TY - JOUR
T1 - Predicting cancer relapse following lung stereotactic radiotherapy
T2 - an external validation study using real-world evidence
AU - Davey, Angela
AU - Thor, Maria
AU - van Herk, Marcel
AU - Faivre-Finn, Corinne
AU - Rimner, Andreas
AU - Deasy, Joseph O.
AU - McWilliam, Alan
N1 - Funding Information:
AD, AM, CF-F and MH are supported by Cancer Research UK via the funding to Cancer Research Manchester Centre [C147/A25254]. AM, MH, and CF-F are also supported by the National Institute for Health and Care Research Manchester Biomedical Research Centre. CF-F and MH are also supported by Cancer Research UK RadNet Manchester [C1994/A28701]. Open access publication fees were provided by The University of Manchester library. Acknowledgments
Funding Information:
AD, AM, CF-F and MH are supported by Cancer Research UK via the funding to Cancer Research Manchester Centre [C147/A25254]. AM, MH, and CF-F are also supported by the National Institute for Health and Care Research Manchester Biomedical Research Centre. CF-F and MH are also supported by Cancer Research UK RadNet Manchester [C1994/A28701]. Open access publication fees were provided by The University of Manchester library.
Publisher Copyright:
Copyright © 2023 Davey, Thor, van Herk, Faivre-Finn, Rimner, Deasy and McWilliam.
PY - 2023/7/12
Y1 - 2023/7/12
N2 - 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.
AB - 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.
KW - biomarker-by-treatment interactions
KW - external validation
KW - image-based data mining
KW - local relapse
KW - NSCLC
KW - personalized medicine
KW - real world data
KW - stereotactic ablative body radiotherapy (SABR)
UR - http://www.scopus.com/inward/record.url?scp=85165951516&partnerID=8YFLogxK
U2 - 10.3389/fonc.2023.1156389
DO - 10.3389/fonc.2023.1156389
M3 - Article
C2 - 37503315
AN - SCOPUS:85165951516
SN - 2234-943X
VL - 13
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1156389
ER -