100% 5-year LC was observed compared to 74% in patients with hypoxic tumors (p=0.039). The difference in 5-year LC between DE (16/19) and ST (10/17) was 25%, p=0.150. No relevant differences related to acute and late toxicities between the groups were observed.

CONCLUSION

This study confirmed the prognostic value of hypoxia PET in LASCCHN for LC. Outcome after hypoxia DE appears promising and may support the concept of DE. Slow accrual and premature closure may partly be due to a high complexity of the study setup which needs to be considered for future multicenter trials.

A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry

Background and purpose

Real-time portal dosimetry compares measured images with predicted images to detect delivery errors as the radiotherapy treatment proceeds. This work aimed to investigate the performance of a recurrent neural network for processing image metrics so as to detect delivery errors as early as possible in the treatment.

Materials and methods

Volumetric modulated arc therapy (VMAT) plans of six prostate patients were used to generate sequences of predicted portal images. Errors were introduced into the treatment plans and the modified plans were delivered to a water-equivalent phantom. Four different metrics were used to detect errors. These metrics were applied to a threshold-based method to detect the errors as soon as possible during the delivery, and also to a recurrent neural network consisting of four layers. A leave-two-out approach was used to set thresholds and train the neural network then test the resulting systems.

Results

When using a combination of metrics in conjunction with optimal thresholds, the median segment index at which the errors were detected was 107 out of 180. When using the neural network, the median segment index for error detection was 66 out of 180, with no false positives. The neural network reduced the rate of false negative results from 0.36 to 0.24.

Conclusions

The recurrent neural network allowed the detection of errors around 30% earlier than when using conventional threshold techniques. By appropriate training of the network, false positive alerts could be prevented, thereby avoiding unnecessary disruption to the patient workflow.

Developing quality assurance tests for simultaneous Positron Emission Tomography – Magnetic Resonance imaging for radiotherapy planning

Background and purpose Simultaneous Positron Emission Tomography – Magnetic Resonance (PET-MR) imaging can potentially improve radiotherapy by enabling more accurate tumour delineation and dose painting. The use of PET-MR imaging for radiotherapy planning requires a comprehensive Quality Assurance (QA) programme to be developed. This study aimed to develop the QA tests required and assess their repeatability and stability. Materials and methods QA tests were developed for: MR image quality, MR geometric accuracy, electromechanical accuracy, PET-MR alignment accuracy, Diffusion Weighted (DW)-MR Apparent Diffusion Coefficient (ADC) accuracy and PET Standard Uptake Value (SUV) accuracy. Each test used a dedicated phantom and was analysed automatically or semi-automatically, with in–house software. Repeatability was evaluated by three same-day measurements with independent phantom positions. Stability was assessed through 12 monthly measurements. Results The repeatability Standard Deviations (SDs) of distortion for the MR geometric accuracy test were ⩽0.7mm. The repeatability SDs in ADC difference from reference were ⩽3% for the DW-MR accuracy test. The PET SUV difference from reference repeatability SD was 0.3%. The stability SDs agreed within 0.6mm, 1 percentage point and 1.4 percentage points of the repeatability SDs for the geometric, ADC and SUV accuracy tests respectively. There were no monthly trends apparent. These results were representative of the other tests. Conclusions QA Tests for radiotherapy planning PET-MR have been developed. The tests appeared repeatable and stable over a 12-month period. The developed QA tests could form the basis of a QA programme that enables high-quality, robust PET-MR imaging for radiotherapy planning.