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Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study

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

Abstract

Purpose

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms.

Methods

Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini–Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs.

Results

Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms’ AUCs decreased using histogram or texture features from NC or NG phases.

Conclusion

The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

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Authors and Affiliations

  1. Department of Radiology, George Washington University Hospital, 900 23rd St NW, Washington, DC, 20037, USA

    Shawn Haji-Momenian, Bhumi Patel, Nicole Law & James Earls

  2. Department of Biomedical Engineering, George Washington University, 800 22nd Street NW, 5000 Science & Engineering Hall, Washington, DC, 20052, USA

    Zixian Lin, Anishsanjay Nayak & Murray Loew

  3. Department of Family Medicine, University of Pittsburgh Medical Center (UPMC) Altoona, 501 Howard Avenue, Suite F2, Altoona, PA, 16601, USA

    Adam Michalak

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  1. Shawn Haji-Momenian
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Correspondence to Shawn Haji-Momenian.

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Haji-Momenian, S., Lin, Z., Patel, B. et al. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study. Abdom Radiol 45, 789–798 (2020). https://doi.org/10.1007/s00261-019-02336-1

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