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PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

BACKGROUND: The use of a Left Ventricular Assist Device (LVAD) in patients with advanced heart failure refractory to optimal medical management has progressed steadily over the past two decades. Data have demonstrated reduced LVAD efficacy, worse clinical outcome, and higher mortality for patients who experience significant ventricular tachyarrhythmia (VTA). We hypothesize that a novel prophylactic intra-operative VTA ablation protocol at the time of LVAD implantation may reduce the recurrent VTA and adverse events postimplant. METHODS: We designed a prospective, multicenter, open-label, randomized-controlled clinical trial enrolling 100 patients who are LVAD candidates with a history of VTA in the previous 5 years. Enrolled patients will be randomized in a 1:1 fashion to intra-operative VTA ablation (n = 50) versus conventional medical management (n = 50) with LVAD implant. Arrhythmia outcomes data will be captured by an implantable cardioverter defibrillator (ICD) to monitor VTA events, with a uniform ICD programming protocol. Patients will be followed prospectively over a mean of 18 months (with a minimum of 9 months) after LVAD implantation to evaluate recurrent VTA, adverse events, and procedural outcomes. Secondary endpoints include right heart function/hemodynamics, healthcare utilization, and quality of life. CONCLUSION: The primary aim of this first-ever randomized trial is to assess the efficacy of intra-operative ablation during LVAD surgery in reducing VTA recurrence and improving clinical outcomes for patients with a history of VTA.

Background and aimsLong QT syndrome (LQTS) and coronary artery disease (CAD) are both associated with increased risk of ventricular tachyarrhythmia. However, there are limited data on the incremental risk conferred by CAD in adult patients with congenital LQTS. We aimed to investigate the risk associated with CAD and life threatening events (LTEs) in patients with LQTS after age 40 years.MethodsThe risk of LTEs (comprising aborted cardiac arrest, sudden cardiac death, or appropriate defibrillator shock) from age 40 through 75 years was examined in 1,020 subjects from the Rochester LQTS registry, categorized to CAD (n = 137) or no-CAD (n = 883) subgroups.ResultsSurvival analysis showed that patients with CAD had a significantly higher cumulative event rate of LTEs from 40 to 75 years (35%) compared with those without CAD (7%; p 4-fold (p = 0.008) increased risk of LTEs among LQTS patients with a lower-range QTc (

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability.PurposeThe purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability.MethodsOur dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics.ResultsDuring the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attention maps are not satisfactory; when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set.ConclusionsOur results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

PurposeRecent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.MethodsWe retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.ResultsThe mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.ConclusionWe observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD.

The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm-1, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm-1. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

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).

IntroductionPancreatic ductal adenocarcinoma (PDAC) is a lethal cancer associated with an immunosuppressive environment. Neutrophil extracellular traps (NETs) were initially described in the context of infection but have more recently been implicated in contributing to the tolerogenic immune response in PDAC. Thus, NETs are an attractive target for new therapeutic strategies. Group A Streptococcus (GAS) has developed defensive strategies to inhibit NETs.MethodsIn the present work, we propose utilizing intra-tumoral GAS injection to stimulate anti-tumor activity by inhibiting cancer-promoting NETs. Mice harboring Panc02 or KPC subcutaneous tumors injected with three different M-type GAS strains. Tumors and spleens were harvested at the endpoint of the experiments to assess bacterial colonization and systemic spread, while sera were analyzed for humoral responses toward the streptococcal antigens, especially the M1 and Scl1 proteins. Role of the streptococcal collagen-like protein 1 (Scl1) in anti-PDAC activity was assessed in vivo after intratumoral injection with M1 GAS wild-type, an isogenic mutant strain devoid of Scl1, or a complemented mutant strain with restored scl1 expression. In addition, recombinant Scl1 proteins were tested for NET inhibition using in vitro and ex vivo assays assessing NET production and myeloperoxidase activity.ResultsInjection of three different M-type GAS strains reduced subcutaneous pancreatic tumor volume compared to control in two different murine PDAC models. Limitation of tumor growth was dependent on Scl1, as isogenic mutant strain devoid of Scl1 did not reduce tumor size. We further show that Scl1 plays a role in localizing GAS to the tumor site, thereby limiting the systemic spread of bacteria and off-target effects. While mice did elicit a humoral immune response to GAS antigens, tested sera were weakly immunogenic toward Scl1 antigen following intra-tumoral treatment with Scl1-expressing GAS. M1 GAS inhibited NET formation when co-cultured with neutrophils while Scl1-devoid mutant strain did not. Recombinant Scl1 protein inhibited NETs ex vivo in a dose-dependent manner by suppressing myeloperoxidase activity.DiscussionAltogether, we demonstrate that intra-tumoral GAS injections reduce PDAC growth, which is facilitated by Scl1, in part through inhibition of cancer promoting NETs. This work offers a novel strategy by which NETs can be targeted through Scl1 protein and potentiates its use as a cancer therapeutic.

