Early-onset osteoradionecrosis following adjuvant volumetric-modulated arc therapy to an osteocutaneous free fibula flap with customized titanium plate.

BACKGROUND: Computerized surgical planning (CSP) in osseous reconstruction of head and neck cancer defects has become a mainstay of treatment. However, the consequences of CSP-designed titanium plating systems on planning adjuvant radiation remains unclear. METHODS: Two patients underwent head and neck cancer resection and maxillomandibular free fibula flap reconstruction with CSP-designed plates and immediate placement of osseointegrated dental implants. Surgical treatment was followed by adjuvant intensity modulated radiation therapy (IMRT). RESULTS: Both patients developed osteoradionecrosis (ORN), and one patient had local recurrence. The locations of disease occurred at the areas of highest titanium plate burden, possibly attributed to IMRT dosing inaccuracy caused by the CSP-designed plating system. CONCLUSION: Despite proven benefits of CSP-designed plates in osseous free flap reconstruction, there may be an underreported risk to adjuvant IMRT treatment planning leading to ORN and/or local recurrence. Future study should investigate alternative plating methods and materials to mitigate this debilitating outcome.

Assessing the reproducibility of CBCT-derived radiomics features using a novel three-dimensional printed phantom.

PURPOSE: Radiomics modeling is an exciting avenue for enhancing clinical decision making and personalized treatment. Radiation oncology patients often undergo routine imaging for position verification, particularly using LINAC-mounted cone beam computed tomography (CBCT). The wealth of imaging data collected in modern radiation therapy presents an ideal use case for radiomics modeling. Despite this, texture feature (TF) calculation can be limited by concerns over feature stability and reproducibility; in theory, this issue is compounded by the relatively poor image quality of CBCT, as well as variation of acquisition and reconstruction parameters. METHODS: In this study, we developed and validated a novel three-dimensional (3D) printed phantom for evaluating CBCT-based TF reliability. The phantom has a cylindrical shape (22 cm diameter and 25.5 cm height) with five inner inserts designed to hold custom-printed rods (1 cm diameter and 10-20 cm height) of various materials, infill shapes, and densities. TF reproducibility was evaluated across and within three LINACs from a single vendor using sets of three consecutive CBCT taken with the head, thorax, and pelvis clinical imaging protocols. PyRadiomics was used to extract a standard set of TFs from regions of interest centered on each rod. Two-way mixed effects absolute agreement intra-class correlation coefficient (ICC) was used to evaluate TF reproducibility, with features showing ICC values above 0.9 considered robust if their Bonferroni-corrected p-value was below 0.05. RESULTS: A total of 63, 87, and 83 features exhibited test-retest reliability for the head, thorax, and pelvis imaging protocols respectively. When assessing stability between discreet imaging sessions on the same LINAC, these numbers were reduced to 5, 63, and 70 features, respectively. The thorax and pelvis protocols maintained a rich candidate feature space in inter-LINAC analysis with 61 and 65 features, respectively, exceeding the ICC criteria. Crucially, no features were deemed reproducible when compared between protocols. CONCLUSIONS: We have developed a 3D phantom for consistent evaluation of TF stability and reproducibility. For LINACs from a single vendor, our study found a substantial number of features available for robust radiomics modeling from CBCT imaging. However, some features showed variations across LINACs. Studies involving CBCT-based radiomics must preselect features prior to their use in clinical-based models.

Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments.

BACKGROUND: Recent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, and automated methods are not as reliable as manual segmentation. PURPOSE: To utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). STUDY TYPE: Retrospective. POPULATION: Eighty-seven total data sets from patients with schizophrenia and matched controls. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, T1 -weighted (SPGR SENSE, 3D BRAVO). ASSESSMENT: MTL-generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [11 C]raclopride. Data were reconstructed by filtered back projection (FBP) with computed tomography (CT) used for attenuation correction. Binding potential values, BPND , and region of interest (ROI) time series and whole-brain connectivity using functional magnetic resonance imaging (fMRI) images were compared between manual and both automated segmentations. STATISTICAL TESTS: Pearson correlation and paired t-test. RESULTS: MTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2 ≥ 0.91 in all caudate and putamen subregions, and R2  = 0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time series (≥0.86) and whole-brain connectivity (≥0.89) across all subregions. DATA CONCLUSION: Across both PET and fMRI task-based assessments, results from MTL-generated segmentations more closely corresponded to results from manually drawn ROIs than CIC-generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three-dimensional striatal subregion segmentation with results comparable to manually segmented ROIs. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Full-count PET recovery from low-count image using a dilated convolutional neural network.

