Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

Table 1 The advantages and disadvantages between the mutation panel test and deep learning-based method

	Mutation panel test	Deep learning-based method
Advantages	(1) High throughput method: Multiplex analysis of various genes; and (2) Quantitative and sensitive detection of genomic aberrations.	(1) More rapid turnaround time: Once trained, the predictions are fast (less than 5 min per gene) and fully automated; (2) Better picture of tumor heterogeneity: Heat map analysis provides insights into spatial distribution of mutations; and (3) Remote testing: It may be able to detect genetic mutation from pictures taken directly from the microscope at the remote institute.
Disadvantages	(1) Longer turnaround time: Run lasts from 1 to 3 d; and (2) High complexity of workflow: Requires complex sample preparation.	(1) Requires separate classifier for each gene; (2) Requires large training dataset: Neural networks work best with more data; and (3) Deep learning method is a black box: It is not straightforward to understand how the decision is made.