Citations

AbstractContent used by Medical Physicists for fluoroscopy safety training to staff is deliverable via several formats, that is, online content or a live audience slide presentations. Here, we share one example of a kinesthetic (live, hands‐on simulation) educational program in use at our facility for some time (~10 years). In this example, the format and content specifically target methods of reducing physician operator exposures from scattered x rays. A kinesthetic format identifies and promotes the adoption of exposure‐reducing behaviors. Key kinesthetic elements of this type of training include: physician hands‐on measurements of radiation levels at locations specific to their standing positions (e.g., primary arterial access points) in the room using handheld exposure rate meters, measurement of exposure rate reduction to physicians provided by using personal protective equipment, that is, wearable aprons, hanging lead drapes, and pull‐down shields. Physician choice of procedure‐specific tableside selectable controls affecting exposure rate from optional fluoroscopy, Cine or digital subtraction angiography (DSA), along with comparative measured contribution to physician exposure is demonstrated. The inverse square exposure rate reduction to physicians when stepping back from the table during DSA is a key observation. Kinesthetic simulations in the rooms used by physicians have been found to provide the highest level of understanding giving rise to adoption of practices that are impactful for physicians. Specific training scripts are in place for physician sub‐specialization in interventional radiology, cardiology, neurosurgery, vascular surgery, and gastroenterology. This training is used for new physician staff while classroom presentations (whose content mimics in room training) are used with staff who have had previously had in room training.

Abstract There have been increasing efforts to standardize MRI scanning protocols for both routine and clinical trial imaging of glioma. The recent BTIP guidelines propose acquisition of T2-weighted images after gadiolinium (Gd) based contrast injection and before post-contrast 3D T1-weighted (T1W) images to control timing of images after contrast administration. While Gd-contrast shortens T1W signal and appears hyperintense on magnetic resonance imaging (MRI), Gd-contrast also shortens T2W signal, which could result in unintended alternations in T2W signal intensities. This has potential implications for model-based approaches for GBM diagnosis and the differential diagnosis of other tumor types (e.g., lymphoma) that rely on T2W signal characteristics. To our knowledge, no neuroimaging studies have formally evaluated the impact of Gd contrast on T2W imaging. We acquired multi-echo T2 sequences (TE=14, 28, 41, 55, 69 ms) both prior to and after the administration of Gd for 5 patients suspected of having a brain tumor. Quantitative T2 maps were created by fitting the signal decay to a signal exponential with the Levenburg-Marqurdt algorithm. Percent difference maps were calculated to compare the quantitative T2 changes between the pre- and post-Gd T2 maps. Additionally, we performed a Wilcoxon Rank-Sum test comparing T2 values within regions of interest (ROIs) representing the tumor and normal appearing white matter (NAWM). Significant differences between the T2 values in the tumor and NAWM ROIs were seen for 4 and 3 of the patients, respectively (all p-values < 0.01). Our initial data suggests that the administration of Gd contrast may affect T2 values in both the tumor and NAWM, which could impact quantitative image-based models and diagnoses in glioma. We are currently replicating these preliminary results in an expanded cohort.

Abstract Dynamic susceptibility contrast (DSC) MRI measures of brain tumor cerebral blood volume (CBV) are able to predict grade, overall survival and response to treatment. Wide-spread acceptance of DSC-MRI has been challenged by the need to balance contrast agent dose and CBV accuracy. The goal of this study was to identify and validate single-dose, BTIP compliant, DSC-MRI protocols. Using a validated, patient-based DRO, we evaluated CBV accuracy across a range of acquisition parameters (field strength, TR, TE, flip angle, multi-echo acquisitions, dosing protocols) and post-processing steps. To validate the optimal protocols, we next collected DSC-MRI data following ASFNR’s recommended “double – dose” approach, where a single-dose preload (to minimize T1 effects) is given prior to a second bolus injection (for DSC-MRI data acquisition). The single-dose DSC-MRI data was collected during the preload bolus injection. Consistency of the derived CBV data, visual agreement and data characteristics (e.g. CNR) was statistically evaluated. When using a single-dose and routine single-echo pulse sequence, the DRO analysis found that a low flip angle (LFA = 30o) and 30ms TE provided the highest CBV accuracy (concordance correlation coefficient (CCC) = 0.92) and precision (coefficient of variation (CV) = 8.2%)). For comparison, the maximum accuracy found with the DRO utilizes a double-dose injection protocol and yielded a CCC of 0.98 and CV of 6.8%. Single-dose, multi-echo acquisitions provided higher accuracy than the LFA data and matched that found with the double-dose approach. In patients (data collection ongoing), the agreement between single-dose LFA (n > 40) or multi-echo (n > 40) based CBV values and the reference double-dose approach was very high (CCC > 0.94) and were statistically equivalent. Optimized single-dose DSC-MRI protocols provide highly accurate CBV data, use lower doses of contrast agent, and simplify scan procedures, indicating their potential for robust use in clinical practice and trials.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.