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Related Experiment Video

Updated: Apr 10, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Using Clinical Risk Models for Lung Nodule Classification.

Stephen A Deppen¹, Eric L Grogan¹

¹Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, Tennessee; Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.

Seminars in Thoracic and Cardiovascular Surgery

|June 16, 2015

Summary

This summary is machine-generated.

Diagnosing indeterminate pulmonary nodules is challenging, especially with increased lung cancer screening. Existing models like PLCO, McWilliams, Mayo, and TREAT aid clinicians in lung cancer diagnosis and management decisions.

Keywords:

clinical models lung cancer screening lung nodule prediction

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Area of Science:

Pulmonology
Thoracic Surgery
Oncology

Background:

Indeterminate pulmonary nodules pose a growing diagnostic challenge.
Lung cancer screening with low-dose CT increases the evaluation of small nodules.

Purpose of the Study:

To review existing clinical decision-making tools for indeterminate pulmonary nodules.
To highlight the need for integrated decision support in clinical practice.

Main Methods:

Review of established risk assessment models for pulmonary nodules.
Discussion of the utility of calculators in clinical decision-making.

Main Results:

Several models exist: PLCO for screening eligibility, McWilliams and Mayo for cancer probability, and TREAT for surgical biopsy decisions.
These tools assist in managing patients with indeterminate pulmonary nodules.

Conclusions:

Decision support tools are crucial for clinicians managing indeterminate pulmonary nodules.
Further development is needed to integrate these tools into clinical practice for enhanced shared decision-making.