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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model.

Hui Li^1,2, Morteza Salehjahromi², Myrna C B Godoy³

¹Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

|May 14, 2025

Summary

This summary is machine-generated.

Persistent pulmonary nodules increase lung cancer risk. Both Brock and Sybil models showed limitations in predicting cancer risk, highlighting the need for optimized models for early detection.

Keywords:

brock model lung cancer risk assessment persistent pulmonary nodules precancer interception sybil model

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Area of Science:

Pulmonology
Oncology
Radiology
Artificial Intelligence in Medicine

Background:

Persistent pulmonary nodules are a significant risk factor for developing lung cancer.
Accurate risk assessment of these nodules is crucial for timely interception and improved patient outcomes.

Purpose of the Study:

To evaluate the performance of the Brock model and the Sybil deep learning model in predicting lung cancer risk in patients with persistent pulmonary nodules.
To explore the potential of machine learning models to refine risk assessment in this patient population.

Main Methods:

Retrospective and prospective cohorts of patients with persistent pulmonary nodules (defined by CT scans over 3 months) were analyzed.
Correlations between demographic factors, nodule characteristics, and Brock scores were assessed.
Performance of Brock and Sybil models was evaluated; machine learning models were developed to enhance risk prediction.

Main Results:

In the prospective cohort (n=301), 62 patients were diagnosed with lung cancer.
Higher median Brock scores were observed in patients with lung cancer (18.65% vs. 4.95%, p < 0.001).
Factors associated with lung cancer risk included family history, nodule size ≥10 mm, part-solid type, and spiculation. Brock model AUC: 0.679, Sybil AUC: 0.678. Logistic regression achieved the highest AUC at 0.729.

Conclusions:

Both the Brock and Sybil models demonstrated utility and limitations for lung cancer risk prediction in real-world hospital cohorts with persistent pulmonary nodules.
Optimizing predictive models is essential for enhancing early lung cancer detection and interception strategies in this population.