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Updated: Apr 24, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Screening for Lung Cancer.

Maximiliano Klug¹, Margit V Szabari¹, Beatrice C Panjwani¹

¹Department of Radiology, Thoracic Imaging and Intervention, Massachusetts General Hospital, 55 Fruit Street, Austen 202, Boston, MA, USA.

Radiologic Clinics of North America

|April 22, 2026

Summary

This summary is machine-generated.

Low-dose computed tomography (CT) lung cancer screening saves lives but needs better systems. This review covers new strategies, imaging, and Lung-RADS for improved lung cancer detection and patient outcomes.

Keywords:

Health disparities in screening Low-dose CT Lung cancer screening Lung-RADS Risk prediction models

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Last Updated: Apr 24, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Area of Science:

Pulmonary Medicine
Radiology
Oncology

Background:

Lung cancer screening using low-dose computed tomography (CT) significantly reduces mortality.
Effective implementation requires integrated, system-based approaches for optimal outcomes.
Evolving strategies address challenges in screening delivery and patient management.

Purpose of the Study:

To review current and emerging lung cancer screening strategies.
To highlight advancements in risk-prediction models and imaging techniques.
To emphasize the role of standardized frameworks like Lung-RADS in clinical decision-making.

Main Methods:

Review of current literature on lung cancer screening protocols.
Analysis of risk-prediction models and imaging advancements.
Evaluation of the Lung CT Screening Reporting and Data System (Lung-RADS) framework.

Main Results:

Low-dose CT screening necessitates comparison with prior scans for change detection.
Lung-RADS categories provide a structured approach to managing screening findings.
Artificial intelligence (AI) and computer-aided detection (CAD) show promise in enhancing nodule detection and risk stratification, requiring radiologist oversight.

Conclusions:

Successful lung cancer screening implementation requires robust infrastructure, targeted outreach, and continuous quality improvement.
Maximizing survival benefits and ensuring equity are key goals for screening programs.
Integrating AI and CAD tools can improve consistency and longitudinal tracking in lung cancer screening.