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Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer.

Marco Bertolini¹, Valeria Trojani¹, Andrea Botti¹

¹S.C. Fisica Medica, Azienda USL-IRCCS di Reggio Emilia, 42124 Reggio Emilia, Italy.

Current Oncology (Toronto, Ont.)

|July 27, 2022

Summary

This summary is machine-generated.

Radiomic features from CT and PET scans can predict outcomes for early-stage non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT). Harmonized CT radiomics improved prediction accuracy, showing potential for personalized treatment strategies.

Keywords:

computed tomography (ct)imaging biomarkers and radiomics machine learning multi-modality ct-positron emission tomography (pet)non-small-cell lung cancer quantitative imaging/analysis stereotactic body radiation therapy (sbrt)

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Area of Science:

Radiology
Oncology
Medical Imaging

Background:

Stereotactic body radiation therapy (SBRT) is a standard treatment for early-stage non-small cell lung cancer (NSCLC).
Predicting treatment response and progression-free survival (PFS) is crucial for optimizing SBRT outcomes.

Purpose of the Study:

To investigate the association between radiomic features from pre-treatment CT and PET scans and clinical outcomes in early-stage NSCLC patients receiving SBRT.
To develop and validate predictive models for 24-month PFS using radiomic data.

Main Methods:

Retrospective analysis of 117 early-stage NSCLC patients from seven Italian centers treated with SBRT.
Extraction of 3004 radiomic features from pre-treatment CT and PET images.
Development of a novel CT feature harmonization technique using LASSO for feature selection.

Main Results:

Harmonized CT radiomic models (B models) outperformed models using original CT features (C models).
A linear support vector machine (SVM) model (A1) incorporating harmonized CT and PET features achieved an AUC of 0.77 (0.63-0.85) for predicting 24-month PFS in an external validation cohort.
Clinical features did not significantly improve the predictive performance of the radiomic models.

Conclusions:

A novel CT data harmonization strategy, termed delta radiomics, shows promise for improving radiomic model performance.
Harmonized radiomic models, particularly those combining CT and PET features, can effectively predict patient prognosis in early-stage NSCLC treated with SBRT.