Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels.

Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with 90Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Materials and Methods: Two tissue-specific DVKs for 90Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations. A method was developed to produce a 3D dose map by convolving the calculated DVKs with the activity biodistribution derived from clinical single-photon emission computed tomography (SPECT) or positron emission tomography (PET) images. Image registration for the SPECT or PET images with the corresponding computed tomography scans was performed before dosimetry calculation. The authors first compared the DVK convolution dosimetry with a direct full MC simulation on an XCAT anthropomorphic phantom. They then tested it in 25 individual clinical cases of patients who underwent 90Y therapy. All MC simulations were carried out using the GATE MC toolkit. Results: Comparison of the measured absorbed dose using tissue-specific DVKs and direct MC simulation on 25 patients revealed a mean difference of 1.07% ± 1.43% for the liver and 1.03% ± 1.21% for the tumor tissue, respectively. The largest difference between DVK convolution and full MC dosimetry was observed for the lung tissue (10.16% ± 1.20%). The DVK statistical uncertainty was <0.75% for both media. Conclusions: This semiautomatic algorithm is capable of performing rapid, accurate, and efficient 3D dosimetry. The proposed method considers tissue and activity heterogeneity using tissue-specific DVKs. Furthermore, this method provides results in <1 min, making it suitable for everyday clinical practice.

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists. Although many studies have been published validating the SWE technique either in a clinical setting, or by applying machine learning on SWE elastograms, minimal work has been done on comparing the performance of popular pre-trained deep learning networks on CLD assessment. Currently available literature reports suggest technical advancements on specific deep learning structures, with specific inputs and usually on a limited CLD fibrosis stage class group, with limited comparison on competitive deep learning schemes fed with different input types. The aim of the present study is to compare some popular deep learning pre-trained networks using temporally stable and full elastograms, with or without augmentation as well as propose suitable deep learning schemes for CLD diagnosis and progress assessment. 200 liver biopsy validated patients with CLD, underwent US SWE examination. Four images from the same liver area were saved to extract elastograms and processed to exclude areas that were temporally unstable. Then, full and temporally stable masked elastograms for each patient were separately fed into GoogLeNet, AlexNet, VGG16, ResNet50 and DenseNet201 with and without augmentation. The networks were tested for differentiation of CLD stages in seven classification schemes over 30 repetitions using liver biopsy as the reference. All networks achieved maximum mean accuracies ranging from 87.2%-97.4% and area under the receiver operating characteristic curves (AUCs) ranging from 0.979-0.990 while the radiologists had AUCs ranging from 0.800-0.870. ResNet50 and DenseNet201 had better average performance than the other networks. The use of the temporal stability mask led to improved performance on about 50% of inputs and network combinations while augmentation led to lower performance for all networks. These findings can provide potential networks with higher accuracy and better setting in the CLD diagnosis and progress assessment. A larger data set would help identify the best network and settings for CLD assessment in clinical practice.

Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization.

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy and the leading cause of death in patients with cirrhosis. Various treatments for HCC are available, including transarterial chemoembolization (TACE), which is the commonest intervention performed in HCC. Radiologic tumor response following TACE is an important prognostic factor for patients with HCC. We hypothesized that, for large HCC tumors, assessment of treatment response made with automated volumetric response evaluation criteria in solid tumors (RECIST) might correlate with the assessment made with the more time- and labor-intensive unidimensional modified RECIST (mRECIST) and manual volumetric RECIST (M-vRECIST) criteria. Accordingly, we undertook this retrospective study to compare automated volumetric RECIST (A-vRECIST) with M-vRECIST and mRESIST for the assessment of large HCC tumors' responses to TACE. Methods:We selected 42 pairs of contrast-enhanced computed tomography (CT) images of large HCCs. Images were taken before and after TACE, and in each of the images, the HCC was segmented using both a manual contouring tool and a convolutional neural network. Three experienced radiologists assessed tumor response to TACE using mRECIST criteria. The intra-class correlation coefficient was used to assess inter-reader reliability in the mRECIST measurements, while the Pearson correlation coefficient was used to assess correlation between the volumetric and mRECIST measurements. Results:Volumetric tumor assessment using automated and manual segmentation tools showed good correlation with mRECIST measurements. For A-vRECIST and M-vRECIST, respectively, r = 0.597 vs. 0.622 in the baseline studies; 0.648 vs. 0.748 in the follow-up studies; and 0.774 vs. 0.766 in the response assessment (P < 0.001 for all). The A-vRECIST evaluation showed high correlation with the M-vRECIST evaluation (r = 0.967, 0.937, and 0.826 in baseline studies, follow-up studies, and response assessment, respectively, P < 0.001 for all). Conclusion:Volumetric RECIST measurements are likely to provide an early marker for TACE monitoring, and automated measurements made with a convolutional neural network may be good substitutes for manual volumetric measurements.

Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study.

PURPOSE: This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib. METHODS: In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods. RESULTS: When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP. CONCLUSIONS: Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs). MATERIALS AND METHODS: Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap-to-stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c-means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison. RESULTS: The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists' measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high-stability areas of the masked SWE images (area-under-the-curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations. CONCLUSION: Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.

Technical Note: A Monte Carlo study of magnetic-field-induced radiation dose effects in mice.

