Selective and deceptive citation in the construction of dueling consensuses

The COVID-19 pandemic provides a unique opportunity to study science communication and, in particular, the transmission of consensus. In this study, we show how "science communicators," writ large to include both mainstream science journalists and practiced conspiracy theorists, transform scientific evidence into two dueling consensuses using the effectiveness of masks as a case study. We do this by compiling one of the largest, hand-coded citation datasets of cross-medium science communication, derived from 5 million Twitter posts of people discussing masks. We find that science communicators selectively uplift certain published works while denigrating others to create bodies of evidence that support and oppose masks, respectively. Anti-mask communicators in particular often use selective and deceptive quotation of scientific work and criticize opposing science more than pro-mask communicators. Our findings have implications for scientists, science communicators, and scientific publishers, whose systems of sharing (and correcting) knowledge are highly vulnerable to what we term adversarial science communication.

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.

Combining interventions to reduce the spread of viral misinformation

Misinformation online poses a range of threats, from subverting democratic processes to undermining public health measures. Proposed solutions range from encouraging more selective sharing by individuals to removing false content and accounts that create or promote it. Here we provide a framework to evaluate interventions aimed at reducing viral misinformation online both in isolation and when used in combination. We begin by deriving a generative model of viral misinformation spread, inspired by research on infectious disease. By applying this model to a large corpus (10.5 million tweets) of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions are unlikely to be effective in isolation. However, our framework demonstrates that a combined approach can achieve a substantial reduction in the prevalence of misinformation. Our results highlight a practical path forward as misinformation online continues to threaten vaccination efforts, equity and democratic processes around the globe.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

Quantitative Imaging Informatics for Cancer Research

PURPOSE

We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program.

METHODS

QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach.

RESULTS

Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation.

CONCLUSION

Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

DeepNeuro: an open-source deep learning toolbox for neuroimaging

Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.

NIMG-05. ADVANCED IMAGING TO ASSESS LONGITUDINAL VASCULAR CHANGES IN BRAIN METASTASES TREATED WITH CHECKPOINT INHIBITION

Immune checkpoint inhibitors (ICI) have recently been shown to be effective for brain metastases (BM) in melanoma and lung cancer. However, accurately assessing intracranial response in patients undergoing ICI is a challenge, as current measures cannot reliably distinguish pseudoprogression from true tumor progression. To identify potential biomarkers of response, we analyzed standard post-contrast and dynamic susceptibility contrast MRI to identify characteristic vascular signatures as part of an ongoing Phase 2 study of pembrolizumab for patients with untreated or progressive, previously treated BM from any histology. Tumor volume measurements were calculated by summating all enhancing voxels. A volumetric increase of >40% was categorized as progressive disease (PD), a decrease of >60% as partial response (PR), and stable disease (SD) as between -60% and +40%. 78 patients have been enrolled, of whom 44 have received at least baseline advanced MR imaging. Histologies include 21 with breast cancer, 5 with non-small cell lung cancer, 4 with melanoma, and 13 with other cancers. At baseline, the total number of BM was 1-50+ per patient. Based on summing the entire enhancing intracranial disease burden, best volumetric responses for the 33 evaluable patients include 4 PR, 10 SD, and 19 PD. On preliminary analysis, there was a correlation between increased tumor cerebral blood volume/flow with tumor progression. Correlation of additional vascular physiologic parameters (e.g. vessel caliber, tissue oxygenation) to volumetric response, patient outcome, and standardized response criteria (iRANO) are ongoing. Our data provides potential evidence that effective ICI is associated with a decrease in perfusion. Ongoing analyses to uncover additional vascular changes – specifically longitudinal metrics reflecting vascular structure and function - within BM to ICI are pending. These findings have potential to explore mechanisms of ICI response and resistance, as well as biomarkers of response.

Advanced imaging to assess longitudinal vascular changes in brain metastases treated with immune checkpoint inhibition

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Background: Immune checkpoint inhibitors (ICI) have recently been shown to be effective for brain metastases (BM) for melanoma and lung cancer. This breakthrough has prompted interest in evaluating ICI in BM of other histologies. However, accurately assessing intracranial response in patients undergoing ICI is a challenge, as current measures cannot distinguish pseudoprogression from true tumor progression. To shed light on potential biomarkers of response, we prospectively use perfusion MRI to identify characteristic vascular signatures in a BM-specific trial of ICI. Methods: As part of an ongoing phase II study of pembrolizumab for patients with untreated or progressive, previously treated BM from any histology, patients underwent advanced MRI that includes tumor volume measurements and perfusion imaging with dynamic susceptibility contrast MRI. To calculate volumetric radiographic response, all enhancing voxels were summated. A volumetric increase of >40% was categorized as progressive disease (PD), a decrease of >60% as partial response (PR), and stable disease (SD) as between -60% and +40%. Results: 53 patients have been enrolled, of whom 44 have received at least baseline advanced MR imaging. Histologies include 21 with breast cancer, 5 with non-small cell lung cancer, 4 with melanoma, and 13 with other cancers. At baseline, the total number of BM was 1-50+ per patient. Based on summing the entire enhancing intracranial disease burden, best volumetric responses for the 33 evaluable patients include 4 PR, 10 SD, and 19 PD. On preliminary analysis, there was a correlation between increased tumor cerebral blood volume/flow with tumor progression. Correlation of additional vascular physiologic parameters (e.g. vessel caliber, tissue oxygenation) and volumetric response to patient outcome and standardized response criteria (iRANO) are ongoing. Conclusions: Pembrolizumab likely has anti-tumor efficacy in BM. Our data provides potential evidence that effective ICI is associated with a decrease in perfusion. Ongoing analyses to uncover additional vascular changes – specifically longitudinal metrics reflecting vascular structure and function - within BM to ICI are pending. These findings have potential to illustrate mechanisms of efficacy for ICI and biomarkers of response in this patient population.

