Open gwaybio opened 5 years ago
The authors train an inception v3 CNN on TCGA lung cancer pathology images. There are two goals presented in the paper 1) CNN to predict normal lung vs. lung adeno (LUAD) vs. lung squamous (LUSC); 2) CNN to predict mutation status of top 10 mutated genes in LUAD. The model is also nicely validated in an independent set and in FFPE and fresh frozen samples.
There are some nice discussions about other CNNs applied to pathology images, and other instances of the inception network applied to biomedical images (#207, #151)
Overall, the performance for the first task is quite good, reaching pathologist level prediction strength. Interestingly, both model and pathologist incorrectly classified many of the same samples. The second task is very interesting - the image data themselves store information about the mutation status of the given pixel patch. The mutations had variable classification performance. For example, STK11 mutations were predicted strongly, while ALK mutations could not be detected.
Interesting application to predict mutation status from pixels. It also looks like many predictions in the same sample (i.e. different patches) were associated with different mutational states and lung cancer subtypes. This approach could be one nice way of assessing mutational heterogeneity (with spatial resolution). It would also be interesting if the samples consistently predicted incorrectly by pathologists and the model were more heterogeneous - many patches predicted as LUAD, LUSC, and higher mutational heterogeneity.
https://doi.org/10.1038/s41591-018-0177-5