PurposeRecent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel.MethodA cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC 50% of radiomic features.ConclusionWe systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: (a) Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. (b) Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.

Abstract Background The predictive value of left ventricular (LV) global longitudinal strain (GLS) to predict outcomes in different left ventricular ejection fraction (LVEF) cohorts is not well known. We aimed to assess the role of LV GLS predicting outcomes in HF patients by LVEF. Methods In the Multicenter Automatic Defibrillator Implantation Trial Cardiac Resynchronization Therapy (MADIT‐CRT), we studied 1077 patients (59%) with 2D speckle tracking data available, 437 patients with LVEF > 30% and 640 with LVEF ≤ 30%. Baseline LV GLS was stratified in tertiles in both LVEF subgroups. The primary endpoint was ventricular tachycardia/fibrillation (VT/VF) or death; the secondary endpoint was heart failure (HF) or death. Results In patients with LVEF ≤ 30%, a higher tertile GLS (T3, less contractility) was associated with a higher rate of VT/VF/death (P 30% (P = 0.057). In patients with LVEF ≤ 30%, a higher tertile GLS was also associated with a higher rate of HF/death. In multivariable models, LV GLS predicted VT/VF or death in the LVEF ≤ 30% subgroup [T1 vs. T2/3 HR = 1.67 (1.16–2.38), P = 0.005], but not in those with LVEF > 30% [T1 vs. T2.3 HR = 1.32 (0.86–2.04), P = 0.21]. LV GLS predicted HF/death in the LVEF ≤ 30% subgroup [T1 vs T2/3 HR = 2.00 (1.30–3.13), P = 0.002], but not in in those with LVEF > 30%. Conclusions In this MADIT‐CRT sub‐study, LV GLS identified patients at higher risk of VT/VF, HF/death risk independently of conventional clinical parameters in patients with LVEF ≤ 30%, but not in patients with LVEF > 30%.

PurposeThe purpose of this work was to estimate and compare breast and lung doses of chest CT scans using organ-based tube current modulation (OBTCM) to those from conventional, attenuation-based automatic tube current modulation (ATCM) across a range of patient sizes.MethodsThirty-four patients (17 females, 17 males) who underwent clinically indicated CT chest/abdomen/pelvis (CAP) examinations employing OBTCM were collected from two multi-detector row CT scanners. Patient size metric was assessed as water equivalent diameter (Dw ) taken at the center of the scan volume. Breast and lung tissues were segmented from patient image data to create voxelized models for use in a Monte Carlo transport code. The OBTCM schemes for the chest portion were extracted from the raw projection data. ATCM schemes were estimated using a recently developed method. Breast and lung doses for each TCM scenario were estimated for each patient model. CTDIvol -normalized breast (nDbreast ) and lung (nDlung ) doses were subsequently calculated. The differences between OBTCM and ATCM normalized organ dose estimates were tested using linear regression models that included CT scanner and Dw as covariates.ResultsMean dose reduction from OBTCM in nDbreast was significant after adjusting for the scanner models and patient size (P = 0.047). When pooled with females and male patient, mean dose reduction from OBTCM in nDlung was observed to be trending after adjusting for the scanner model and patient size (P = 0.085).ConclusionsOne specific manufacturer's OBTCM was analyzed. OBTCM was observed to significantly decrease normalized breast relative to a modeled version of that same manufacturer's ATCM scheme. However, significant dose savings were not observed in lung dose over all. Results from this study support the use of OBTCM chest protocols for females only.