MRI changes in patients with newly diagnosed glioblastoma treated as part of a phase II trial with bavituximab, radiation, and temozolomide

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Background: Glioblastoma and tumor endothelial cells express phosphatidylserine (PS), a highly immunosuppressive membrane phospholipid. Bavituximab – a chimeric monoclonal antibody – binds to β2-glycoprotein 1 (β2-GP1) to form a complex of β2-GP1 with PS, resulting in immune activation against tumor cells and anti-angiogenic effects. Phase I/II trials in other solid cancers demonstrated response rates up to 75% when bavituximab was given with cytotoxic chemotherapy. Pre-clinical data in glioblastoma models suggested synergistic effects of PS blockade, radiation, and temozolomide. Methods: 33 adult patients with IDH-wild-type, MGMT-methylated or -unmethylated newly diagnosed glioblastoma were enrolled in this phase II trial (NCT03139916) and received 6 weeks of chemoradiation, followed by 6 cycles of adjuvant temozolomide (C1-C6 aTMZ). Bavituximab (3 mg/kg) was given weekly, starting week 1 of chemoradiation, for 18 weeks with the option to continue if tolerated. Physiologic MRIs were performed pre-treatment, pre-C1, pre-C3, and pre-C5 aTMZ. Within the enhancing tumor region, median tumor Ktrans (reflecting vascular permeability) and relative cerebral blood flow (rCBF) were measured. Median percent changes during treatment were compared to pre-treatment values. Results: Median progression-free survival (mPFS) was 8 months. Based on a median overall survival (mOS) of 17.1 months, patients were categorized into above-median survivors (AMS) and below-median survivors (BMS). All patients had pre-treatment scans. 31 had evaluable pre-C1, 25 had pre-C3, and 7 had pre-C5 scans. Compared to BMS, AMS had a greater reuction in enhancing tumor volume and rCBF, and a greater increase in Ktrans during treatment (table). One patient remains on study; 23 patients have died. Bavituximab was well tolerated. Conclusions: mPFS and mOS in patients treated with bavituximab, radiation and temozolomide were comparable to standard chemoradiation and aTMZ. Lower rCBF in AMS may reflect decreased tumor perfusion while higher Ktrans could imply enhanced drug delivery to the tumor. Bavituximab induces changes in tumor vasculature that may improve survival in a subset of patients. Clinical trial information: NCT03139916 . [Table: see text]

Distributed deep learning networks among institutions for medical imaging

Objective

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

Methods

We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet).

Results

We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer.

Conclusions

We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

NIMG-43. LONGITUDINAL TRACKING AND GROWTH RATE CHARACTERIZATION OF BRAIN METASTASES ON MAGNETIC RESONANCE IMAGING

PURPOSE

Measuring treatment response is vital for assessing efficacy of treatment regimen for patients with brain metastases (BM). Unfortunately, manual delineation of all lesions on MRI across time-points is prohibitively time-consuming, making it infeasible to track individual lesion growth/shrinkage rates as part of the clinical workflow. To overcome this challenge, we propose a deep learning approach to segment all BM, and furthermore, show that certain brain regions are more prone to high-growth rate lesions.

METHODS

163 longitudinal MRIs from 77 patients with MPRAGE-post contrast imaging protocol were prospectively obtained from Massachusetts General Hospital (MGH). An expert neuro-oncologist provided ground truth segmentations for all patients. A 3D U-Net architecture was trained to automatically segment BM; training was stopped when validation set Dice score plateaued to prevent overfitting. To enable lesion tracking, all time-points per patient were affinely registered to each other. Every lesion was subsequently classified based on its growth rate (responder: overall lesion shrinkage; inconclusive: 0% to 40% lesion growth; non-responder: more than 40% lesion growth). Characterization of global lesion growth rate patterns was accomplished by affinely registering all time-points to the MNI brain atlas. Segmented lesions were projected onto the atlas, which was qualitatively analyzed to identify spatial regions composed primarily of one class of lesion.

