Exploring and verifying key thin-section computed tomography features for accurately differentiating granulomas and...

Area of Science:

• Radiology
• Pulmonology
• Oncology

Background:

• Granulomas and peripheral lung cancers (PLCs) often present with similar imaging findings on computed tomography.
• Misdiagnosis can occur due to overlapping imaging characteristics, necessitating improved differentiation methods.

Purpose of the Study:

• To establish a classification system based on thin-section computed tomography (TSCT) features.
• To accurately distinguish between pulmonary granulomas and PLCs presenting as solid nodules (SNs).

Main Methods:

• Retrospective analysis of 561 granulomas and 561 size-matched PLCs.
• Evaluation and comparison of TSCT features for differentiation.
• Development of an eight-type classification system based on nodule characteristics.

Main Results:

• Eight distinct TSCT-based lesion types were identified and compared between granulomas and PLCs.
• Types I (satellite lesions), II (halo sign/calcification), III (special shape), and IV (ill-defined boundary) were more common in granulomas.
• Types VI (coarse margin), VII (vacuole sign), and VIII (lobulated, no vacuole sign) were more frequent in PLCs, while Type V (smooth margin) was common in granulomas.
• Receiver operating characteristic (ROC) curve analysis confirmed the effectiveness of types I, II, III, V, VII, and VIII in differentiation.

Conclusions:

• A TSCT-based classification system effectively differentiates pulmonary granulomas from PLCs.
• Specific TSCT features, including types I, II, III, and V, are indicative of granulomas.
• This classification system aids in reducing misdiagnosis between these conditions.