Missed Pulmonary Nodules on CT: Determinants and Implications for Radiologic Practice

Area of Science:

• Radiology
• Medical Imaging
• Diagnostic Errors

Background:

• Undetected lung nodules contribute significantly to failure-to-diagnose errors in radiology.
• Specific nodule characteristics (small size, pure ground-glass) and complex anatomical locations (endobronchial, perivascular) increase missed detection rates.
• Cognitive biases, perceptual limitations, and reader fatigue also play a role in missed nodules.

Purpose of the Study:

• To review factors influencing lung nodule detection on chest CT scans.
• To categorize these factors into technical limitations, nodule/adjacent structure characteristics, and reader-related issues.
• To explore the impact of artificial intelligence and discuss medicolegal implications.

Main Methods:

• Literature review focusing on factors affecting nodule detection in chest CT.
• Categorization of influencing factors into technical, intrinsic nodule/adjacent structure, and reader-based elements.
• Discussion of artificial intelligence and medicolegal aspects.

Main Results:

• Nodule detection is influenced by technical limitations, intrinsic nodule characteristics, adjacent structures, and reader-specific factors.
• Small nodules and pure ground-glass nodules are frequently missed.
• Complex anatomical regions and reader fatigue are associated with higher miss rates.

Conclusions:

• Understanding the multifaceted contributors to missed lung nodules is crucial for developing mitigation strategies.
• Objective characterization of missed nodules can inform medicolegal frameworks.
• Advances in CT technology and AI show promise but do not eliminate the need for improved radiologist performance.