PURPOSE: Positron emission tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using a novel dilated convolutional neural network (CNN) to recover full-count images from low-count images. METHODS: We adopted similar hierarchical structures as the conventional U-Net and incorporated dilated kernels in each convolution to allow the network to observe larger, more robust features within the image without the requirement of downsampling and upsampling internal representations. Our dNet was trained alongside a U-Net for comparison. Both models were evaluated using a leave-one-out cross-validation procedure on a dataset of 35 subjects (~3500 slabs), which were obtained from an ongoing 18 F-Fluorodeoxyglucose (FDG) study. Low-count PET data (10% count) were generated by randomly selecting one-tenth of all events in the associated listmode file. Analysis was done on the static image from the last 10 minutes of emission data. Both low-count PET and full-count PET were reconstructed using ordered subset expectation maximization (OSEM). Objective image quality metrics, including mean absolute percent error (MAPE), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM), were used to analyze the deep learning methods. Both deep learning methods were further compared to a traditional Gaussian filtering method. Further, region of interest (ROI) quantitative analysis was also used to compare U-Net and dNet architectures. RESULTS: Both the U-Net and our proposed network were successfully trained to synthesize full-count PET images from the generated low-count PET images. Compared to low-count PET and Gaussian filtering, both deep learning methods improved MAPE, PSNR, and SSIM. Our dNet also systematically outperformed U-Net on all three metrics (MAPE: 4.99 ± 0.68 vs 5.31 ± 0.76, P < 0.01; PSNR: 31.55 ± 1.31 dB vs 31.05 ± 1.39, P < 0.01; SSIM: 0.9513 ± 0.0154 vs 0.9447 ± 0.0178, P < 0.01). ROI quantification showed greater quantitative improvements using dNet over U-Net. CONCLUSION: This study proposed a novel approach of using dilated convolutions for recovering full-count PET images from low-count PET images.

The importance of identifying functional Val158Met polymorphism in catechol-O- Methyltransferase when assessing MRI-based volumetric measurements in major depressive disorder.

Many studies have shown volumetric differences in the hippocampus between COMT gene polymorphisms and other studies have shown differences between depressed patients and controls; yet, few studies have been completed to identify the volumetric differences when taking both factors into consideration. Using voxel-based morphology (VBM) we investigated, in major depressive disorder (MDD) patients and healthy controls, the relationship between COMT gene polymorphism and volumetric abnormalities. Data from 60 MDD patients and 25 healthy controls were included in this study. Volumetric measurements and genotyping of COMTval158met polymorphism were conducted to determine its impact on gray matter volume (GMV) in the hippocampus and amygdala using a Met dominant model (Val/Val vs Met/Val & Met/Met). In the analysis, a significant difference in the right hippocampus (p = 0.015), right amygdala (p = 0.003) and entire amygdala (p = 0.019) was found between the interaction of diagnosis and genotype after MRI scanner, age and sex correction. Healthy controls (HC) with the Met dominant genotype exhibited a larger right hippocampal, right amygdalar and entire amydgalar volume than MDD patients with the Met dominant genotype. Conversely, HC with the Val/Val genotype displayed a lower right hippocampal, right amygdalar and entire amygdalar volume than their MDD counterparts. This study shows that COMT polymorphism and depression may have a confounding effect on neuroimaging studies.

Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.