RESULTS

For automatic segmentation, we report a mean dice score of 0.778, 0.737, and 0.704 on training, validation, and testing sets respectively. Furthermore, we find that the largest BM with the highest average growth rate (non-responders) tend to be located in the posterior frontal/parietal lobes, while smaller, lower growth rate lesions (responders) tend to be localized in the frontal lobes. The posterior fossa was found to be heterogeneous in lesion size and growth rate.

CONCLUSION

We developed automatic metastatic lesion tracking over time-points and identified brain regions associated with differing growth rate lesions.

Abstract WMP17: Fully-Automated Ischemic Brain Infarct Volumetric Segmentation in Diffusion Weighted MR using Deep Learning

Introduction: Brain imaging is a key step in the clinical evaluation of ischemic stroke, with Diffusion Weighted Magnetic Resonance (DWI) being a key imaging modality, as it allows for assessment of extent of acute ischemic brain injury. Manual delineation of stroke regions is expensive, time-consuming, and subject to inter-rater variability. In this study, we develop a deep learning approach for ischemic stroke volumetric segmentation in a large clinical dataset of 1,239 patients from the NIH-funded Heart-Brain Interactions in Human Acute Ischemic Stroke Study utilizing only DWI imaging.

Hypothesis: Deep learning can be used to automatically calculate stroke volumes in high agreement with manual human expert segmentations.

Methods: The patients were randomly divided into Training (n = 743), Validation (n=248), and Testing (n=248). We implemented the 3D U-Net neural network architecture. Additionally, we modified the 3D U-Net by incorporating incorporate state-of-the-art components that have improved neural network architectures for classification tasks, namely residual connections, inception modules, dense connections, and squeeze-and-excitation modules.

Results: The best performing individual model was the Inception U-Net, which had a median dice similarity coefficient of 0.720 (0.011-0.920) within the Testing Set. In comparing manually and automatically derived infarct volumes, the Intraclass Correlation Coefficient was 0.974 (p<.0001) in the Testing Set.

Conclusions: Our fully-automatic pipeline for stroke segmentation demonstrates the potential for deep learning-based tools to automate ischemic stroke volumetrics.

Abstract 162: Viscerotoxic Brain Infarcts: The Results of Heart-Brain Interactions Study

Introduction: Acute brain infarcts can provoke functional and morphologic changes in internal organs in the absence of primary visceral causes. We tested the hypothesis that infarcts in certain regions of the brain were much more likely to be associated with internal organ injury (IOI) than other regions using a method that was free from the bias of a-priori hypothesis as to any specific location.

Methods: We generated neuroanatomic maps of brain infarcts for plasma troponin T elevation (structural cardiac marker), QTc prolongation on ECG (electrophysiological cardiac marker), pneumonia and urinary tract infection (marker for altered immune/pulmonary/urinary physiology), acute stress hyperglycemia (hepatic marker of glycogenolysis) in 1208 consecutive patients who were prospectively recruited into the NIH-funded HBI study. We utilized Threshold-Free Cluster Enhancement to examine the relationship between infarct location and IOI. We generated voxel-wise p-value maps using a permutation-based approach and identified clusters of contiguous voxels associated with IOI with a p-value <0.05.

Results: We identified significant clusters of voxels for each form of IOI. The clusters for troponin elevation and QTc prolongation encompassed similar regions in the right insular cortex. The cluster for acute stress hyperglycemia involved the left insula. We found two large clusters for post-stroke infection, one for pneumonia in the left and one for urinary tract infection in the right hemisphere, both encompassing the insular cortex. The relationship between infarct location and IOI persisted after adjusting for infarct volume, vascular risk factors, and stroke etiology.

Conclusion: Our results uncover the neuroanatomical substrate of post-stroke IOI. Localizing discrete regions of brain infarcts associated with IOI could be used to bootstrap towards new markers for better differentiation between neurogenic and non-neurogenic mechanisms of post-stroke IOI.

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Importance

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable.

Objective

To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs.

Design, Setting, and Participants

A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre-plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017.

Exposures

A deep learning algorithm trained on retinal photographs.

Main Outcomes and Measures

Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre-plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts.

Results

Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre-plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre-plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre-plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre-plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts.

Conclusions and Relevance

This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

Semi-automated pulmonary nodule interval segmentation using the NLST data

PURPOSE

To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.

METHODS

We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.

RESULTS

We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).

CONCLUSIONS

We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073-81. ©2017 AACR.

Using three-dimensional imaging to assess treatment outcomes in orthodontics: a progress report from the University of the Pacific

Past research in integrated three-dimensional (3D) craniofacial mapping at the Craniofacial Research Instrumentation Laboratory (CRIL) of the University of the Pacific is summarized in narrative form. The advantages and limitations of recent commercial developments in the application of cone beam geometry volumetric X-ray scanners in dentistry and surface digital mapping of study casts are discussed. The rationale for methods currently in development at CRIL for merging longitudinal information from existing 3D study casts and two-dimensional lateral X-ray cephalograms in studies of orthodontic treatment outcome is presented.