Diagnostic approach to interstitial lung disease

Gogali, Athena; Wells, Athol U.

doi:10.1007/s13665-012-0029-6

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Specifically, an acute exacerbation of their lung disease is an uncommon but potentially fatal complication following biopsy [2]. Surgical lung biopsy can be obviated when the clinical and radiographic impression are typical of UIP [7, 8, 9, 10]. However, atypical appearances of UIP are common, accounting for 30% to 50% of patients [2, 11].…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Depeursinge¹,

Chin

Leung

et al. 2015

Investigative Radiology

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Objectives We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). Materials and Methods 33 patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of two thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence–based guidelines for CT diagnosis of UIP. Two cardiothoracic fellows with one year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple detector CT images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multi-directional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. Results We compared the predictions based on regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the two fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. Conclusions Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.

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Section: Introductionmentioning

confidence: 99%

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Depeursinge¹,

Chin

Leung

et al. 2015

Investigative Radiology

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“…In this study, we addressed three questions: First, can we identify lung pattern types associated with radiological disease progression in addition to those known and named such as ground glass opacities? This is relevant, since known patterns have limited power in reliably diagnosing fibrosing lung diseases [ 30 , 31 ]. Consequently, data-driven means to expand our disease marker vocabulary could contribute to improving the diagnostic and prognostic capability of imaging.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised machine learning identifies predictive progression markers of IPF

et al. 2022

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Objectives To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome. Methods We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center. Results Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort. Conclusions Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. Key Points • Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort.

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“…The diagnostic approach includes: clinical history and examination, pulmonary function tests, selective blood tests, imaging examinations: chest radiography and high resolution computed tomography, bronchoscopy with bronchoalveolar lavage (BAL) and/or endobronchial/transbronchial biopsies and/or a surgical biopsy [4].…”

Section: Introductionmentioning

confidence: 99%

Is It Possible to Predict Whether BAL Salvage Is Going to Be Diagnostic?

Skoczyński

Tobiczyk

Minarowski

et al. 2019

Advances in Respiratory Medicine

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Introduction: Bronchoalveolar lavage (BAL) is used in the diagnosis of interstitial lung diseases. BAL is diagnostic when ≥ 60% of the instilled volume is recovered. There are no reliable markers useful to predict whether BAL volume is going to be diagnostic. Our goal was to search for pulmonary function markers which could anticipate whether the recovered volume of instilled fluid would be ≥ 60% of administered volume. Material and methods: BAL volumes and quality were analyzed in the context of disease, medical condition and lung function test results of the subjects hospitalized at the Pulmonology Ward from January 2015 to October 2016. The patients' average age was 61 (29-89). Results: Among 80 procedures, diagnostic BAL (≥ 60%) has been obtained in 58 cases. The analysis of the group of patients with an interstitial lung disease confirmed that there is a correlation between decreasing BAL recovered volume and an increase of RV[%pred] (r =-0.34) and RV/TLC[%pred] (r =-0.41); p < 0.05. There was no significant correlation with DLCO. RV/TLC[%pred] was the parameter with the highest predictive value for an anticipated correct BAL recovery. The curve analysis of the receiver operating characteristic (ROC) showed a diagnostic accuracy (AUC 0.73, 95% CI 0.61-0.86). Conclusions: Pulmonary hyperinflation may have a predictive role in anticipating a proper recovery of the BAL fluid volume.

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Diagnostic approach to interstitial lung disease

Cited by 5 publications

References 45 publications

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Unsupervised machine learning identifies predictive progression markers of IPF

Is It Possible to Predict Whether BAL Salvage Is Going to Be Diagnostic?

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