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Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate externally validated magnetic resonance (MR)–based and computed tomography (CT)–based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC).

Materials and methods

Patients with pathologically proven ccRCC in 2009–2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation.

Results

Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC.

Conclusions

MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT–based classifiers are potentially superior to those based on single-sequence or single-phase imaging.

Key Points

• Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs.

• ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

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Abbreviations

ACC:

Accuracy

ADC:

Apparent diffusion coefficient

ccRCC:

Clear cell renal cell carcinoma

CMP:

Corticomedullary phase

CT:

Computed tomography

GLCM:

Gray-level cooccurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size-zone matrix

GTDM:

Gray-tone difference matrix

ML:

Machine learning

MRI:

Magnetic resonance imaging

NP:

Nephrographic phase

NPV:

Negative predictive value

PCP:

Precontrast phase

PPV:

Positive predictive value

RCC:

Renal cell carcinoma

SPC:

Specificity

TCGA-KIRC:

The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma

TPR:

True positive rate

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Funding

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

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Authors and Affiliations

  1. Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China

    Enming Cui, Zhuoyong Li, Changyi Ma, Yong Lan, Zhipeng Zhou & Wansheng Long

  2. Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China

    Qing Li & Ronggang Li

  3. Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China

    Yi Lei, Juan Yu & Fan Lin

Authors
  1. Enming Cui
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  4. Qing Li
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  7. Juan Yu
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  10. Wansheng Long
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  11. Fan Lin
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Corresponding authors

Correspondence to Wansheng Long or Fan Lin.

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Guarantor

The scientific guarantor of this publication is Wansheng Long.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts were derived from TCGA public database (TCGA Research Network: http://cancergenome.nih.gov/), which could be used in other research.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Cui, E., Li, Z., Ma, C. et al. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics. Eur Radiol 30, 2912–2921 (2020). https://doi.org/10.1007/s00330-019-06601-1

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  • DOI: https://doi.org/10.1007/s00330-019-06601-1

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