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In The British journal of radiology

OBJECTIVES : Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.

METHODS : In this institutional review board approved study of 143 patients with predominantly solid, lipid-poor, contrast enhancing renal lesions (46 benign and 97 malignant), patients with a preoperative multiphase CECT of the abdomen and pelvis obtained between June 2009 and June 2015 were retrospectively queried. Benign renal masses included oncocytoma and lipid-poor angiomyolipoma and the malignant group included clear cell, papillary, and chromophobe carcinomas.Region of interests of whole tumor volumes were manually segmented, and CT Phase images with the largest cross-section of the segmented tumor in the axial plane were used for assessment. Post-surgical pathological evaluation was used to establish diagnosis.The segmented images of renal masses were used as input to a CNN. The data was augmented and split into training (83.9%) and validation sets (16.1%) to determine the hyperparameters of the CNN. Thereafter. the performance of the resulting CNN was quantified using 8-fold cross-validation.

RESULTS : The CNN-based classifier demonstrated an overall accuracy of 78% (95% CI: 76-80%), sensitivity of 70% (95% CI: 66-74%), specificity of 81% (79-83%) and an AUC of 0.82.

CONCLUSION : A CNN-based classifier to diagnose solid enhancing malignant renal masses based on multiphase CECT images was developed.

ADVANCES IN KNOWLEDGE : It was established that a CNN-based classifier could be trained to accurately distinguish malignant renal lesions.

Oberai Assad, Varghese Bino, Cen Steven, Angelini Tomas, Hwang Daryl, Gill Inderbir, Aron Manju, Lau Christopher, Duddalwar Vinay