PURPOSE: Radiomics allows for powerful data-mining and feature extraction techniques to guide clinical decision making. Image segmentation is a necessary step in such pipelines and different techniques can significantly affect results. We demonstrate that a convolutional neural network (CNN) segmentation method performs comparably to expert manual segmentations in an established radiomics pipeline. METHODS: Using the manual regions of interest (ROIs) of an expert radiologist (R1), a CNN was trained to segment breast lesions from dynamic contrast-enhanced MRI (DCE-MRI). Following network training, we segmented lesions for the testing set of a previously established radiomics pipeline for predicting lymph node metastases using DCE-MRI of breast cancer. Prediction accuracy of CNN segmentations relative to manual segmentations by R1 from the original study, a resident (R2), and another expert radiologist (R3) were determined. We then retrained the CNN and radiomics model using R3's manual segmentations to determine the effects of different expert observers on end-to-end prediction. RESULTS: Using R1's ROIs, the CNN achieved a mean Dice coefficient of 0.71 ± 0.16 in the testing set. When input to our previously published radiomics pipeline, these CNN segmentations achieved comparable prediction performance to R1's manual ROIs, and superior performance to those of the other radiologists. Similar results were seen when training the CNN and radiomics model using R3's ROIs. CONCLUSION: A CNN architecture is able to provide DCE-MRI breast lesion segmentations which are suitable for input to our radiomics model. Moreover, the previously established radiomics model and CNN can be accurately trained end-to-end using ground truth data provided by distinct experts.

Merging Orthovoltage X-Ray Minibeams spare the proximal tissues while producing a solid beam at the target.

Conventional radiation therapy of brain tumors often produces cognitive deficits, particularly in children. We investigated the potential efficacy of merging Orthovoltage X-ray Minibeams (OXM). It segments the beam into an array of parallel, thin (~0.3 mm), planar beams, called minibeams, which are known from synchrotron x-ray experiments to spare tissues. Furthermore, the slight divergence of the OXM array make the individual minibeams gradually broaden, thus merging with their neighbors at a given tissue depth to produce a solid beam. In this way the proximal tissues, including the cerebral cortex, can be spared. Here we present experimental results with radiochromic films to characterize the method's dosimetry. Furthermore, we present our Monte Carlo simulation results for physical absorbed dose, and a first-order biologic model to predict tissue tolerance. In particular, a 220-kVp orthovoltage beam provides a 5-fold sharper lateral penumbra than a 6-MV x-ray beam. The method can be implemented in arc-scan, which may include volumetric-modulated arc therapy (VMAT). Finally, OXM's low beam energy makes it ideal for tumor-dose enhancement with contrast agents such as iodine or gold nanoparticles, and its low cost, portability, and small room-shielding requirements make it ideal for use in the low-and-middle-income countries.

Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network.

Attenuation correction is a notable challenge associated with simultaneous PET/MRI, particularly in neuroimaging, where sharp boundaries between air and bone volumes exist. This challenge leads to concerns about the visual and, more specifically, quantitative accuracy of PET reconstructions for data obtained with PET/MRI. Recently developed techniques can synthesize attenuation maps using only MRI data and are likely adequate for clinical use; however, little work has been conducted to assess their suitability for the dynamic PET studies frequently used in research to derive physiologic information such as the binding potential of neuroreceptors in a region. At the same time, existing PET/MRI attenuation correction methods are predicated on synthesizing CT data, which is not ideal, as CT data are acquired with much lower-energy photons than PET data and thus do not optimally reflect the PET attenuation map. Methods: We trained a convolutional neural network to generate patient-specific transmission data from T1-weighted MRI. Using the trained network, we generated transmission data for a testing set comprising 11 subjects scanned with 11C-labeled N-[2-]4-(2-methoxyphenyl)-1-piperazinyl]ethyl]-N-(2-pyridinyl)cyclohexanecarboxamide) (11C-WAY-100635) and 10 subjects scanned with 11C-labeled 3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)benzonitrile (11C-DASB). We assessed both static and dynamic reconstructions. For dynamic PET data, we report differences in both the nondisplaceable and the free binding potential for 11C-WAY-100635 and distribution volume for 11C-DASB. Results: The mean bias for generated transmission data was -1.06% ± 0.81%. Global biases in static PET uptake were -0.49% ± 1.7%, and -1.52% ± 0.73% for 11C-WAY-100635 and 11C-DASB, respectively. Conclusion: Our neural network approach is capable of synthesizing patient-specific transmission data with sufficient accuracy for both static and dynamic PET studies.

