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Related Experiment Video

Updated: Jan 13, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

Interstitial Lung Diseases Presenting as Small Nodules: Imaging Phenotypes.

Palmi Shah¹, Maham Jehangir¹, Mohamed Hussein¹

¹Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL.

Seminars in Roentgenology

|January 9, 2026

Summary

High-Resolution Computed Tomography (HRCT) helps diagnose interstitial lung diseases (ILDs) by analyzing small lung nodules. Nodules are classified into perilymphatic, centrilobular, and random patterns to identify specific ILDs.

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Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

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Area of Science:

Radiology
Pulmonology
Pathology

Background:

Interstitial lung disease (ILD) involves lung inflammation and scarring, leading to functional decline.
High-Resolution Computed Tomography (HRCT) is essential for diagnosing ILDs.
Small lung nodules (<10 mm) on HRCT require careful characterization for accurate diagnosis.

Purpose of the Study:

To categorize ILD presentations based on small lung nodules identified via HRCT.
To establish imaging phenotypes for small nodules to aid differential diagnosis.
To correlate nodule distribution patterns with specific ILD conditions.

Main Methods:

Analysis of small lung nodules (<10 mm) on HRCT images.
Classification of nodules based on size, distribution, borders, attenuation, and evolution.
Categorization into three primary distribution patterns: perilymphatic, centrilobular, and random.

Main Results:

Perilymphatic nodules are associated with lymphatic involvement, seen in Sarcoidosis, occupational lung diseases (CWP, silicosis), and others.
Centrilobular nodules, categorized as nonbranching or branching ('tree-in-bud'), are linked to conditions like hypersensitivity pneumonitis (HP) and respiratory bronchiolitis (RB).
Random nodule distribution typically suggests hematogenous infections or miliary metastases, though profuse perilymphatic nodules can mimic this pattern.

Conclusions:

HRCT-based nodule characterization, particularly distribution patterns, is crucial for diagnosing ILDs.
The perilymphatic, centrilobular, and random phenotypes provide a framework for differentiating various ILD causes.
Understanding these imaging phenotypes aids in narrowing diagnostic possibilities for patients with small lung nodules on HRCT.