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Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

ABSTRACT: BACKGROUND:Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. OBJECTIVE:The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. METHODS:We performed an imaging-based cluster analysis using quantitative computed tomography-based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables. RESULTS:Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively. CONCLUSIONS:Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies.

SUBMITTER: Choi S

PROVIDER: S-EPMC5534190 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

Choi Sanghun S Hoffman Eric A EA Wenzel Sally E SE Castro Mario M Fain Sean S Jarjour Nizar N Schiebler Mark L ML Chen Kun K Lin Ching-Long CL

The Journal of allergy and clinical immunology 20170129 3

<h4>Background</h4>Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects.<h4>Objective</h4>The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics.<h4>Methods</h4>We performed an imaging-based cluster analysis using quantitative computed tomo ...[more]

PMID: 28143694

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Project description:ImportanceSquamous cell carcinoma (SCC) of the oral cavity is one of the most common tumor entities worldwide. Precise initial staging is necessary to determine a diagnosis, treatment, and prognosis.ObjectiveTo examine the diagnostic accuracy of preoperative 18-F fluorodeoxyglucose (FDG) positron emission tomographic/computed tomographic (PET/CT) imaging in detecting cervical lymph node metastases.Design, setting, and participantsThis prospective diagnostic study was performed at a single tertiary reference center between June 1, 2013, and January 31, 2016. Data were analyzed from April 7, 2018, through May 31, 2019. Observers of the FDG PET/CT imaging were blinded to patients' tumor stage. A total of 150 treatment-naive patients with clinical suspicion of SCC of the oral cavity were enrolled.ExposuresAll patients underwent FDG PET/CT imaging before local tumor resection with selective or complete neck dissection.Main outcomes and measuresThe accuracy of FDG PET/CT in localizing primary tumor, lymph node, and distant metastases was tested. Histopathologic characteristics of the tissue samples served as the standard of reference.ResultsOf the 150 patients enrolled, 135 patients (74 [54.8%] men) with a median age of 63 years (range, 23-88 years) met the inclusion criteria (histopathologically confirmed primary SCC of the oral cavity/level-based histopathologic assessment of the resected lymph nodes). Thirty-six patients (26.7%) in the study cohort had neck metastases. Use of FDG PET/CT detected cervical lymph node metastasis with 83.3% sensitivity (95% CI, 71.2%-95.5%) and 84.8% specificity (95% CI, 77.8%-91.9%) and had a negative predictive value of 93.3% (95% CI, 88.2%-98.5%). The specificity was higher than for contrast-enhanced cervical CT imaging (67.0%; 95% CI, 57.4%-76.7%; P < .01) and cervical magnetic resonance imaging (62.6%; 95% CI, 52.7%-72.6%; P < .001). Ipsilateral lymph node metastasis in left- or right-sided primary tumor sites was detected with 78.6% sensitivity (95% CI, 63.4%-93.8%) and 83.1% specificity (95% CI, 75.1%-91.2%), and contralateral metastatic involvement was detected with 66.7% sensitivity (95% CI, 28.9%-100.0%) and 98.6% specificity (95% CI, 95.9%-100.0%). No distant metastases were observed.Conclusions and relevanceIn this study, FDG PET/CT imaging had a high negative predictive value in detecting cervical lymph node metastasis in patients with newly diagnosed, treatment-naive SCC of the oral cavity. Routine clinical use of FDG PET/CT might lead to a substantial reduction of treatment-related morbidity in most patients.

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Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

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Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

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