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Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients.

Morteza Salehjahromi¹, Hui Li^1,2, Eman Showkatian¹

¹Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77030, USA.

|November 13, 2025

Summary

This summary is machine-generated.

A new radiomics approach improves lung cancer risk prediction for non-smokers and light smokers. This method uses serial CT scans to track nodule changes, enhancing early detection in patients ineligible for standard screening.

Keywords:

light-smokers lung cancer risk prediction USPSTF pulmonary nodules radiomics

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Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer

Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer

Published on: May 20, 2016

Area of Science:

Radiology
Medical Imaging Analysis
Oncology

Background:

Lung cancer risk models often exclude non-smokers and light smokers, who constitute a significant portion of cases.
Pulmonary nodules are frequently found incidentally on CT scans, necessitating better risk stratification tools.
Existing models like the Brock model lack generalizability to diverse smoking populations.

Purpose of the Study:

To develop a longitudinal, radiomics-based model for lung cancer risk prediction.
To enhance early lung cancer detection in patients ineligible for USPSTF screening.
To integrate time-varying radiomic features for dynamic risk assessment.

Main Methods:

Longitudinal analysis of 622 CT scans from 122 lung cancer patients (30% never-smokers, 69% former smokers).
Extraction of quantitative radiomic features from serial CT scans to track nodule evolution.
Implementation of a time-varying survival model, incorporating handcrafted radiomics and the Sybil deep learning model.

Main Results:

Radiomics identified CT patterns (size, intensity, entropy) indicative of malignant transformation.
The composite model integrating radiomics, delta-radiomics, and longitudinal features achieved a C-index of 0.69, outperforming demographics (0.50) and Sybil (0.54).
The model showed improved accuracy (78%), sensitivity (89%), and specificity (67%) compared to the Brock model.

Conclusions:

Integrating radiomics, Sybil, and clinical factors significantly improves lung cancer risk prediction in USPSTF-ineligible patients.
The developed model offers superior performance over existing methods for early cancer risk stratification.
Findings support personalized screening and early intervention strategies for lung cancer.