Faculty Bibliography

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.

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.

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.

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:;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.

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:-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.