Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.

ABSTRACT:

Importance

Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening.

Objective

To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial.

Design, setting, and participants

In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screenin

SUBMITTER: Gonzalez Maldonado S 

PROVIDER: S-EPMC12543391 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature