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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

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Radiomics-based Prediction of Local Recurrence after Stereotactic Body Radiation Therapy for Early-Stage Non-Small

Chioma P Ogbonna¹, William G Breen², Pierre Le Noach^1,3

¹Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine.

Annals of the American Thoracic Society

|April 10, 2025

Summary

Radiomics models predict recurrence risk in early-stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The pre-treatment CT scan model shows promise for personalized treatment planning and surveillance.

Keywords:

computer-aided nodule analysis and risk yield (CANARY)local recurrence non–small cell lung cancer (NSCLC)radiomics stereotactic body radiation therapy (SBRT)

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Published on: October 10, 2018

Area of Science:

Radiology
Oncology
Medical Imaging Analysis

Background:

Stereotactic body radiation therapy (SBRT) is effective for early-stage non-small cell lung cancer (NSCLC).
Local and systemic recurrences remain significant challenges after SBRT for NSCLC.
Computed tomography (CT) radiomics offers potential for predicting recurrence risk.

Purpose of the Study:

To develop and validate radiomics-based risk models for predicting local recurrence in early-stage NSCLC patients treated with SBRT.
To assess the predictive performance of models using pre-treatment, post-treatment, and delta CT scans.
To identify a clinically relevant model for individualized treatment planning and surveillance.

Main Methods:

Retrospective case-control study with training and independent validation sets of early-stage NSCLC patients treated with SBRT.
Extraction of 102 quantitative radiomic features from semi-automatically segmented tumors on CT scans.
Development of three multivariable models (pre-SBRT, post-SBRT, Delta) using selected features and validation of the pre-SBRT model.

Main Results:

Thirteen independent variables were selected using the Boruta algorithm.
High performance for all models: pre-SBRT (AUC 0.91), post-SBRT (AUC 0.92), and Delta (AUC 0.94).
The pre-SBRT model achieved an AUC of 0.89 in the independent validation set, demonstrating clinical utility.

Conclusions:

Radiomic analysis successfully developed high-performing models to predict local recurrence after SBRT for NSCLC.
The pre-SBRT radiomics model was successfully validated and identified as clinically relevant.
This validated model may aid in personalized surveillance, treatment planning, and adjuvant therapy selection for NSCLC patients.