Association of FVC/DLCO with pulmonary hypertension risk and interstitial disease in systemic sclerosis patients

Vallejos, E; Martinez, John; Cabrera, Florencia; Mastandrea, Noelia; Pertuz, Milerna; Marengo, Zullie; Meiller, María José López; Volberg, Verónica I.; Tejada, Ricardo Gómez

doi:10.1183/13993003.congress-2020.302

Cited by 1 publication

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“…This can be attributed to the challenge of estimating reference equations for diverse population groups. Therefore we proceeded our research on estimating absolute PFTs, because 1) estimating PFTs%pred indirectly was more accurate than a direct estimation; and 2) this approach is more flexible as other PFT biomarkers, such as FEV 1 /FVC [1] and FVC/DLCO [30], can then also be derived from the estimated absolute PFT values.…”

Section: H Multiple Variable Regression Analysis (Q7)mentioning

confidence: 99%

Automatic Pulmonary Function Estimation From Chest CT Scans Using Deep Regression Neural Networks: The Relation Between Structure and Function in Systemic Sclerosis

Jia,

Marges,

De Vries-Bouwstra

et al. 2023

IEEE Access

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Pulmonary function tests (PFTs) play an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFTs due to contraindications. In addition, it is unclear how lung function is affected by changes in lung structure in SSc. Therefore, this study aims to explore the potential of automatically estimating PFT results from chest CT scans of SSc patients and how different regions influence the estimation of PFTs. Deep regression networks were developed with transfer learning to estimate PFTs from 316 SSc patients. Segmented lungs and vessels were used to mask the CT images to train the network with different inputs: from entire CT scan, lungs-only to vessels-only. The network trained on entire CT scans with transfer learning achieved an ICC of 0.71, 0.76, 0.80, and 0.81 for the estimation of DLCO, FEV 1 , FVC and TLC, respectively. The performance of the networks gradually decreased when trained on data from lungs-only and vessels-only. Regression attention maps showed that regions close to large vessels were highlighted more than other regions, and occasionally regions outside the lungs were highlighted. These experiments show that apart from the lungs and large vessels, other regions contribute to PFT estimation. In addition, adding manually designed biomarkers increased the correlation (R) from 0.75, 0.74, 0.82, and 0.83 to 0.81, 0.83, 0.88, and 0.90, respectively. This suggests that that manually designed imaging biomarkers can still contribute to explaining the relation between lung function and structure.

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Section: H Multiple Variable Regression Analysis (Q7)mentioning

confidence: 99%