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Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC.

Methods

A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients’ clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves.

Results

The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68–0.80] in the training cohort and 0.77 [95% CI, 0.68–0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value.

Conclusions

We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC.

Key Points

• There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status.

• Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease.

• Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.

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Abbreviations

AUC:

Area under the curve

CRC:

Colorectal cancer

CT:

Computed tomography

dMMR:

Deficient MMR

FFPE:

Paraffin-embedded

HDL:

High-density lipoprotein

ICCs:

Inter-observer intraclass correlation coefficients

IHC:

Immunohistochemistry

LASSO:

Least absolute shrinkage and selection operator

MMR:

Mismatch repair

MSI:

Microsatellite instability

MSI-H:

High MSI

MSI-L:

Low MSI

MSS:

Microsatellite stability

NCI:

National Cancer Institute

PCR:

Polymerase chain reaction

PLT:

Platelet

pMMR:

Proficient MMR

ROC:

Receiver operating characteristic

ROI:

Regions of interest

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Acknowledgements

We thank Dr. Feiyue Zeng (Xiangya Hospital, Central South University) for helpful discussion and assistance in data analysis. We thank staff members in the Departments of Radiology, General Surgery, and Pathology at Xiangya Hospital for their efforts in collecting the information used in this study. Editing assistance was provided by Kerin Higa, PhD (City of Hope National Medical Center).

Funding

This study has received funding (in part) by the Natural Science Foundation of Hunan Province (2018JJ2641).

Author information

Author notes

  1. Qian Pei and Xiaoping Yi contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

    Qian Pei, Fengbo Tan & Hongyan Zai

  2. Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People’s Republic of China

    Xiaoping Yi, Yan Fu, Guangwu Lei & Changyong Chen

  3. National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, People’s Republic of China

    Xiaoping Yi & Yan Fu

  4. Department of Radiology, 331 Hospital of Zhuzhou City, Zhuzhou, People’s Republic of China

    Chen Chen

  5. GE Healthcare, Hangzhou, 310000, People’s Republic of China

    Peipei Pang

  6. Department of Pathology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People’s Republic of China

    Guanghui Gong & Qingling Li

  7. Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA

    Bihong T. Chen

Authors
  1. Qian Pei
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  2. Xiaoping Yi
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Corresponding authors

Correspondence to Xiaoping Yi or Qingling Li.

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Guarantor

The scientific guarantor of this publication is Prof. Xiaoping Yi.

Conflict of interest

One of the authors of this manuscript (Peipei Pang) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Case-control study

• Performed at one institution

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Pei, Q., Yi, X., Chen, C. et al. Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. Eur Radiol 32, 714–724 (2022). https://doi.org/10.1007/s00330-021-08167-3

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  • DOI: https://doi.org/10.1007/s00330-021-08167-3

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