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An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.

Materials and Methods

A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT <  = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).

Results

The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4–90.6] and mean AUC-ROC of 0.795 [0.608–0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).

Conclusion

Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

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Abbreviations

ccRCC:

Clear Cell Renal Cell Carcinoma

VHL:

Von Hippel-Lindau

RFC:

Random Forest Classifier

AUC:

Area Under the curve

ROC:

Receiver operating characteristic curve

CI:

Confidence interval

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Author information

Author notes

  1. Ashkan A. Malayeri and W. Marston Linehan equally share the senior authorship.

Authors and Affiliations

  1. Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA

    Pouria Yazdian Anari, Aditi Chaurasia, Safa Samimi, Fatemeh Dehghani Firouzabadi, Evrim Turkbey & Elizabeth C. Jones

  2. Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA

    Nathan Lay, Stephanie Harmon & Baris Turkbey

  3. Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA

    Nikhil Gopal & Mark W. Ball

  4. Pathology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA

    Maria J. Merino

  5. Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, USA

    Paul Wakim

  6. Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bldg. 10, Room 2 W-5940 and Room 1-5940, 10 Center Drive, Bethesda, MD, 20892, USA

    W. Marston Linehan

  7. Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA

    Ashkan A. Malayeri

Authors
  1. Pouria Yazdian Anari
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  2. Nathan Lay
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  10. Evrim Turkbey
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  11. Elizabeth C. Jones
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  12. Mark W. Ball
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Corresponding authors

Correspondence to W. Marston Linehan or Ashkan A. Malayeri.

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This single-institution study was approved by the institutional review board. Written informed consent was obtained from all patients.

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Anari, P.Y., Lay, N., Gopal, N. et al. An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome. Abdom Radiol 47, 3554–3562 (2022). https://doi.org/10.1007/s00261-022-03610-5

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  • DOI: https://doi.org/10.1007/s00261-022-03610-5

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