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Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

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Abstract

Objective

To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning–based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT.

Methods

This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed.

Results

Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI.

Conclusion

Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading.

Key Points

• Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading.

• Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC.

• Shape features and first-order statistics features showed superior discriminative capability compared to texture features.

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Abbreviations

ACC:

Accuracy

AUC:

Area under the curve

ccRCC:

Clear cell renal cell carcinoma

CECT:

Contrast-enhanced computed tomography

CMP:

Corticomedullary phase

CT:

Computed tomography

EP:

Excretory phase

NP:

Nephrographic phase

PACS:

Picture archiving and communication system

RCC:

Renal cell carcinoma

ROC:

Receiver operating characteristic

SEN:

Sensitivity

SMOTE:

Synthetic minority oversampling technique

SPE:

Specificity

UP:

Unenhanced phase

VOI:

Volume of interest

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Funding

This study has received funding from the National Natural Science Foundation of China [grant numbers 81971574, 81874216], the Natural Science Foundation of Guangdong Province, P.R. China [grant number 2021A1515011350], the Science and Technology Project of Guangzhou, P.R. China [grant numbers 201904010422, 202002030268, 202102010025], the Guangzhou Key Laboratory of Molecular Imaging and Clinical Translational Medicine and the Construction of High-level Key Clinical Specialty (Medical Imaging) in Guangzhou.

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Author notes

  1. Shiwei Luo and Ruili Wei contributed equally.

Authors and Affiliations

  1. Department of Radiology, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, Guangdong, 510180, China

    Shiwei Luo, Ruili Wei, Zhe Wu, Xinrui Pang, Xinhua Wei, Xinqing Jiang & Ruimeng Yang

  2. School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China

    Songlin Lu & Xin Zhen

  3. School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, China

    Shengsheng Lai

  4. Department of Radiology, University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518000, China

    Jialiang Wu

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  1. Shiwei Luo
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Correspondence to Xin Zhen or Ruimeng Yang.

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The scientific guarantor of this publication is Dr. Ruimeng Yang.

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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.

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• retrospective

• observational

• multicenter study

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Luo, S., Wei, R., Lu, S. et al. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis. Eur Radiol 32, 2340–2350 (2022). https://doi.org/10.1007/s00330-021-08322-w

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