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2. Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Author

Ram, Sundaresh, Tang, Wenfei, Bell, Alexander J., Spencer, Cara, Buschhaus, Alexander, Hatt, Charles R., diMagliano, Marina Pasca, Rodriguez, Jeffrey J., Galban, Stefanie, and Galban, Craig J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms., Comment: 10 pages, 9 figures, 3 tables

Published
2021

3. Robust Segmentation of Cell Nuclei in 3-D Microscopy Images

Author

Ram, Sundaresh and Rodriguez, Jeffrey J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Accurate segmentation of 3-D cell nuclei in microscopy images is essential for the study of nuclear organization, gene expression, and cell morphodynamics. Current image segmentation methods are challenged by the complexity and variability of microscopy images and often over-segment or under-segment the cell nuclei. Thus, there is a need to improve segmentation accuracy and reliability, as well as the level of automation. In this paper, we propose a new automated algorithm for robust segmentation of 3-D cell nuclei using the concepts of random walk, graph theory, and mathematical morphology as the foundation. Like other segmentation algorithms, we first use a seed detection/marker extraction algorithm to find a seed voxel for each individual cell nucleus. Next, using the concept of random walk on a graph we find the probability of all the pixels in the 3-D image to reach the seed pixels of each nucleus identified by the seed detection algorithm. We then generate a 3-D response image by combining these probabilities for each voxel and use the marker controlled watershed transform on this response image to obtain an initial segmentation of the cell nuclei. Finally, we apply local region-based active contours to obtain final segmentation of the cell nuclei. The advantage of using such an approach is that it is capable of accurately segmenting highly textured cells having inhomogeneous intensities and varying shapes and sizes. The proposed algorithm was compared with three other automated nucleus segmentation algorithms for segmentation accuracy using overlap measure, Tanimoto index, Rand index, F-score, and Hausdorff distance measure. Quantitative and qualitative results show that our algorithm provides improved segmentation accuracy compared to existing algorithms., Comment: 10 pages, 4 figures, and 3 tables

Published
2021

4. Deep Learning-Based Air Trapping Quantification Using Paired Inspiratory-Expiratory Ultra-low Dose CT

Author

Muller, Sarah M., Ram, Sundaresh, Bayfield, Katie J., Reuter, Julia H., Gestewitz, Sonja, Yu, Lifeng, Wielpütz, Mark O., Kauczor, Hans-Ulrich, Heussel, Claus P., Robinson, Terry E., Bartholmai, Brian J., Hatt, Charles R., Robinson, Paul D., Galban, Craig J., Weinheimer, Oliver, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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7. Object sieving and morphological closing to reduce false detections in wide-area aerial imagery

Author

Gao, Xin, Ram, Sundaresh, and Rodriguez, Jeffrey J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

For object detection in wide-area aerial imagery, post-processing is usually needed to reduce false detections. We propose a two-stage post-processing scheme which comprises an area-thresholding sieving process and a morphological closing operation. We use two wide-area aerial videos to compare the performance of five object detection algorithms in the absence and in the presence of our post-processing scheme. The automatic detection results are compared with the ground-truth objects. Several metrics are used for performance comparison., Comment: 5 Pages, Submitted to 2016 23rd International Conference of Image Processing (ICIP), September 23-28, Phoenix, AZ, USA (Paper ID: 3218)

Published
2020

8. Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Author

Gao, Xin, Szep, Jeno, Satam, Pratik, Hariri, Salim, Ram, Sundaresh, and Rodriguez, Jeffrey J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives., Comment: 11 pages

Published
2020
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9. Drive-Net: Convolutional Network for Driver Distraction Detection

Author

Majdi, Mohammed S., Ram, Sundaresh, Gill, Jonathan T., and Rodriguez, Jeffery J.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approaches: a recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the methods on a publicly available database of images acquired under a controlled environment containing about 22425 images manually annotated by an expert. Results show that Drive-Net achieves a detection accuracy of 95%, which is 2% more than the best results obtained on the same database using other methods

Published
2020
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12. Conditional Entropy as a Supervised Primitive Segmentation Loss Function

