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    Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence

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    Authors
    Davey, Angela
    Thor, M.
    van Herk, Marcel
    Faivre-Finn, Corinne
    Rimner, A.
    Deasy, J. O.
    McWilliam, Alan
    Affiliation
    Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester
    Issue Date
    2023
    
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    Abstract
    Purpose: For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer. Methods: 199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90th percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported. Results: Local relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation). Conclusion: In these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.
    Citation
    Davey A, Thor M, van Herk M, Faivre-Finn C, Rimner A, Deasy JO, et al. Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence. Frontiers in oncology. 2023;13:1156389. PubMed PMID: 37503315. Pubmed Central PMCID: PMC10369005. Epub 2023/07/28. eng.
    Journal
    Frontiers in Oncology
    URI
    http://hdl.handle.net/10541/626482
    DOI
    10.3389/fonc.2023.1156389
    PubMed ID
    37503315
    Additional Links
    https://dx.doi.org/10.3389/fonc.2023.1156389
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.3389/fonc.2023.1156389
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