PURPOSE: Magnetic fields are known to alter radiation dose deposition. Before patients receive treatment using an MRI-linear accelerator (MRI-Linac), preclinical studies are needed to understand the biological consequences of magnetic-field-induced dose effects. In the present study, the authors sought to identify a beam energy and magnetic field strength combination suitable for preclinical murine experiments. METHODS: Magnetic field dose effects were simulated in a mouse lung phantom using various beam energies (225 kVp, 350 kVp, 662 keV [Cs-137], 2 MV, and 1.25 MeV [Co-60]) and magnetic field strengths (0.75, 1.5, and 3 T). The resulting dose distributions were compared with those in a simulated human lung phantom irradiated with a 6 or 8 MV beam and orthogonal 1.5 T magnetic field. RESULTS: In the human lung phantom, the authors observed a dose increase of 45% and 54% at the soft-tissue-to-lung interface and a dose decrease of 41% and 48% at the lung-to-soft-tissue interface for the 6 and 8 MV beams, respectively. In the mouse simulations, the magnetic fields had no measurable effect on the 225 or 350 kVp dose distribution. The dose increases with the Cs-137 beam for the 0.75, 1.5, and 3 T magnetic fields were 9%, 29%, and 42%, respectively. The dose decreases were 9%, 21%, and 37%. For the 2 MV beam, the dose increases were 16%, 33%, and 31% and the dose decreases were 9%, 19%, and 30%. For the Co-60 beam, the dose increases were 19%, 54%, and 44%, and the dose decreases were 19%, 42%, and 40%. CONCLUSIONS: The magnetic field dose effects in the mouse phantom using a Cs-137, 3 T combination or a Co-60, 1.5 or 3 T combination most closely resemble those in simulated human treatments with a 6 MV, 1.5 T MRI-Linac. The effects with a Co-60, 1.5 T combination most closely resemble those in simulated human treatments with an 8 MV, 1.5 T MRI-Linac.

Prospective observer and software-based assessment of magnetic resonance imaging quality in head and neck cancer: Should standard positioning and immobilization be required for radiation therapy applications?

PURPOSE: The purpose of this study was to investigate the potential of a head and neck magnetic resonance simulation and immobilization protocol on reducing motion-induced artifacts and improving positional variance for radiation therapy applications. METHODS AND MATERIALS: Two groups (group 1, 17 patients; group 2, 14 patients) of patients with head and neck cancer were included under a prospective, institutional review board-approved protocol and signed informed consent. A 3.0-T magnetic resonance imaging (MRI) scanner was used for anatomic and dynamic contrast-enhanced acquisitions with standard diagnostic MRI setup for group 1 and radiation therapy immobilization devices for group 2 patients. The impact of magnetic resonance simulation/immobilization was evaluated qualitatively by 2 observers in terms of motion artifacts and positional reproducibility and quantitatively using 3-dimensional deformable registration to track intrascan maximum motion displacement of voxels inside 7 manually segmented regions of interest. RESULTS: The image quality of group 2 (29 examinations) was significantly better than that of group 1 (50 examinations) as rated by both observers in terms of motion minimization and imaging reproducibility (P < .0001). The greatest average maximum displacement was at the region of the larynx in the posterior direction for patients in group 1 (17 mm; standard deviation, 8.6 mm), whereas the smallest average maximum displacement was at the region of the posterior fossa in the superior direction for patients in group 2 (0.4 mm; standard deviation, 0.18 mm). Compared with group 1, maximum regional motion was reduced in group 2 patients in the oral cavity, floor of mouth, oropharynx, and larynx regions; however, the motion reduction reached statistical significance only in the regions of the oral cavity and floor of mouth (P < .0001). CONCLUSIONS: The image quality of head and neck MRI in terms of motion-related artifacts and positional reproducibility was greatly improved by use of radiation therapy immobilization devices. Consequently, immobilization with external and intraoral fixation in MRI examinations is required for radiation therapy application.

Magnetic resonance imaging assessment of a convective therapy delivery paradigm in a canine prostate model.

PURPOSE: To quantitatively investigate the feasibility of MRI as a tool for assessing the spatial distribution of a convectively delivered agent using a canine prostate model. MATERIALS AND METHODS: Canine prostates (ex vivo, n = 3; in vivo, n = 12) were injected under several injection paradigms with a solution of gadolinium-DTPA for MR contrast and methylene blue as a grossly visible surrogate drug marker. Ex vivo and in vivo distributions were assessed at 1.5T and quantitatively compared. RESULTS: Measured distributions using MRI and methylene blue pathology photographs were analyzed using a Bland-Altman method. The fractional percentage volume covered (V frac) compared the measurements grossly: Pearson's correlation coefficients were R = 0.99 for ex vivo and R = 0.77 for in vivo (P < 0.05). The fractional percentage of area covered (A frac) demonstrated the high degree of spatial correlation between individual slices: R = 0.93 for ex vivo and R = 0.98 for in vivo (P < 0.05). There was no statistically observable bias in scale or offset between the measurements. CONCLUSION: Measured distributions using MRI and pathology were highly correlated and unbiased, indicating the potential of MRI as a tool for quantitative assessment of interstitial delivery of injected therapies in vivo.