Author

Ram, Sundaresh and Sabuncu, Mert R.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Supervised image segmentation assigns image voxels to a set of labels, as defined by a specific labeling protocol. In this paper, we decompose segmentation into two steps. The first step is what we call "primitive segmentation", where voxels that form sub-parts (primitives) of the various segmentation labels available in the training data, are grouped together. The second step involves computing a protocol-specific label map based on the primitive segmentation. Our core contribution is a novel loss function for the first step, where a primitive segmentation model is trained. The proposed loss function is the entropy of the (protocol-specific) "ground truth" label map conditioned on the primitive segmentation. The conditional entropy loss enables combining training datasets that have been manually labeled with different protocols. Furthermore, as we show empirically, it facilitates an efficient strategy for transfer learning via a lightweight protocol adaptation model that can be trained with little manually labeled data. We apply the proposed approach to the volumetric segmentation of brain MRI scans, where we achieve promising results., Comment: There are errors in the protocol-adaption section and we were unable to regenerate the results

Published
2018

13. Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks

Author

Ram, Sundaresh, Nguyen, Vicky T., Limesand, Kirsten H., and Sabuncu, Mert R.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells. Our model is trained end-to-end from scratch on a mouse parotid salivary gland stem cell nuclei dataset comprising 107 image stacks from three independent cell preparations, each containing several hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets. The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to state-of-the-art and benchmark algorithms. Finally, we use a previously described test-time drop-out strategy to obtain uncertainty estimates on our predictions and validate these estimates by demonstrating that they are strongly correlated with accuracy., Comment: We were not able to reproduce the results

Published
2018

15. Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Author

Cheng, Guang-Shing, Selwa, Katherine E., Hatt, Charles, Ram, Sundaresh, Fortuna, Aleksa B., Guerriero, Margaret, Himelhoch, Ben, McAree, Daniel, Hoffman, Timothy C., Brisson, Joseph, Nazareno, Ryan, Bloye, Kiernan, Johnson, Timothy D., Remberger, Mats, Mattsson, Jonas, Vummidi, Dharshan, Kazerooni, Ella E., Lama, Vibha N., Galban, Stefanie, Boeckh, Michael, Yanik, Gregory A., and Galban, Craig J.

Published
2020
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19. CT-based Machine Learning for Donor Lung Screening Prior to Transplantation

Author

Ram, Sundaresh, primary, Verleden, Stijn E, additional, Kumar, Madhav, additional, Bell, Alexander J., additional, Pal, Ravi, additional, Ordies, Sofie, additional, Vanstapel, Arno, additional, Dubbeldam, Adriana, additional, Vos, Robin, additional, Galban, Stefanie, additional, Ceulemans, Laurens J., additional, Frick, Anna E., additional, Van Raemdonck, Dirk E., additional, Verschakelen, Johny, additional, Vanaudenaerde, Bart M., additional, Verleden, Geert M., additional, Lama, Vibha N, additional, Neyrinck, Arne P., additional, and Galban, Craig J., additional

Published
2023
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20. Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches

Author

David J. Blok, Ronald E. Crump, Ram Sundaresh, Martial Ndeffo-Mbah, Alison P. Galvani, Travis C. Porco, Sake J. de Vlas, Graham F. Medley, and Jan Hendrik Richardus

Subjects
Leprosy, Brazil, Model comparison, LMER, Back-calculation, Compartmental, Individual-based model, Forecast, Infectious and parasitic diseases, RC109-216
Abstract

Background: Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins. Methods: A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012–2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches. Results: Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000. Conclusions: All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated.

Published
2017
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21. Radiomic Feature Spatially Characterizes the Transition of Normal Lung Parenchyma to Small Airways Disease in COPD

Author

Bell, Alexander J., primary, Ram, Sundaresh, additional, Labaki, Wassim W., additional, Hoff, Benjamin A., additional, Wang, Jennifer M., additional, Murray, Susan, additional, Kazerooni, Ella A., additional, Galban, Stefanie, additional, Lynch, David A., additional, Humphries, Stephen M., additional, Martinez, Fernando J., additional, Hatt, Charles R., additional, Han, MeiLan K., additional, and Galban, Craig J., additional

Published
2023
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23. Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS

Author

Wang, Jennifer M., primary, Labaki, Wassim W., additional, Murray, Susan, additional, Martinez, Fernando J., additional, Curtis, Jeffrey L., additional, Hoffman, Eric A., additional, Ram, Sundaresh, additional, Bell, Alexander J., additional, Galban, Craig J., additional, Han, MeiLan K., additional, and Hatt, Charles, additional

Published
2023
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26. CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Author