Background Sex‐specific risk management may improve outcomes in congenital long QT syndrome (LQTS). We recently developed a prediction score for cardiac events (CEs) and life‐threatening events (LTEs) in postadolescent women with LQTS. In the present study, we aimed to develop personalized risk estimates for the burden of CEs and LTEs in male adolescents with potassium channel‐mediated LQTS. Methods and Results The prognostic model was derived from the LQTS Registry headquartered in Rochester, NY, comprising 611 LQT1 or LQT2 male adolescents from age 10 through 20 years, using the following variables: genotype/mutation location, QTc‐specific thresholds, history of syncope, and β‐blocker therapy. Anderson‐Gill modeling was performed for the end point of CE burden (total number of syncope, aborted cardiac arrest, and appropriate defibrillator shocks). The applicability of the CE prediction model was tested for the end point of the first LTE (excluding syncope and adding sudden cardiac death) using Cox modeling. A total of 270 CEs occurred during follow‐up. The genotype–phenotype risk prediction model identified low‐, intermediate‐, and high‐risk groups, comprising 74%, 14%, and 12% of the study population, respectively. Compared with the low‐risk group, high‐risk male subjects experienced a pronounced 5.2‐fold increased risk of recurrent CEs (P

PurposeTask Group Report 195 of the American Association of Physicists in Medicine contains reference datasets for the direct comparison of results among different Monte Carlo (MC) simulation tools for various aspects of imaging research that employs ionizing radiation. While useful for comparing and validating MC codes, that effort did not provide the information needed to compare absolute dose estimates from CT exams. Therefore, the purpose of this work is to extend those efforts by providing a reference dataset for benchmarking fetal dose derived from MC simulations of clinical CT exams.Acquisition and validation methodsThe reference dataset contains the four necessary elements for validating MC engines for CT dosimetry: (a) physical characteristics of the CT scanner, (b) patient information, (c) exam specifications, and (d) fetal dose results from previously validated and published MC simulations methods in tabular form. Scanner characteristics include non-proprietary descriptions of equivalent source cumulative distribution function (CDF) spectra and bowtie filtration profiles, as well as scanner geometry information. Additionally, for the MCNPX MC engine, normalization factors are provided to convert raw simulation results to absolute dose in mGy. The patient information is based on a set of publicly available fetal dose models and includes de-identified image data; voxelized MC input files with fetus, uterus, and gestational sac identified; and patient size metrics in the form of water equivalent diameter (Dw ) z-axis distributions from a simulated topogram (Dw,topo ) and from the image data (Dw,image ). Exam characteristics include CT scan start and stop angles and table and patient locations, helical pitch, nominal collimation and measured beam width, and gantry rotation time for each simulation. For simulations involving estimating doses from exams using tube current modulation (TCM), a realistic TCM scheme is presented that is estimated based upon a validated method. (d) Absolute and CTDIvol -normalized fetal dose results for both TCM and FTC simulations are given for each patient model under each scan scenario.Data format and usage notesEquivalent source CDFs and bowtie filtration profiles are available in text files. Image data are available in DICOM format. Voxelized models are represented by a header followed by a list of integers in a text file representing a three-dimensional model of the patient. Size distribution metrics are also given in text files. Results of absolute and normalized fetal dose with associated MC error estimates are presented in tabular form in an Excel spreadsheet. All data are stored on Zenodo and are publicly accessible using the following link: https://zenodo.org/record/3959512.Potential applicationsSimilar to the work of AAPM Report 195, this work provides a set of reference data for benchmarking fetal dose estimates from clinical CT exams. This provides researchers with an opportunity to compare MC simulation results to a set of published reference data as part of their efforts to validate absolute and normalized fetal dose estimates. This could also be used as a basis for comparison to other non-MC approaches, such as deterministic approaches, or to commercial packages that provide estimates of fetal doses from clinical CT exams.

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Click chemistry refers to selective reactions developed for grafting of bio(macro)molecules in their biological media. Caged click compounds have been employed to spatiotemporally control click reactions. Here, we survey the uncaging of photo-dibenzocyclooctyne-OH (photoDIBO-OH) to its click-chemistry active form DIBO-OH, with particular attention to its conversion timescale and efficiency. Ultraviolet pump–infrared probe experiments reveal a stepwise decarbonylation: first, carbon monoxide (C≡O) is released within 1.8 ps, and then, it converts, within 10 ps, to DIBO-OH. Completion of uncaging is achieved with an efficiency of ∼50%. A successful demonstration of two-photon uncaging of photoDIBO-OH at long wavelength (700 nm) confers enhanced in vivo compatibility and proceeds on the same timescale. [ABSTRACT FROM AUTHOR]

