I'm a colleague of the author, and I have talked to him quite a bit about C-Path since I work on something related. While I agree with much of what you are saying, I disagree slightly with your comment that the article is "principally, about a new morphological feature that the researchers believe is tied more strongly to survival according to their computational model."
The significance here is that he extracted 6000 low-level morphological features without any pre-conception about their usefulness. He then used GLMNET (logistic regression with L1-regularization) to automatically pick which of these features was important. Then, the craziest part is that the most informative features were not even cancer cells, but rather, surrounding stromal tissue. To quote from the paper, "Pathologists currently use only epithelial features in the standard grading scheme for breast cancer and
other carcinomas. Our findings suggest that evaluation of morphologic features of the tumor stroma may offer significant benefits for assessing prognosis." He essentially took a completely blinded, machine learning technique to find features that have been relatively ignored in pathology.
I think this is more indicative of a new paradigm in computer vision and machine learning in general that finely-tuned, human-crafted features can be beat with more automatic methods. Whereas before, we have tried to program features that characterize what we see, now we are finally looking at image features that can characterize what we're missing.
The significance here is that he extracted 6000 low-level morphological features without any pre-conception about their usefulness. He then used GLMNET (logistic regression with L1-regularization) to automatically pick which of these features was important. Then, the craziest part is that the most informative features were not even cancer cells, but rather, surrounding stromal tissue. To quote from the paper, "Pathologists currently use only epithelial features in the standard grading scheme for breast cancer and other carcinomas. Our findings suggest that evaluation of morphologic features of the tumor stroma may offer significant benefits for assessing prognosis." He essentially took a completely blinded, machine learning technique to find features that have been relatively ignored in pathology.
I think this is more indicative of a new paradigm in computer vision and machine learning in general that finely-tuned, human-crafted features can be beat with more automatic methods. Whereas before, we have tried to program features that characterize what we see, now we are finally looking at image features that can characterize what we're missing.