Wang,Jennifer M, Ram,Sundaresh, Labaki,Wassim W, Han,MeiLan K, and Galbán,Craig J

Subjects
International Journal of Chronic Obstructive Pulmonary Disease
Abstract

Jennifer M Wang,1 Sundaresh Ram,2 Wassim W Labaki,1 MeiLan K Han,1 Craig J Galbán2 1Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA; 2Department of Radiology, University of Michigan, Ann Arbor, MI, USACorrespondence: Craig J Galbán, Department of Radiology, University of Michigan, BSRB, Room A506, 109 Zina Pitcher Place, Ann Arbor, MI, 48109-2200, USA, Tel +1 734-764-8726, Fax +1 734-615-1599, Email cgalban@med.umich.eduAbstract: Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.Keywords: lung disease, medical imaging, phenotyping, clinical, quantitative

Published
2022

27. Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS.

Author

Wang, Jennifer M., Labaki, Wassim W., Murray, Susan, Martinez, Fernando J., Curtis, Jeffrey L., Hoffman, Eric A., Ram, Sundaresh, Bell, Alexander J., Galban, Craig J., Han, MeiLan K., and Hatt, Charles

Subjects
MEDICAL screening, MACHINE learning, CHRONIC obstructive pulmonary disease, OBSTRUCTIVE lung diseases, FORCED expiratory volume
Abstract

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Topological Analysis of the CT Based Parametric Response Map in a Large COPD Population: a COPDGene Study

Author

Bell, Alexander J., primary, Ram, Sundaresh, additional, Labaki, Wassim W., additional, Hoff, Benjamin A., additional, Murray, Susan, additional, Kazerooni, Ella A., additional, Galban, Stefanie, additional, Lynch, David A., additional, Humphries, Steven M., additional, Martinez, Fernando J., additional, Hatt, Charles R., additional, Han, MeiLan K., additional, and Galban, Craig J., additional

Published
2022
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29. Quantitative CT Correlates with Local Inflammation in Lung of Patients with Subtypes of Chronic Lung Allograft Dysfunction

Author

Ram, Sundaresh, primary, Verleden, Stijn E., additional, Bell, Alexander J., additional, Hoff, Benjamin A., additional, Labaki, Wassim W., additional, Murray, Susan, additional, Vanaudenaerde, Bart M., additional, Vos, Robin, additional, Verleden, Geert M., additional, Kazerooni, Ella A., additional, Galbán, Stefanie, additional, Hatt, Charles R., additional, Han, Meilan K., additional, Lama, Vibha N., additional, and Galbán, Craig J., additional

Published
2022
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32. Elastic principal graphs for clinical trajectory analysis in COPD: a COPDGene study

Author

Bell, Alexander, primary, Ram, Sundaresh, additional, Labaki, Wassim, additional, Murray, Susan, additional, Kazerooni, Ella, additional, Galbán, Stefanie, additional, Martinez, Fernando, additional, Hatt, Charles, additional, Mirkes, Evgeny, additional, Zinovyev, Andrei, additional, Gorban, Alexander, additional, Han, Meilan, additional, and Galbán, Craig, additional

Published
2021
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34. Exposure to the US Criminal Legal System and Well-Being: A 2018 Cross-Sectional Study

Author

Emily A Wang, Ram Sundaresh, Youngmin Yi, Carley Riley, Brita Roy, and Christopher Wildeman

Subjects
Adult, Male, medicine.medical_specialty, Adolescent, Cross-sectional study, AJPH Open-Themed Research, 03 medical and health sciences, Social support, Young Adult, Law Enforcement, Environmental health, Criminal Law, medicine, Humans, Family history, Aged, 030505 public health, Public health, Racial Groups, Public Health, Environmental and Occupational Health, Law enforcement, Social Support, Middle Aged, Police, United States, Cross-Sectional Studies, Well-being, Female, Public Health, 0305 other medical science, Psychology
Abstract

Objectives. To assess the association between exposure to the US criminal legal system and well-being. Methods. We used data from the 2018 Family History of Incarceration Survey, a nationally representative cross-sectional study of family incarceration experience (n = 2815), which includes measures of participants’ own criminal legal system exposure, including police stops, arrests, and incarceration. We measured well-being across 5 domains—physical, mental, social, spiritual, and overall life evaluation—and analyzed trends in well-being by criminal legal system exposure using logistic regression. Results. Exposure to police stops, arrests, and incarceration were each associated with lower well-being in every domain compared with those not exposed. Longer durations of incarceration and multiple incarcerations were associated with progressively lower well-being. Those who were stopped and frisked by the police had low well-being similar to that of those who had been incarcerated multiple times. Conclusions. Any exposure to police contact or incarceration is associated with lower well-being in every domain. More involved exposure is associated with even lower well-being. Public Health Implications. Jail diversion and broader criminal justice reform may improve population-level well-being by reducing police contact and incarceration.

Published
2020

35. Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Author

Cheng, Guang‐Shing, Selwa, Katherine E., Hatt, Charles, Ram, Sundaresh, Fortuna, Aleksa B., Guerriero, Margaret, Himelhoch, Ben, McAree, Daniel, Hoffman, Timothy C., Brisson, Joseph, Nazareno, Ryan, Bloye, Kiernan, Johnson, Timothy D., Remberger, Mats, Mattsson, Jonas, Vummidi, Dharshan, Kazerooni, Ella E., Lama, Vibha N., Galban, Stefanie, Boeckh, Michael, Yanik, Gregory A., Galban, Craig J., Cheng, Guang‐Shing, Selwa, Katherine E., Hatt, Charles, Ram, Sundaresh, Fortuna, Aleksa B., Guerriero, Margaret, Himelhoch, Ben, McAree, Daniel, Hoffman, Timothy C., Brisson, Joseph, Nazareno, Ryan, Bloye, Kiernan, Johnson, Timothy D., Remberger, Mats, Mattsson, Jonas, Vummidi, Dharshan, Kazerooni, Ella E., Lama, Vibha N., Galban, Stefanie, Boeckh, Michael, Yanik, Gregory A., and Galban, Craig J.

Abstract

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = −0.236, P = .046; and R = −0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

Published
2020
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36. Improved detection of air trapping on expiratory computed tomography using deep learning

Author

Ram, Sundaresh, primary, Hoff, Benjamin A., additional, Bell, Alexander J., additional, Galban, Stefanie, additional, Fortuna, Aleksa B., additional, Weinheimer, Oliver, additional, Wielpütz, Mark O., additional, Robinson, Terry E., additional, Newman, Beverley, additional, Vummidi, Dharshan, additional, Chughtai, Aamer, additional, Kazerooni, Ella A., additional, Johnson, Timothy D., additional, Han, MeiLan K., additional, Hatt, Charles R., additional, and Galban, Craig J., additional

Published
2021
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39. Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning

Author

Ram, Sundaresh, primary, Hoff, Benjamin A., additional, Bell, Alexander J., additional, Galban, Stefanie, additional, Fortuna, Aleksa B., additional, Weinheimer, Oliver, additional, Wielpütz, Mark O., additional, Robinson, Terry E., additional, Newman, Beverley, additional, Vummidi, Dharshan, additional, Chughtai, Aamer, additional, Kazerooni, Ella A., additional, Johnson, Timothy D., additional, Han, MeiLan K., additional, Hatt, Charles R., additional, and Galban, Craig J., additional

Published
2020
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40. Abstract 3855: Oncogenic Kras-mediated regulation of the tumor microenvironment in lung cancer

Author

Steele, Nina, primary, Abdelmalak, Kristena Y., additional, Ferris, Sarah F., additional, Lee, Jennifer M., additional, Espinoza, Carlos, additional, Zhang, Yaqing, additional, Ram, Sundaresh, additional, Galban, Craig, additional, Ramnath, Nithya, additional, Frankel, Timothy L., additional, di Magliano, Marina Pasca, additional, and Galbán, Stefanie, additional

Published
2020
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44. Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches

Author

Martial L. Ndeffo-Mbah, David J. Blok, Ram Sundaresh, Travis C. Porco, Ronald E. Crump, Graham F. Medley, Alison P. Galvani, Sake J. de Vlas, Jan Hendrik Richardus, and Public Health

Subjects
Mixed model, LMER, Individual-based model, Epidemiology, 030231 tropical medicine, Population, Clinical Sciences, Model fitting, Microbiology, Article, Back calculation, lcsh:Infectious and parasitic diseases, 03 medical and health sciences, 0302 clinical medicine, Models, RA0421, Leprosy, Virology, Statistics, Medicine, Humans, lcsh:RC109-216, 030212 general & internal medicine, education, education.field_of_study, Case detection, Models, Statistical, business.industry, Incidence, Public Health, Environmental and Occupational Health, Model comparison, Statistical, Back-calculation, Compartmental, Good Health and Well Being, Infectious Diseases, Public Health and Health Services, National database, Forecast, Parasitology, business, Brazil, Demography, Forecasting
Abstract

Background\ud \ud Brazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins.\ud \ud Methods\ud \ud A linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012–2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches.\ud \ud Results\ud \ud Each model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000.\ud \ud Conclusions\ud \ud All models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated.

Published
2017
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45. Exposure to Family Member Incarceration and Adult Well-being in the United States

Author

Youngmin Yi, Carley Riley, Christopher Wildeman, Hedwig Lee, Tyler D. Harvey, Ram Sundaresh, Emily A. Wang, and Brita Roy

Subjects
Adult, Male, Adolescent, Population, Young Adult, Social support, Life Expectancy, Humans, Medicine, Family, Family history, education, Original Investigation, Aged, Aged, 80 and over, education.field_of_study, business.industry, Research, Prisoners, Extended family, Life satisfaction, General Medicine, Middle Aged, Mental health, United States, Online Only, Cross-Sectional Studies, Well-being, Life expectancy, Female, Public Health, Family Relations, business, Demography
Abstract

Key Points Question Is the incarceration of a family member associated with well-being and projected life expectancy? Findings In this cross-sectional study including 2815 individuals, any family member incarceration was associated with lower well-being and a projected 2.6-year reduction in life expectancy compared with no family member incarceration experience. Among those with any family incarceration, Black respondents had an estimated 0.5 fewer years of projected life expectancy compared with White respondents. Meaning These findings suggest that efforts to decarcerate may improve population-level health and well-being by reducing racial disparities and detrimental outcomes associated with incarceration for nonincarcerated family members., This cross-sectional study assesses the associations of incarceration of a family member with perceived well-being and projected life expectancy among adults in the US., Importance More than half of the adult population in the United States has ever had a family member incarcerated, an experience more common among Black individuals. The impacts of family incarceration on well-being are not fully understood. Objective To assess the associations of incarceration of a family member with perceived well-being and differences in projected life expectancy. Design, Setting, and Participants This nationally representative cross-sectional study used data from the 2018 Family History of Incarceration Survey to examine how experiences of family member incarceration were associated with a holistic measure of well-being, including physical, mental, social, financial, and spiritual domains. Well-being was used to estimate change in life expectancy and was compared across varying levels of exposure to immediate and extended family member incarceration using logistic regression models to adjust for individual and household characteristics. Data were analyzed from October 2019 to April 2020. Exposures Respondents’ history of family member incarceration, including immediate and extended family members. Main Outcomes and Measures The main outcome was self-reported life-evaluation, a measure of overall well-being from the 100 Million Healthier Lives Adult Well-being Assessment. Respondents were considered thriving with a current life satisfaction score of 7 or greater and a future life optimism score of 8 or greater, each on a scale of 0 to 10. Other outcomes included physical health, mental health, social support, financial well-being, and spiritual well-being, each measured with separate scales. Additionally, life expectancy projections were estimated using population-level correlations with the Life Evaluation Index. All percentages were weighted to more closely represent the US population. Results Of 2815 individuals included in analysis, 1472 (51.7%) were women, 1765 (62.8%) were non-Hispanic White, and 868 (31.5%) were aged 35 to 54 years. A total of 1806 respondents (45.0%) reported having an immediate family member who was incarcerated. Compared with respondents with no family incarceration, any family member incarceration was associated with lower well-being overall (thriving: 69.5% [95% CI, 65.0%-75.0%] vs 56.9% [95% CI, 53.9%-59.9%]) and in every individual domain (eg, physical thriving: 51.1% [95% CI, 46.2-56.0] vs 35.5% [95% CI, 32.6%-38.3%]) and with a mean (SE) estimated 2.6 (0.03) years shorter life expectancy. Among those with any family incarceration, Black respondents had a mean (SE) estimated 0.46 (0.04) fewer years of life expectancy compared with White respondents. Conclusions and Relevance These findings suggest that family member health and well-being may be an important avenue through which incarceration is associated with racial disparities in health and mortality. Decarceration efforts may improve population-level well-being and life expectancy by minimizing detrimental outcomes associated with incarceration among nonincarcerated family members.

Published
2021
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