Abstract Background The use of a Left Ventricular Assist Device (LVAD) in patients with advanced heart failure refractory to optimal medical management has progressed steadily over the past two decades. Data have demonstrated reduced LVAD efficacy, worse clinical outcome, and higher mortality for patients who experience significant ventricular tachyarrhythmia (VTA). We hypothesize that a novel prophylactic intra‐operative VTA ablation protocol at the time of LVAD implantation may reduce the recurrent VTA and adverse events postimplant. Methods We designed a prospective, multicenter, open‐label, randomized‐controlled clinical trial enrolling 100 patients who are LVAD candidates with a history of VTA in the previous 5 years. Enrolled patients will be randomized in a 1:1 fashion to intra‐operative VTA ablation (n = 50) versus conventional medical management (n = 50) with LVAD implant. Arrhythmia outcomes data will be captured by an implantable cardioverter defibrillator (ICD) to monitor VTA events, with a uniform ICD programming protocol. Patients will be followed prospectively over a mean of 18 months (with a minimum of 9 months) after LVAD implantation to evaluate recurrent VTA, adverse events, and procedural outcomes. Secondary endpoints include right heart function/hemodynamics, healthcare utilization, and quality of life. Conclusion The primary aim of this first‐ever randomized trial is to assess the efficacy of intra‐operative ablation during LVAD surgery in reducing VTA recurrence and improving clinical outcomes for patients with a history of VTA.

Abstract Background Congenital Long QT Syndrome (LQTS) is a hereditary arrhythmic disorder. We aimed to assess the performance of current genetic variant annotation scores among LQTS patients and their predictive impact. Methods We evaluated 2025 patients with unique mutations for LQT1–LQT3. A patient‐specific score was calculated for each of four established genetic variant annotation algorithms: CADD, SIFT, REVEL, and PolyPhen‐2. The scores were tested for the identification of LQTS and their predictive performance for cardiac events (CE) and life‐threatening events (LTE) and then compared with the predictive performance of LQTS categorization based on mutation location/function. Score performance was tested using Harrell's C‐index. Results A total of 917 subjects were classified as LQT1, 838 as LQT2, and 270 as LQT3. The identification of a pathogenic variant occurred in 99% with CADD, 92% with SIFT, 100% with REVEL, and 86% with PolyPhen‐2. However, none of the genetic scores correlated with the risk of CE (Harrell's C‐index: CADD = 0.50, SIFT = 0.51, REVEL = 0.50, and PolyPhen‐2 = 0.52) or LTE (Harrell's C‐index: CADD = 0.50, SIFT = 0.53, REVEL = 0.54, and PolyPhen‐2 = 0.52). In contrast, high‐risk mutation categorization based on location/function was a powerful independent predictor of CE (HR = 1.88; p

PurposeRecent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.MethodsWe retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.ResultsThe mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.ConclusionWe observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0–10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

PurposeThe purpose of this work was to estimate scanner-independent CTDIvol -to-fetal-dose coefficients for tube current-modulated (TCM) and fixed tube current (FTC) computed tomography (CT) examinations of pregnant patients of various gestational ages undergoing abdominal/pelvic CT examinations.MethodsFor 24 pregnant patients of gestational age from

PurposeWith recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.MethodsThe pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.ResultsThe pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.ConclusionsThe pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.

IntroductionThe implantable cardioverter defibrillator (ICD) is effective for the prevention of sudden cardiac death (SCD) in patients with heart failure and a reduced ejection fraction (HFrEF). The benefit of the ICD in patients with advanced CKD, remains elusive. Moreover, the benefit of the ICD in patients with advanced chronic kidney disease (CKD) and HFrEF who are cardiac resynchronization therapy (CRT) recipients may be attenuated.HypothesisWe hypothesized that patients with CKD who are CRT recipients may derive less benefit from the ICD due to the competing risk of dying prior to experiencing an arrhythmia.MethodsThe study population included 1,015 patients receiving CRT with defibrillator (CRT-D) device for primary prevention of SCD who were enrolled in either (Multicenter Automated Defibrillator Implantation Trial) MADIT-CRT trial or the Ranolazine in High-Risk Patients with Implanted Cardioverter Defibrillator (RAID) trial. The cohort was divided into two groups based on the stage of CKD: those with Stage 1 to 3a KD, labeled as (S1-S3a)KD. The second group included patients with Stage 3b to stage 5 kidney disease, labeled as (S3b-S5)KD. The primary endpoint was any ventricular tachycardia (VT) or ventricular fibrillation (VF) (Any VT/VF).ResultsThe cumulative incidence of Any VT/VF was 23.5% in patients with (S1-S3a)KD and 12.6% in those with (S3b-S5)KD (p