Apple Machine Learning Algorithms Successfully Detect Colon Cancer but Fail to Predict KRAS Mutation Status

TL;DR

This study demonstrates that Apple machine learning algorithms can accurately distinguish colon cancer from benign tissue but are currently ineffective at predicting KRAS mutation status from histopathological images.

Contribution

The paper shows the potential of Apple CreateML for cancer diagnosis and highlights its limitations in mutation prediction from histopathological data.

Findings

High accuracy in differentiating colon cancer from benign tissue (98%)

Inability to reliably predict KRAS mutation status (highest accuracy 66%)

Potential for smartphone-based diagnostic applications

Abstract

Colon cancer is the second leading cause of cancer-related death in the United States of America. Its prognosis has significantly improved with the advancement of targeted therapies based on underlying molecular changes. The KRAS mutation is one of the most frequent molecular alterations seen in colon cancer and its presence can affect treatment selection. We attempted to use Apple machine learning algorithms to diagnose colon cancer and predict the KRAS mutation status from histopathological images. We captured 250 colon cancer images and 250 benign colon tissue images. Half of colon cancer images were captured from KRAS mutation-positive tumors and another half from KRAS mutation-negative tumors. Next, we created Image Classifier Model using Apple CreateML machine learning module. The trained and validated model was able to successfully differentiate between colon cancer and benign colon tissue images with 98 % recall and 98 % precision. However, our model failed to reliably identify KRAS mutations, with the highest realized accuracy of 66 %. Although not yet perfected, in the near future Apple CreateML modules can be used in diagnostic smartphone-based applications and potentially alleviate shortages of medical professionals in understaffed parts of the world.