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Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study

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Abstract

Objective

To assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies.

Methods

A total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC.

Results

No independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733–0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice.

Conclusions

A multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies.

Key Points

• Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies.

• Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms.

• RSs are established using a logistic regression (LR) algorithm.

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Abbreviations

3D-ROI:

Three-dimensional region of interest

AUC:

Area under the curve

DCA:

Decision curve analysis

DKI:

Diffusion kurtosis imaging

FS-T2WI:

Fat-suppressed T2-weighted images

ICC:

Intraclass correlation coefficient

Kmax:

Maximum kurtosis

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

mRMR:

Maximum relevance minimum redundancy

RS:

Radiomics signature

T1c:

Contrast-enhanced T1

T1WI:

T1-weighted images

TE:

Echo time

TR:

Repetition time

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Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Shucheng Bi and Jie Li are primary authors and contributed equally to this work.

Authors and Affiliations

  1. The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China

    Shucheng Bi, Jie Li, Tongyu Wang, Hexiang Wang & Dapeng Hao

  2. The Department of Radiology, The PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China

    Fengyuan Man

  3. The Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China

    Peng Zhang

  4. The Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China

    Feng Hou

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  1. Shucheng Bi
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  2. Jie Li
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  3. Tongyu Wang
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  4. Fengyuan Man
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  5. Peng Zhang
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  7. Hexiang Wang
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Corresponding authors

Correspondence to Hexiang Wang or Dapeng Hao.

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The scientific guarantor of this publication is Dapeng Hao.

Conflict of interest

The authors of this manuscript 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 (Jie Li) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic or prognostic study

• multicentre study

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Bi, S., Li, J., Wang, T. et al. Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study. Eur Radiol 32, 6933–6942 (2022). https://doi.org/10.1007/s00330-022-08780-w

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  • DOI: https://doi.org/10.1007/s00330-022-08780-w

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