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Screen-detected solid nodules: from detection of nodule to structured reporting
Mario Silva1, Gianluca Milanese1, Roberta E Ledda1
1Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
Translational Lung Cancer Research
|June 24, 2021
Summary
Lung cancer screening using low-dose computed tomography (LDCT) detects lung nodules. Artificial intelligence enhances nodule characterization, improving lung cancer risk prediction and personalizing screening for better patient outcomes.
Area of Science:
- Pulmonology
- Radiology
- Artificial Intelligence
Background:
- Lung cancer screening (LCS) utilizes low-dose computed tomography (LDCT) for early detection of lung nodules.
- The majority of LDCT scans reveal solid nodules, but few are malignant, necessitating accurate characterization to prevent overdiagnosis and unnecessary procedures.
Purpose of the Study:
- To review the current standards for managing solid nodules detected via LDCT.
- To explore the role of artificial intelligence (AI) in improving the accuracy of lung nodule detection and characterization.
Main Methods:
- Review of existing literature on lung nodule management and AI applications in radiology.
- Analysis of AI-driven computer-aided detection and diagnosis (CAD) systems for lung nodules.
Main Results:
- AI-based CAD systems show promise in accurately detecting and characterizing lung nodules, reducing false positives.
- AI approaches outperform current risk models, potentially decreasing the need for surveillance CT scans.
- AI facilitates personalized lung cancer risk stratification beyond current size-based thresholds.
Conclusions:
- AI integration in LCS is poised to refine nodule management and candidate selection for further workup.
- Future LCS guidelines may incorporate AI for continuous, personalized lung cancer risk assessment.
- AI advancements aim to optimize LCS by improving diagnostic accuracy and patient selection.