Prediction of Abnormal Bone Density and Osteoporosis From Lumbar Spine MR Using Modified Dixon Quant in 257 Subjects With Quantitative Computed Tomography as Reference.

BACKGROUND: Bone marrow fat increases when bone mass decreases, which could be attributed to the fact that adipogenesis competes with osteogenesis. Bone marrow fat has the potential to predict abnormal bone density and osteoporosis. PURPOSE: To investigate the predictive value of using vertebral bone marrow fat fraction(BMFF) obtained from modified Dixon(mDixon) Quant in the determination of abnormal bone density and osteoporosis. STUDY TYPE: Prospective. POPULATION: 257 subjects (age: 20-79 years old; BMI: 16.6-32.9 kg/m2 ;181 females,76 males) without known spinal tumor, history of trauma, dysplasia, spinal surgery or hormone therapy. FIELD STRENGTH/SEQUENCE: 3.0T/mDixon. ASSESSMENT: BMFF was measured at the L1, L2 and L3 vertebral body on fat fraction maps of the lumbar spine. Bone mineral density (BMD) was obtained using quantitative computed tomography, which served as the reference standard. STATISTICAL TESTS: The BMFF between the three groups (normal bone density, osteopenia and osteoporosis) was tested using one-way analysis of variance in SPSS. The correlation and partial correlation of BMFF and BMD were analyzed before and after controlling for age, sex and BMI. Logistic regression analysis using independent training and validation data was conducted to evaluate the performance of predicting abnormal BMD or osteoporosis using BMFF. RESULTS: There was a significant difference in vertebral BMFF between the three groups (P < 0.001). Moderate inverse correlation was found between vertebral BMFF and BMD after controlling age, sex and BMI (r = -0.529; P < 0.001). The mean area under the curve, sensitivity, specificity and negative predictive value (NPV) for predicting abnormal bone density were 0.940, 0.877, 0.896, and 0.890, respectively. The corresponding results for predicting subjects with osteoporosis were 0.896, 0.848, 0.853, and 0.969, respectively. DATA CONCLUSION: mDixon Quant is a fast, simple, noninvasive and nonionizing method to access vertebral BMFF and has a high predictive power for identifying abnormal bone density and osteoporosis. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:390-399.

Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.

BACKGROUND: Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy. PURPOSE: To noninvasively predict SLN metastasis in breast cancer using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) intra- and peritumoral radiomics features combined with or without clinicopathologic characteristics of the primary tumor. STUDY TYPE: Retrospective. POPULATION: A total of 163 breast cancer patients (55 positive SLN and 108 negative SLN). FIELD STRENGTH/SEQUENCE: 1.5T, T1 -weighted DCE-MRI. ASSESSMENT: A total of 590 radiomic features were extracted for each patient from both intratumoral and peritumoral regions of interest. To avoid overfitting, the dataset was randomly separated into a training set (∼67%) and a validation set (∼33%). The prediction models were built with the training set using logistic regression on the most significant radiomic features in the training set combined with or without clinicopathologic characteristics. The prediction performance was further evaluated in the independent validation set. STATISTICAL TESTS: Mann-Whitney U-test, Spearman correlation, least absolute shrinkage selection operator (LASSO) regression, logistic regression, and receiver operating characteristic (ROC) analysis were performed. RESULTS: Combining radiomic features with clinicopathologic characteristics, six features were automatically selected in the training set to establish the prediction model of SLN metastasis. In the independent validation set, the area under ROC curve (AUC) was 0.869 (NPV = 0.886). Using radiomic features alone in the same procedure, 4 features were selected and the validation set AUC was 0.806 (NPV = 0.824). DATA CONCLUSION: This is the first attempt to demonstrate the feasibility of using DCE-MRI radiomics to predict SLN metastasis in breast cancer. Clinicopathologic characteristics improved the prediction performance. This study provides noninvasive methods to evaluate SLN status for guiding further treatment of breast cancer patients, and can potentially benefit those with negative SLN, by eliminating unnecessary invasive lymph node removal and the associated complications, which is a step further towards precision medicine. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:131-140.

Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model.

BACKGROUND: Diffusion-weighted imaging (DWI) has been studied in breast imaging and can provide more information about diffusion, perfusion and other physiological interests than standard pulse sequences. The stretched-exponential model has previously been shown to be more reliable than conventional DWI techniques, but different diagnostic sensitivities were found from study to study. PURPOSE: This work investigated the characteristics of whole-lesion histogram parameters derived from the stretched-exponential diffusion model for benign and malignant breast lesions, compared them with conventional apparent diffusion coefficient (ADC), and further determined which histogram metrics can be best used to differentiate malignant from benign lesions. STUDY TYPE: This was a prospective study. POPULATION: Seventy females were included in the study. FIELD STRENGTH/SEQUENCE: Multi-b value DWI was performed on a 1.5T scanner. ASSESSMENT: Histogram parameters of whole lesions for distributed diffusion coefficient (DDC), heterogeneity index (α), and ADC were calculated by two radiologists and compared among benign lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma confirmed by pathology. STATISTICAL TESTS: Nonparametric tests were performed for comparisons among invasive carcinoma, DCIS, and benign lesions. Comparisons of receiver operating characteristic (ROC) curves were performed to show the ability to discriminate malignant from benign lesions. RESULTS: The majority of histogram parameters (mean/min/max, skewness/kurtosis, 10-90th percentile values) from DDC, α, and ADC were significantly different among invasive carcinoma, DCIS, and benign lesions. DDC10% (area under curve [AUC] = 0.931), ADC10% (AUC = 0.893), and αmean (AUC = 0.787) were found to be the best metrics in differentiating benign from malignant tumors among all histogram parameters derived from ADC and α, respectively. The combination of DDC10% and αmean , using logistic regression, yielded the highest sensitivity (90.2%) and specificity (95.5%). DATA CONCLUSION: DDC10% and αmean derived from the stretched-exponential model provides more information and better diagnostic performance in differentiating malignancy from benign lesions than ADC parameters derived from a monoexponential model. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1701-1710.

Diffusion Entropy: A Potential Neuroimaging Biomarker of Bipolar Disorder in the Temporal Pole.

Despite much research, bipolar depression remains poorly understood, with no clinically useful biomarkers for its diagnosis. The paralimbic system has become a target for biomarker research, with paralimbic structural connectivity commonly reported to distinguish bipolar patients from controls in tractography-based diffusion MRI studies, despite inconsistent findings in voxel-based studies. The purpose of this analysis was to validate existing findings with traditional diffusion MRI metrics and investigate the utility of a novel diffusion MRI metric, entropy of diffusion, in the search for bipolar depression biomarkers. We performed group-level analysis on 9 un-medicated (6 medication-naïve; 3 medication-free for at least 33 days) bipolar patients in a major depressive episode and 9 matched healthy controls to compare: (1) average mean diffusivity (MD) and fractional anisotropy (FA) and; (2) MD and FA histogram entropy-a statistical measure of distribution homogeneity-in the amygdala, hippocampus, orbitofrontal cortex and temporal pole. We also conducted classification analyses with leave-one-out and separate testing dataset (N = 11) approaches. We did not observe statistically significant differences in average MD or FA between the groups in any region. However, in the temporal pole, we observed significantly lower MD entropy in bipolar patients; this finding suggests a regional difference in MD distributions in the absence of an average difference. This metric allowed us to accurately characterize bipolar patients from controls in leave-one-out (accuracy = 83%) and prediction (accuracy = 73%) analyses. This novel application of diffusion MRI yielded not only an interesting separation between bipolar patients and healthy controls, but also accurately classified bipolar patients from controls.