High-Resolution CT Findings in Interstitial Lung Disease Associated with Connective Tissue Diseases: Differentiating Patterns for Clinical Practice—A Systematic Review with Meta-Analysis
Notice bibliographique
Résumé
Objectives: Connective tissue diseases (CTDs) include a diverse group of systemic autoimmune conditions, among which interstitial lung disease (ILD) is acknowledged as a major determinant of prognosis. High-resolution computed tomography (HRCT) is the gold standard for ILD assessment. The distribution of HRCT patterns across CTDs remain incompletely defined. The objective of this systematic review is to synthesize available evidence regarding the prevalence of specific radiological patterns within CTD-ILDs and to assess whether specific patterns occur at different frequencies among individual CTDs. Methods: The inclusion criteria encompassed original human studies published in English between 2015 and 2024, involving adult participants (≥18 years) with CTD-ILDs assessed primarily by HRCT and designed as retrospective, prospective, or cross-sectional trials with extractable data. We systematically searched PubMed, Scopus, and Web of Science (January 2025). Risk of bias was evaluated using the Newcastle–Ottawa Scale (NOS) for cohort and case–control studies, and the JBI Critical Appraisal Checklist for cross-sectional studies. Data were extracted and categorized by HRCT pattern for each CTD, and then summarized descriptively and statistically. Results: We analyzed 23 studies published between 2015 and 2024, which included 2020 patients with CTD-ILDs. The analysis revealed non-specific interstitial pneumonia (NSIP) as the most prevalent pattern overall (36.5%), followed by definite usual interstitial pneumonia (UIP) (24.8%), organizing pneumonia (OP) (9.8%) and lymphoid interstitial pneumonia (LIP) (1.25%). HRCT distribution varied by CTD: NSIP predominated in systemic sclerosis, idiopathic inflammatory myopathies, and mixed connective tissue disease; UIP was most frequent in rheumatoid arthritis; LIP was more common in Sjögren’s syndrome. While global differences were statistically significant, pairwise comparisons often lacked significance, likely due to sample size constraints. Discussion: Limitations include varying risk of bias across study designs, heterogeneity in HRCT reporting, small sample sizes, and inconsistent follow-up, which may reduce precision and generalizability. In addition to the quantitative synthesis, this review offers a detailed description of each radiologic pattern mentioned above, illustrated by representative examples to support the recognition in clinical settings. Furthermore, it includes a brief overview of the major CTDs associated with ILD, summarizing their epidemiological data, risk factors for ILD and clinical presentation and diagnostic recommendations. Conclusions: NSIP emerged as the most common HRCT pattern across CTD-ILDs, with UIP predominating in RA. Although inter-disease differences were observed, statistical significance was limited, likely reflecting sample size constraints. These findings emphasize the diagnostic and prognostic relevance of HRCT pattern recognition and highlight the need for larger, standardized studies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,035 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».