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Micronodular lung disease on high-resolution CT: patterns and differential diagnosis

医学 鉴别诊断 病理 肺 放射科 结节病 高分辨率计算机断层扫描 淋巴系统 结核(地质) 计算机断层摄影术 古生物学 生物 内科学
作者
J. Kim,Borna E. Dabiri,Mark M. Hammer
出处
期刊:Clinical Radiology [Elsevier BV]
卷期号:76 (6): 399-406 被引量:31
标识
DOI:10.1016/j.crad.2020.12.025
摘要

With the advent of high-resolution computed tomography (HRCT), micronodular lung disease is a routinely encountered pathology in thoracic imaging. This article will review how to differentiate the three main micronodular patterns and review the differential diagnosis for each. Differential diagnosis of micronodular lung disease may be extensive, but by identifying the pattern and using additional clues, such as distribution, additional imaging findings, and clinical history, a radiologist can make an accurate diagnosis. First, three micronodular patterns — centrilobular, peri-lymphatic, and random — can be identified by using a simple algorithm based on the location of nodules. This algorithm requires understanding of the anatomy and function of the secondary pulmonary lobule. Each micronodular pattern offers a unique differential diagnosis. Centrilobular nodules can be seen with inflammatory, infectious, or vascular aetiologies; peri-lymphatic nodules with sarcoidosis and lymphangitic carcinomatosis; and random nodules with haematogenous metastases or infections. With the advent of high-resolution computed tomography (HRCT), micronodular lung disease is a routinely encountered pathology in thoracic imaging. This article will review how to differentiate the three main micronodular patterns and review the differential diagnosis for each. Differential diagnosis of micronodular lung disease may be extensive, but by identifying the pattern and using additional clues, such as distribution, additional imaging findings, and clinical history, a radiologist can make an accurate diagnosis. First, three micronodular patterns — centrilobular, peri-lymphatic, and random — can be identified by using a simple algorithm based on the location of nodules. This algorithm requires understanding of the anatomy and function of the secondary pulmonary lobule. Each micronodular pattern offers a unique differential diagnosis. Centrilobular nodules can be seen with inflammatory, infectious, or vascular aetiologies; peri-lymphatic nodules with sarcoidosis and lymphangitic carcinomatosis; and random nodules with haematogenous metastases or infections.
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