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CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single institute pilot study

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Abstract

Objectives

To assess the potential of radiomic features (RFs) extracted from simulation computed tomography (CT) images in discriminating local progression (LP) after stereotactic body radiotherapy (SBRT) in the management of lung oligometastases (LOM) from colorectal cancer (CRC).

Materials and methods

Thirty-eight patients with 70 LOM treated with SBRT were analyzed. The largest LOM was considered as most representative for each patient and was manually delineated by two blinded radiation oncologists. In all, 141 RFs were extracted from both contours according to IBSI (International Biomarker Standardization Initiative) recommendations. Based on the agreement between the two observers, 134/141 RFs were found to be robust against delineation (intraclass correlation coefficient [ICC] > 0.80); independent RFs were then assessed by Spearman correlation coefficients. The association between RFs and LP was assessed with Mann–Whitney test and univariate logistic regression (ULR): the discriminative power of the most informative RF was quantified by receiver-operating characteristics (ROC) analysis through area under curve (AUC).

Results

In all, 15/38 patients presented LP. Median time to progression was 14.6 months (range 2.4–66 months); 5/141 RFs were significantly associated to LP at ULR analysis (p < 0.05); among them, 4 RFs were selected as robust and independent: Statistical_Variance (AUC = 0.75, p = 0.002), Statistical_Range (AUC = 0.72, p = 0.013), Grey Level Size Zone Matrix (GLSZM) _zoneSizeNonUniformity (AUC = 0.70, p = 0.022), Grey Level Dependence Zone Matrix (GLDZM) _zoneDistanceEntropy (AUC = 0.70, p = 0.026). Importantly, the RF with the best performance (Statisical_Variance) is simply representative of density heterogeneity within LOM.

Conclusion

Four RFs extracted from planning CT were significantly associated with LP of LOM from CRC treated with SBRT. Results encourage further research on a larger population aiming to define a usable radiomic score combining the most predictive RFs and, possibly, additional clinical features.

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Acknowledgements

The authors thank Prof. Chiara Brombin (MSc, PhD, University Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy) for her help in drafting the final version of the statistical analysis.

Funding

Dr. Martina Mori, PhD, receives funds from the Italian Association for Cancer Research (AIRC, IG23150).

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, 60, Olgettina Street, 20132, Milan, Italy

    Andrei Fodor MD, Roberta Tummineri MD, Chiara Lucrezia Deantoni MD, Simone Baroni MD, Stefano Lorenzo Villa MD, Italo Dell’Oca MD &  Nadia Di Muzio MD

  2. Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Martina Mori PhD, Sara Broggi MSc, Paola Mangili MSc, Antonella Del Vecchio MSc & Claudio Fiorino MSc

  3. Vita-Salute San Raffaele University, Milan, Italy

    Nadia Di Muzio MD

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  1. Andrei Fodor MD
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Corresponding author

Correspondence to Andrei Fodor MD.

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Conflict of interest

A. Fodor, M. Mori, R. Tummineri, S. Broggi, C.L. Deantoni, P. Mangili, S. Baroni, S.L. Villa, I. Dell’Oca, A. Del Vecchio, C. Fiorino and N. Di Muzio declare that they have no competing interests.

Additional information

Data availability statement

The data that support the findings of this study (anonymized individual participant data) are available on request from the corresponding author to researchers who provide a methodologically sound proposal. Requests made to the corresponding author (AF) will be evaluated by the IRCCS San Raffaele Scientific Institute Ethics Committee.

Supplementary Information

66_2022_2034_MOESM1_ESM.tif

Fig. 1 Suppl.: The heat map of radiomic features extracted. The most correlated couplespairs of features according to the Spearman coefficient are reported in red and green

66_2022_2034_MOESM2_ESM.docx

CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single Institute pilot study

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Fodor, A., Mori, M., Tummineri, R. et al. CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single institute pilot study. Strahlenther Onkol 199, 477–484 (2023). https://doi.org/10.1007/s00066-022-02034-w

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