• Waleed M Ghareeb, Eman Draz, Khaled Madbouly, Ahmed H Hussein, Mohammed Faisal, Wagdi Elkashef, Mona Hany Emile, Marcus Edelhamre, Seon Hahn Kim, Sameh Hany Emile, and for the Anam Hospital KRAS Research Group.
• From the Gastrointestinal Surgery Unit (Ghareeb, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt.
• J. Am. Coll. Surg. 2022 Sep 1; 235 (3): 482493482-493.

BackgroundKRAS mutation can alter the treatment plan after resection of colorectal cancer. Despite its importance, the KRAS status of several patients remains unchecked because of the high cost and limited resources. This study developed a deep neural network (DNN) to predict the KRAS genotype using hematoxylin and eosin (H&E)-stained histopathological images.Study DesignThree DNNs were created (KRAS_Mob, KRAS_Shuff, and KRAS_Ince) using the structural backbone of the MobileNet, ShuffleNet, and Inception networks, respectively. The Cancer Genome Atlas was screened to extract 49,684 image tiles that were used for deep learning and internal validation. An independent cohort of 43,032 image tiles was used for external validation. The performance was compared with humans, and a virtual cost-saving analysis was done.ResultsThe KRAS_Mob network (area under the receiver operating curve [AUC] 0.8, 95% CI 0.71 to 0.89) was the best-performing model for predicting the KRAS genotype, followed by the KRAS_Shuff (AUC 0.73, 95% CI 0.62 to 0.84) and KRAS_Ince (AUC 0.71, 95% CI 0.6 to 0.82) networks. Combing the KRAS_Mob and KRAS_Shuff networks as a double prediction approach showed improved performance. KRAS_Mob network accuracy surpassed that of two independent pathologists (AUC 0.79 [95% CI 0.64 to 0.93], 0.51 [95% CI 0.34 to 0.69], and 0.51 (95% CI 0.34 to 0.69]; p < 0.001 for all comparisons).ConclusionThe DNN has the potential to predict the KRAS genotype directly from H&E-stained histopathological slide images. As an algorithmic screening method to prioritize patients for laboratory confirmation, such a model might possibly reduce the number of patients screened, resulting in significant test-related time and economic savings.Copyright © 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.

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