Fukuda Taiki, Egashira Ryoko, Ueno Midori, Hashisako Mikiko, Sumikawa Hiromitsu, Tominaga Junya, Yamada Daisuke, Fukuoka Junya, Misumi Shigeki, Ojiri Hiroya, Hatabu Hiroto, Johkoh Takeshi
Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-Ku, Tokyo, 105-8461, Japan.
Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga-City, Saga, 849-8501, Japan.
Insights Imaging. 2023 Oct 20;14(1):177. doi: 10.1186/s13244-023-01501-x.
High-attenuation pulmonary abnormalities are commonly seen on CT. These findings are increasingly encountered with the growing number of CT examinations and the wide availability of thin-slice images. The abnormalities include benign lesions, such as infectious granulomatous diseases and metabolic diseases, and malignant tumors, such as lung cancers and metastatic tumors. Due to the wide spectrum of diseases, the proper diagnosis of high-attenuation abnormalities can be challenging. The assessment of these abnormal findings requires scrutiny, and the treatment is imperative. Our proposed stepwise diagnostic algorithm consists of five steps. Step 1: Establish the presence or absence of metallic artifacts. Step 2: Identify associated nodular or mass-like soft tissue components. Step 3: Establish the presence of solitary or multiple lesions if identified in Step 2. Step 4: Ascertain the predominant distribution in the upper or lower lungs if not identified in Step 2. Step 5: Identify the morphological pattern, such as linear, consolidation, nodular, or micronodular if not identified in Step 4. These five steps to diagnosing high-attenuation abnormalities subdivide the lesions into nine categories. This stepwise radiologic diagnostic approach could help to narrow the differential diagnosis for various pulmonary high-attenuation abnormalities and to achieve a precise diagnosis.Critical relevance statement Our proposed stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities may help to recognize a variety of those high-attenuation findings, to determine whether the associated diseases require further investigation, and to guide appropriate patient management. Key points • To provide a stepwise diagnostic approach to high-attenuation pulmonary abnormalities.• To familiarize radiologists with the varying cause of high-attenuation pulmonary abnormalities.• To recognize which high-attenuation abnormalities require scrutiny and prompt treatment.
高衰减肺部异常在CT上很常见。随着CT检查数量的增加和薄层图像的广泛应用，这些发现越来越多地被遇到。这些异常包括良性病变，如感染性肉芽肿疾病和代谢性疾病，以及恶性肿瘤，如肺癌和转移性肿瘤。由于疾病谱广泛，对高衰减异常进行正确诊断可能具有挑战性。对这些异常发现的评估需要仔细审查，且治疗势在必行。我们提出的逐步诊断算法包括五个步骤。步骤1：确定是否存在金属伪影。步骤2：识别相关的结节状或肿块样软组织成分。步骤3：如果在步骤2中识别出病变，确定是单发还是多发。步骤4：如果在步骤2中未识别出病变，确定在上肺还是下肺的主要分布。步骤5：如果在步骤4中未识别出病变，识别形态模式，如线状、实变、结节状或微结节状。这五个诊断高衰减异常的步骤将病变细分为九类。这种逐步的放射学诊断方法有助于缩小各种肺部高衰减异常的鉴别诊断范围，并实现精确诊断。关键相关性声明我们提出的高衰减肺部异常逐步诊断算法可能有助于识别各种高衰减表现，确定相关疾病是否需要进一步检查，并指导适当的患者管理。要点• 为高衰减肺部异常提供逐步诊断方法。• 使放射科医生熟悉高衰减肺部异常的不同病因。• 识别哪些高衰减异常需要仔细审查和及时治疗。
