scholarly journals Medical versus Surgical Treatment of a Primary Tuberculous Pleural Peel

2001 ◽  
Vol 8 (6) ◽  
pp. 449-453 ◽  
Author(s):  
Richard Long ◽  
Anne Fanning ◽  
Ciaran McNamee ◽  
James Barrie ◽  
Lakshmi Puttagunta
Keyword(s):  
Computed Tomography ◽  
Drug Therapy ◽  
Surgical Treatment ◽  
Pulmonary Function ◽  
Present Report ◽  
Pulmonary Function Tests ◽  
Response To Treatment ◽  
Lung Expansion ◽  
Computed Tomography Scans ◽  
Antituberculous Drug

The role and timing of surgical decortication in the management of a primary tuberculous pleural peel remains controversial. The present report describes the case of a young man with an extensive primary tuberculous pleural peel that responded dramatically to medical therapy. A serious attempt at surgical decortication three weeks into antituberculous drug therapy may have removed some compressive aspects of the peel, facilitating lung expansion. However, it had almost no measurable impact on the size of peel and was technically very difficult. Response to treatment was measured anatomically (computed tomography scans) and physiologically (pulmonary function tests).

scholarly journals Computed tomography findings as determinants of pulmonary function tests in fibrotic interstitial lung diseases—Network-analyses and multivariate models

2020 ◽  
Vol 17 ◽  
pp. 147997312096702
Author(s):  
David Lang ◽  
Kaveh Akbari ◽  
Stefan Walcherberger ◽  
Benedikt Hergan ◽  
Andreas Horner ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Information Criterion ◽  
Forced Expiratory Volume ◽  
Interstitial Lung Diseases ◽  
High Resolution Computed Tomography ◽  
Diffusion Capacity ◽  
Network Analyses ◽  
The Impact

The aim was to evaluate the impact of multiple high-resolution computed tomography (HRCT) features on pulmonary function test (PFT) biomarkers in fibrotic interstitial lung disease (FILD) patients. HRCT of subsequently ILD-board-discussed FILD patients were semi-quantitatively evaluated in a standardized approach: 18 distinct lung regions were scored for noduli, reticulation, honeycombing, consolidations, ground glass opacities (GGO), traction bronchiectasis (BRK) and emphysema. Total lung capacity (TLC), forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC, diffusion capacity for carbon monoxide (DLCO) and transfer coefficient (KCO) were assessed. Interactions between each PFT biomarker and all HRCT scores were visualized by network analyses, modeled according to the Schwarz Bayesian Information Criterion and incorporated in uni- and multivariate stepwise regression analyses. Among 108 FILD patients (mean age 67 years, 77% male), BRK extent was a major significant uni- or multivariate determinant of all PFT analyzed. Besides that, diffusion-based variables DLCO and KCO showed a larger dependency on reticulation, emphysema and GGO, while forced expiratory volume-based measures FEV1, FVC and FEV1/FVC were more closely associated with consolidations. For TLC, the only significant multivariate determinant was reticulation. In conclusion, PFT biomarkers derived from spirometry, body plethysmography and diffusion capacity in FILD patients are differentially influenced by semi-quantified HRCT findings.

scholarly journals Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Joshua Gawlitza ◽  
Timo Sturm ◽  
Kai Spohrer ◽  
Thomas Henzler ◽  
Ibrahim Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Function ◽  
Pulmonary Function ◽  
Polynomial Regression ◽  
Pulmonary Function Tests ◽  
Quantitative Computed Tomography ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Function Tests

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

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