Generative Method to Discover Genetically Driven Image Biomarkers

Abstract. We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort

Inverse Consistency by Construction for Multistep Deep Registration

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency

Automated Axial Right Ventricle to Left Ventricle Diameter Ratio Computation in Computed Tomography Pulmonary Angiography

Background and Purpose Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE. Materials and Methods The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant. Results The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75. Conclusions An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.Madrid-MIT M+Vision Consortiu

Distinct emphysema subtypes defined by quantitative CT analysis are associated with specific pulmonary matrix metalloproteinases

BACKGROUND: Emphysema is characterised by distinct pathological sub-types, but little is known about the divergent underlying aetiology. Matrix-metalloproteinases (MMPs) are proteolytic enzymes that can degrade the extracellular matrix and have been identified as potentially important in the development of emphysema. However, the relationship between MMPs and emphysema sub-type is unknown. We investigated the role of MMPs and their inhibitors in the development of emphysema sub-types by quantifying levels and determining relationships with these sub-types in mild-moderate COPD patients and ex/current smokers with preserved lung function.METHODS: Twenty-four mild-moderate COPD and 8 ex/current smokers with preserved lung function underwent high resolution CT and distinct emphysema sub-types were quantified using novel local histogram-based assessment of lung density. We analysed levels of MMPs and tissue inhibitors of MMPs (TIMPs) in bronchoalveolar lavage (BAL) and assessed their relationship with these emphysema sub-types.RESULTS: The most prevalent emphysema subtypes in COPD subjects were mild and moderate centrilobular (CLE) emphysema, while only small amounts of severe centrilobular emphysema, paraseptal emphysema (PSE) and panlobular emphysema (PLE) were present. MMP-3, and -10 associated with all emphysema sub-types other than mild CLE, while MMP-7 and -8 had associations with moderate and severe CLE and PSE. MMP-9 also had associations with moderate CLE and paraseptal emphysema. Mild CLE occurred in substantial quantities irrespective of whether airflow obstruction was present and did not show any associations with MMPs.CONCLUSION: Multiple MMPs are directly associated with emphysema sub-types identified by CT imaging, apart from mild CLE. This suggests that MMPs play a significant role in the tissue destruction seen in the more severe sub-types of emphysema, whereas early emphysematous change may be driven by a different mechanism.TRIAL REGISTRATION: Trial registration number NCT01701869

Optimizing parameters of an open-source airway segmentation algorithm using different CT images.

Background: Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters. Methods: In this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT’09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered. Results: All the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams’ methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation. Conclusion: The system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm

Relative Predictive Value of Circulating Immune Markers in US Adults Without Cardiovascular Disease: Implications for Risk Reclassification

OBJECTIVE: To investigate the relative predictive value of circulating immune cell markers for cardiovascular mortality in ambulatory adults without cardiovascular disease. METHODS: We analyzed data of participants enrolled in the National Health and Nutrition Examination Survey from January 1, 1999, to December 31, 2010, with the total leukocyte count within a normal range (4000-11,000 cells/μL [to convert to cells ×10 RESULTS: Among 21,599 participants eligible for this analysis, the median age was 47 years (interquartile range, 34-63 years); 10,651 (49.2%) participants were women, and 10,713 (49.5%) were self-reported non-Hispanic white. During a median follow-up of 9.6 years (interquartile range, 6.8-13.1 years), there were 627 cardiovascular deaths. MLR had the best predictive value for cardiovascular mortality. The addition of elevated MLR (≥0.3) to the 10-year ASCVD risk score improved the classification by 2.7%±1.4% (P=.04). Elevated MLR had better predictive value than C-reactive protein and several components of the 10-year ASCVD risk score. CONCLUSION: Among ambulatory US adults without preexisting cardiovascular disease, we found that MLR had the best predictive value for cardiovascular mortality among circulating immune markers. The addition of MLR to the 10-year risk score significantly improved the risk classification of participants

Differences in Respiratory Symptoms and Lung Structure Between Hispanic and Non-Hispanic White Smokers: A Comparative Study

