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Article ; Online: Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.

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

Respiratory research

2024 Volume 25, Issue 1, Page(s) 106

Abstract: Background: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) ... ...

Abstract	Background: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Methods: PRM metrics of normal lung (PRM Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRM Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRM
MeSH term(s)	Humans ; Cross-Sectional Studies ; Pulmonary Disease, Chronic Obstructive/diagnostic imaging ; Lung/diagnostic imaging ; Pulmonary Emphysema ; Emphysema ; Forced Expiratory Volume/physiology
Language	English
Publishing date	2024-02-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2041675-1
ISSN	1465-993X ; 1465-993X
ISSN (online)	1465-993X
ISSN	1465-993X
DOI	10.1186/s12931-024-02729-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression.

Wang, Jennifer M / Bell, Alexander J / Ram, Sundaresh / Labaki, Wassim W / Hoff, Benjamin A / Murray, Susan / Kazerooni, Ella A / Galban, Stefanie / Hatt, Charles R / Han, MeiLan K / Galban, Craig J

Academic radiology

2023 Volume 31, Issue 3, Page(s) 1148–1159

Abstract: Rationale and objectives: Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow ... ...

Abstract	Rationale and objectives: Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. Materials and methods: We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year five to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics, and clinical measures. Results: We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year five emphysema, FT showed rapid local emphysema progression (-57.5% ± 1.1) compared to FD (-49.9% ± 0.5) and ED (-33.1% ± 0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. Conclusion: The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.
MeSH term(s)	Humans ; Pulmonary Emphysema/diagnostic imaging ; Pulmonary Disease, Chronic Obstructive/diagnostic imaging ; Lung/diagnostic imaging ; Emphysema ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2023-09-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1355509-1
ISSN	1878-4046 ; 1076-6332
ISSN (online)	1878-4046
ISSN	1076-6332
DOI	10.1016/j.acra.2023.08.003
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Article ; Online: Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Ram, Sundaresh / Tang, Wenfei / Bell, Alexander J / Pal, Ravi / Spencer, Cara / Buschhaus, Alexander / Hatt, Charles R / diMagliano, Marina Pasca / Rehemtulla, Alnawaz / Rodríguez, Jeffrey J / Galban, Stefanie / Galban, Craig J

Neoplasia (New York, N.Y.)

2023 Volume 42, Page(s) 100911

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 ... ...

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-ras
MeSH term(s)	Animals ; Mice ; Algorithms ; Lung Neoplasms/diagnosis ; Machine Learning ; Treatment Outcome ; Lung
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	1483840-0
ISSN	1476-5586 ; 1522-8002
ISSN (online)	1476-5586
ISSN	1522-8002
DOI	10.1016/j.neo.2023.100911
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Article: Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV

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 / Hatt, Charles

Frontiers in physiology

2023 Volume 14, Page(s) 1144192

Abstract: Purpose: ...

Abstract	Purpose:
Language	English
Publishing date	2023-04-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2023.1144192
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Book ; Online: Increasing Public Awareness of MemoClock to Assist the Elderly in Social Isolation During the COVID-19 Pandemic

Balasubramanian, Akshaj / Bell, Alexander J / Johnson, Caroline M / Henehan, Sophia E

Interactive Qualifying Projects (All Years)

2020

Abstract: MemoClock is a phone and tablet app for care partners to send messages remotely to their loved ones with dementia, but it lacks public awareness. Our project, conducted remotely with MemoClock in Denmark as a result of the COVID-19 pandemic, created a ... ...

Abstract	MemoClock is a phone and tablet app for care partners to send messages remotely to their loved ones with dementia, but it lacks public awareness. Our project, conducted remotely with MemoClock in Denmark as a result of the COVID-19 pandemic, created a marketing strategy to increase MemoClock’s public awareness through analysis of competitors and data from app users. Surveys and coded interviews with MemoClock users provided testimonials for marketing materials. We developed a schedule for social media posts and compiled a list of organizations for MemoClock to engage on social media. Finally, we made recommendations for future app features and marketing.
Keywords	covid19
Publishing date	2020-05-13T07:00:00Z
Publisher	Digital WPI
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.

medRxiv : the preprint server for health sciences

2023

Abstract: Objectives: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) ... ...

Abstract	Objectives: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Materials and methods: PRM metrics of normal lung (PRM Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRM Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRM
Language	English
Publishing date	2023-11-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.26.23290532
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Article ; Online: The Severity of Functional Small Airway Disease in Military Personnel with Constrictive Bronchiolitis as Measured by Quantitative Computed Tomography.

Davis, Caroline W / Lopez, Camden L / Bell, Alexander J / Miller, Robert F / Rabin, Alexander S / Murray, Susan / Falvo, Michael J / Han, MeiLan K / Galban, Craig J / Osterholzer, John J

American journal of respiratory and critical care medicine

2022 Volume 206, Issue 6, Page(s) 786–789

MeSH term(s)	Bronchiolitis/complications ; Bronchiolitis/diagnostic imaging ; Bronchiolitis Obliterans/diagnostic imaging ; Humans ; Military Personnel ; Pulmonary Disease, Chronic Obstructive ; Tomography
Language	English
Publishing date	2022-05-20
Publishing country	United States
Document type	Letter ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202201-0153LE
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Article ; Online: Quantitative CT Correlates with Local Inflammation in Lung of Patients with Subtypes of Chronic Lung Allograft Dysfunction.

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

Cells

2022 Volume 11, Issue 4

Abstract: Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent ... ...

Abstract	Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (
MeSH term(s)	Allografts ; Biomarkers ; Bronchiolitis Obliterans/diagnostic imaging ; Graft vs Host Disease ; Humans ; Inflammation ; Lung/diagnostic imaging ; Lung Transplantation/adverse effects ; Syndrome ; Tomography, X-Ray Computed/methods
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-02-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2661518-6
ISSN	2073-4409 ; 2073-4409
ISSN (online)	2073-4409
ISSN	2073-4409
DOI	10.3390/cells11040699
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Article: CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.

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

medRxiv : the preprint server for health sciences

2023

Abstract: Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated ... ...

Abstract	Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.
Language	English
Publishing date	2023-03-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.28.23287705
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Article ; Online: Computed tomography-based machine learning for donor lung screening before transplantation.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation

2023 Volume 43, Issue 3, Page(s) 394–402

Abstract: Background: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) ... ...

Abstract	Background: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. Methods: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.
MeSH term(s)	Humans ; Lung/diagnostic imaging ; Lung Transplantation ; Machine Learning ; Prospective Studies ; Tissue Donors ; Tomography, X-Ray Computed ; Clinical Trials as Topic
Language	English
Publishing date	2023-09-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	1062522-7
ISSN	1557-3117 ; 1053-2498
ISSN (online)	1557-3117
ISSN	1053-2498
DOI	10.1016/j.healun.2023.09.018
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