Dataset Information

How to Improve SBRT Outcomes in NSCLC: From Pre-Clinical Modeling to Successful Clinical Translation.

ABSTRACT: Despite major research and clinical efforts, lung cancer remains the leading cause of cancer-related death. While the delivery of conformal radiotherapy and image guidance of stereotactic body radiotherapy (SBRT) have revolutionized the treatment of early-stage non-small-cell lung cancer (NSCLC), additional research is needed to elucidate underlying mechanisms of resistance and identify novel therapeutic combinations. Clinical progress relies on the successful translation of pre-clinical work, which so far has not always yielded expected results. Improved clinical modelling involves characterizing the preclinical models and selecting appropriate experimental designs that faithfully mimic precise clinical scenarios. Here, we review the current role of SBRT and the scope of pre-clinical armamentarium at our disposal to improve successful clinical translation of pre-clinical research in the radiation oncology of NSCLC.

SUBMITTER: Milic M

PROVIDER: S-EPMC8997119 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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How to Improve SBRT Outcomes in NSCLC: From Pre-Clinical Modeling to Successful Clinical Translation.

Milic Marina M Mondini Michele M Deutsch Eric E

Cancers 20220327 7

Despite major research and clinical efforts, lung cancer remains the leading cause of cancer-related death. While the delivery of conformal radiotherapy and image guidance of stereotactic body radiotherapy (SBRT) have revolutionized the treatment of early-stage non-small-cell lung cancer (NSCLC), additional research is needed to elucidate underlying mechanisms of resistance and identify novel therapeutic combinations. Clinical progress relies on the successful translation of pre-clinical work, w ...[more]

PMID: 35406477

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Project description: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.

Dataset Information

How to Improve SBRT Outcomes in NSCLC: From Pre-Clinical Modeling to Successful Clinical Translation.

Publications

How to Improve SBRT Outcomes in NSCLC: From Pre-Clinical Modeling to Successful Clinical Translation.

Similar Datasets

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