Background: Prior studies have demonstrated that U.S. Hispanic smokers have a lower risk of decline in lung function and chronic obstructive pulmonary disease (COPD) compared with non-Hispanic whites (NHW). This suggests there might be racial-ethnic differences in susceptibility in cigarette smoke-induced respiratory symptoms, lung parenchymal destruction, and airway and vascular disease, as well as in extra-pulmonary manifestations of COPD. Therefore, we aimed to explore respiratory symptoms, lung function, and pulmonary and extra-pulmonary structural changes in Hispanic and NHW smokers. Methods: We compared respiratory symptoms, lung function, and computed tomography (CT) measures of emphysema-like tissue, airway disease, the branching generation number (BGN) to reach a 2-mm-lumen-diameter airway, and vascular pruning as well as muscle and fat mass between 39 Hispanic and 39 sex-, age- and smoking exposure-matched NHW smokers. Results: Hispanic smokers had higher odds of dyspnea than NHW after adjustment for COPD and asthma statuses (odds ratio[OR] = 2.96; 95% confidence interval [CI] 1.09-8.04), but no significant differences were found in lung function and CT measurements. Conclusions: While lung function and CT measures of the lung structure were similar, dyspnea is reported more frequently by Hispanic than matched-NHW smokers. It seems to be an impossible puzzle but it's easy to solve a Rubik' Cube using a few algorithms

B cell–adaptive immune profile in emphysema-predominant chronic obstructive pulmonary disease

Cigarette smoke, the major risk factor for COPD in developed countries, causes pulmonary inflammation that persists long after smoking cessation, suggesting self-perpetuating adaptive immune responses similar to those that occur in autoimmune diseases. Increases in the number and size of B cell–rich lymphoid follicles (LFs) have been shown in patients in severe stages of COPD (4), and increased B-cell products (autoantibodies) have been observed in the blood and lungs of patients with COPD (5, 6). Oligoclonal rearrangement of the immunoglobulin genes has been observed in B cells isolated from COPD LFs, suggesting that a specific antigenic stimulation drives B-cell proliferation. Consistently, we have shown that in the COPD lung, there is an overexpression of BAFF (B-cell activation factor of the TNF family), which is a key regulator of B-cell homeostasis in several autoimmune diseases (7) and is involved in the growth of LFs in COPD. However, a network analysis of lung transcriptomics showed that a prominent B-cell molecular signature characterized emphysema preferentially but was absent in AD independently of the degree of airflow limitation (8). In the current study, we investigated the correlation between B-cell responses in lung tissue from patients with COPD and healthy smokers, and the extent of emphysema versus airflow limitation

A novel spirometric measure identifies mild COPD unidentified by standard criteria

BACKGROUND: In chronic obstructive pulmonary disease, both smaller and larger airways are affected. FEV1 mainly reflects large airways obstruction, while the later fraction of forced exhalation reflects reduction in terminal expiratory flow. In this study, the objective was to evaluate the relationship between spirometric ratios, including the ratio of forced expiratory volume in 3 and 6 seconds (FEV3/FEV6), and small airways measures and gas trapping at quantitative chest CT scanning, and clinical outcomes in the Genetic Epidemiology of COPD (COPDGene) cohort. METHODS: Seven thousand eight hundred fifty-three current and ex-smokers were evaluated for airflow obstruction by using recently defined linear iteratively derived equations of Hansen et al to determine lower limit of normal (LLN) equations for prebronchodilator FEV1/FVC, FEV1/FEV6, FEV3/FEV6, and FEV3/FVC. General linear and ordinal regression models were applied to the relationship between prebronchodilator spirometric and radiologic and clinical data. RESULTS: Of the 10,311 participants included in the COPDGene phase I study, participants with incomplete quantitative CT scanning or relevant spirometric data were excluded, resulting in 7,853 participants in the present study. Of 4,386 participants with FEV1/FVC greater than or equal to the LLN, 15.4% had abnormal FEV3/FEV6. Compared with normal FEV3/FEV6 and FEV1/FVC, abnormal FEV3/FEV6 was associated with significantly greater gas trapping; St. George's Respiratory Questionnaire score; modified Medical Research Council dyspnea score; and BMI, airflow obstruction, dyspnea, and exercise index and with shorter 6-min walking distance (all P < .0001) but not with CT scanning evidence of emphysema. CONCLUSIONS: Current and ex-smokers with prebronchodilator FEV3/FEV6 less than the LLN as the sole abnormality identifies a distinct population with evidence of small airways disease in quantitative CT scanning, impaired indexes of physical function and quality of life otherwise deemed normal by using the current spirometric definition.United States Department of Health & Human Services - R01 HL 08 9856 - R01 HL 08 9897National Institutes of Health (NIH) - USA - 1KL2TR001419NIH National Heart Lung & Blood Institute (NHLBI) - UL1TR001417 - KL2TR001419 - UL1TR001881NIH National Center for Advancing Translational Sciences (NCATS) - U01HL089897 - U01HL089856 - R01HL124233 - R01HL089856 - R01HL08989

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte