Evidence map›Paper›PMID 31940114›Full record

ArticleClinical rheumatology2020

Assessment of interstitial lung disease in systemic sclerosis using the quantitative CT algorithm CALIPER.

Alessandro Maria Ferrazza, Antonietta Gigante, Maria Ludovica Gasperini, Rosa Maria Ammendola, Gregorino Paone, Iacopo Carbone, Edoardo Rosato

Abstract read
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In one paragraph

Article in Clinical rheumatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
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  6. Review
  7. Article
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  13. Type 2 cytokines and scleroderma interstitial lung disease.Clinical and experimental medicine · 2023
    Article
  14. Review
  15. Article
  16. Towards Treatable Traits for Pulmonary Fibrosis.Journal of personalized medicine · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Alessandro Maria FerrazzaDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Rome, Italy.
Antonietta GiganteDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Maria Ludovica GasperiniDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Rosa Maria AmmendolaDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Rome, Italy.
Gregorino PaoneDepartment of Cardiovascular and Respiratory Sciences, Sapienza University of Rome, Rome, Italy.
Iacopo CarboneDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Rome, Italy.
Edoardo RosatoDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy. edoardo.rosato@uniroma1.it.ORCID https://orcid.org/0000-0002-7417-8093
Sapienza University of Rome · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interstitial lung disease (ILD) remains a major cause of morbidity and mortality in systemic sclerosis (SSc). Study aim is to characterize and quantify SSc-ILD by using Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER). Secondly, our objective is to evaluate which radiological pattern is predictive of lung function decline at 12 months follow-up. In the prospective study (IRB 5435), 66 SSc patients underwent high-resolution computerized tomography (HRCT) at baseline. HRCT was performed according to standard protocol using a CT 64GE light speed VCT power scanner. CALIPER classified lung parenchyma on volume units. Every volume unit was classified into radiological parenchymal patterns (honeycombing, reticular and ground glass). Pulmonary function tests (PFTs) were performed at baseline and after 12 months of follow-up. Cigarette smoking and other lung diseases unrelated to SSc are exclusion criteria. CALIPER analysis showed normal lung parenchyma 87.4 ± 9.8%, ground glass 2.8 ± 5.3%, reticular 4 ± 5.7%, and honeycombing 1 ± 1%. In multiple regression analysis, FEV1 (p < 0.0001), FVC (p = 0.001), and DLCO (p < 0.0001) measurements at baseline showed a negative correlation with the reticular pattern percentage. At follow-up, DLCO reduction showed a positive correlation (p < 0.001) with the percentage of ground glass pattern (r = 0.33, beta coefficient = 0.51). In the ROC curve analysis, ground glass score is a good predictor (0.75, p = 0.009; 95% CI 0.59-0.91) of DLCO worsening, defined as a decrease of more than 10% of DLCO. Using a cutoff ≥ 4.5 for ground glass score, the RR for DLCO worsening is 6.8 (p < 0.01; 95% CI 1.6-29.2). The results of this study show that CALIPER is useful not only for quantifying lung damage but also for assessing worsening PFTs, but larger studies are needed to confirm these preliminary data.Key Points• At baseline reticular pattern showed negative correlation with PFTs• At follow-up ground glass pattern predicts worsening of DLCO• CALIPER is a useful to quantify lung damage.

Indexed as

SoftwareAdultAgedAlgorithmsFemaleHumansImage Processing, Computer-AssistedLungLung Diseases, InterstitialMaleMiddle AgedProspective StudiesRegression AnalysisRespiratory Function TestsROC CurveScleroderma, SystemicCALIPERHRCTInterstitial lung diseasePulmonary function testsSystemic sclerosis

Identifiers

PMID31940114
OpenAlexW2999922148

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.