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Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?

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Abstract

Purpose

To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer.

Methods

We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging.

Results

The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720–0.839 for combined model and AUC = 0.697, 95% CI = 0.538–0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621–0.777 for combined model and AUC = 0.628, 95% CI = 0.558–0.689 for CT staging only, Boot CI = 0.099).

Conclusion

CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.

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Acknowledgements

The content of this publication was previously presented in European Congress of Radiology 2021.

Funding

No financial support for this study.

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Authors and Affiliations

  1. Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands

    Eun Kyoung Hong, Zuhir Bodalal, Federica Landolfi, Nino Bogveradze, Paula Bos & Regina Beets-Tan

  2. GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands

    Eun Kyoung Hong, Zuhir Bodalal, Paula Bos & Regina Beets-Tan

  3. Radiology Unit, Sant’Andrea Hospital, Sapienza University of Rome, Rome, Italy

    Federica Landolfi

  4. Academic Pridon Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia

    Nino Bogveradze

  5. Seoul National University Hospital, Seoul, South Korea

    Eun Kyoung Hong, Sae Jin Park & Jeong Min Lee

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  1. Eun Kyoung Hong
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  2. Zuhir Bodalal
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  3. Federica Landolfi
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  4. Nino Bogveradze
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  8. Regina Beets-Tan
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Correspondence to Eun Kyoung Hong.

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The authors declare that they have no conflict of interest.

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This is a retrospective study and local institutional boards from two tertiary centers approved and waived the requirement for informed consent.

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Hong, E.K., Bodalal, Z., Landolfi, F. et al. Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?. Abdom Radiol 47, 2739–2746 (2022). https://doi.org/10.1007/s00261-022-03534-0

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