


Previous attempts to build a computerised pathologist of this sort required the designers to specify precisely which characteristics of the samples being examined were most important. For example, they would tell the computer to measure the three traits human pathologists use to determine a tumour's grade: the percentage of its cells that are tubelike; the diversity of appearance of the cell nuclei; and the proportion of cancer cells undergoing division. However, people are excellent at pattern recognition and skilled pathologists rely not just on these relatively easy-to-describe traits, but also on less well-defined characteristics that years of experience have taught them are significant too. Restricting computerised pathologists to the well-characterised bits of the process therefore inevitably results in worse performance than that delivered by their human counterparts.

Dr Koller's Computational Pathologist (C-Path), by contrast, lets the system work out for itself what the most important features of a tumour are. She and her colleagues started by setting down 6,642 characteristics the program might choose from when it assessed images of biopsies from breast-cancer patients, but did not tell it which to prefer. Some of the characteristics they offered were inherent to the cancer cells. Others were features of the surrounding “stromal” cells, which are not, themselves malignant, but act to support a tumour. And some were not features of individual cells at all but, rather, measured relations between cells (for example, the average distance between cancer-cell nuclei) and the context cells found themselves in (for example, whether they occurred in large clusters or were frequently interspersed with stroma).

The program was initially trained and tested using 248 breast-cancer samples from the Netherlands Cancer Institute. It was fed with images of slides from these patients, together with information on how long each patient had survived after the sample in question had been taken. That done, the software was then tested using a second set of samples, this time from 286 breast-cancer patients at Vancouver General Hospital. The system was able both to grade the slides and to predict, in a way that human pathologists could not, whether each patient would survive for five years after treatment.

When Dr Koller looked at which 11 features were the most robust predictors of survival, she discovered that only eight were characteristic of the tumour cells themselves. The other three were stromal characteristics. The fact that three stromal features were on the list suggests that the surrounding stroma influences whether or not a cancer progresses and kills the patient. That is important information because, hitherto, pathologists have focused on the cancer cells themselves and ignored the stroma. As well as outperforming human pathologists, it seems that C-Path can also teach them a thing or two about cancer biology.