Classification of renal tumour using convolutional neural networks to detect oncocytoma

Mikkel Pedersen*, Michael Brun Andersen*, Henning Christiansen*, Nessn H. Asawi*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


To investigate the ability of convolutional neural networks (CNNs) to facilitate differentiation of oncocytoma from renal cell carcinoma (RCC) using non-invasive imaging technology.

Data were collected from 369 patients between January 2015 and September 2018. True labelling of scans as benign or malignant was determined by subsequent histological findings post-surgery or ultrasound-guided percutaneous biopsy. The data included 20,000 2D CT images.
Data were randomly divided into sets for training (70 %), validation (10 %) and independent testing (20 %, DataTest_1). A small dataset (DataTest_2) was used for additional validation of the training model. Data were divided into sets at the patient level, rather than by individual image. A modified version of the ResNet50V2 was used. Accuracy of detecting benign or malignant renal mass was evaluated by a 51 % majority vote of individual image classifications to determine the classification for each patient.

Test results from DataTest_1 indicate an area under the curve (AUC) of 0.973 with 93.3 % accuracy and 93.5 % specificity. Results from DataTest_2 indicate an AUC of 0.946 with 90.0 % accuracy and 98.0 % specificity when evaluation is performed image by image.
There is no case in which multiple false negative images originate from the same patient. When evaluated with 51 % majority of scans for each patient, the accuracy rises to 100 % and the incidence of false negatives falls to zero.

CNNs and deep learning technology can classify renal tumour masses as oncocytoma with high accuracy. This diagnostic method could prevent overtreatment for patients with renal masses.

CNN ( convolutional neural networks), RCC ( renal cell carcinoma), US ( ultrasound), CT ( computed tomography), IQR ( interquartile range), kV ( kilo voltage), RUC ( area under the curve)
Original languageEnglish
Article number109343
JournalEuropean Journal of Radiology
Issue number109343
Number of pages5
Publication statusPublished - Dec 2020


  • Deep learning
  • Machine learning
  • Oncocytoma
  • Renal cell carcinoma

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