Harnessing Baseline Radiomic Features in Early-Stage NSCLC: What Role in Clinical Outcome Modeling for SBRT Candidates?

TL;DR

This study shows that radiomic features from CT scans can improve survival predictions for early-stage lung cancer patients treated with SBRT.

Contribution

The study demonstrates that radiomic features outperform clinical factors in predicting overall and loco-regional survival in SBRT-treated early-stage lung cancer patients.

Findings

Radiomic models outperformed clinical models in predicting overall survival and loco-regional progression-free survival.

Clinical models slightly outperformed radiomic models in predicting progression-free survival.

Radiomic features show promise as non-invasive biomarkers for outcome prediction in early-stage lung cancer.

Abstract

This study aims to assess whether non-invasive radiomic features (RFs) derived from CT scans could improve survival predictions for Early-Stage Non-Small Cell Lung Cancer (ES-NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Three prognostic models were built: clinical, radiomic, and combined clinical-radiomic, and their predictive accuracy for overall survival (OS), progression-free survival (PFS), and loco-regional progression-free survival (LRPFS) were compared using the C-index. Data from 100 patients were analyzed. Results showed that the radiomic model provided superior prediction for OS and LRPFS compared to clinical factors alone, though clinical models slightly outperformed RFs for PFS. The findings support the potential of RFs as non-invasive biomarkers for outcome prediction in ES-NSCLC patients. Future studies are planned to validate these results and further explore RFs’ utility in clinical practice.

Aim: An Early-Stage Non-Small Cell Lung Cancer (ES-NSCLC) patient candidate for stereotactic body radiotherapy (SBRT) may start their treatment without a histopathological assessment, due to relevant comorbidities. The aim of this study is twofold: (i) build prognostic models to test the association between CT-derived radiomic features (RFs) and the outcomes of interest (overall survival (OS), progression-free survival (PFS) and loco-regional progression-free survival (LRPFS)); (ii) quantify whether the combination of clinical and radiomic descriptors yields better prediction than clinical descriptors alone in prognostic modeling for ES-NSCLC patients treated with SBRT. Methods: Simulation CT scans of ES-NSCLC patients treated with curative-intent SBRT at the European Institute of Oncology (IEO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy between 2013 and 2023 were retrospectively retrieved. PyRadiomics v3.0.1 was used for image preprocessing and subsequent RFs extraction and selection. A radiomic score was calculated for each patient, and three prognostic models (clinical model, radiomic model, clinical-radiomic model) for each survival endpoint were built. Relative performances were compared using the C-index. All analyses were considered statistically significant if p < 0.05. The statistical analyses were performed using R Software version 4.1. Results: A total of 100 patients met the inclusion criteria. Median age at diagnosis was 76 (IQR: 70–82) years, with a median Charlson Comorbidity Index (CCI) of 7 (IQR: 6–8). At the last available follow-up, 76 patients were free of disease, 17 were alive with disease, and 7 were deceased. Considering relapses, progression of any kind was diagnosed in 31 cases. Regarding model performances, the radiomic score allowed for excellent prognostic discrimination for all the considered endpoints. Of note, the use of RFs alone proved to be more informative than clinical characteristics alone for the prediction of both OS and LRPFS, but not for PFS, for which the individual predictive performances slightly favored the clinical model. Conclusion: The use of RFs for outcome prediction in this clinical setting is promising, and results seem to be rather consistent across studies, despite some methodological differences that should be acknowledged. Further studies are being planned in our group to externally validate these findings, and to better determine the potential of RFs as non-invasive and reproducible biomarkers in ES-NSCLC.

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