In previous blog-posts we have discussed how simple models can perform just as well if not better than more complex ones when attempting to predict the cardiac liability of a new drug, see here and for the latest article on the matter here. One of the examples we discussed involved taking a signal, action-potential, deriving 100s of features from it and placing them into a machine learning algorithm to predict drug toxicity. This approach gave very impressive performance. However, we found that we could get the same results by simply adding/subtracting 3 numbers together! It seems there are other examples of this nature…

A recent paper sent to me was on the topic of recidivism, see here. The paper explored how well a machine learning algorithm which uses >100 features performed compared to the general public at predicting re-offending risk. What they found is that the general public was just as good. They also found that the performance of the machine learning algorithm could be easily matched by a two variable model!

Let’s move back to the life-sciences and take a look at an emerging field called radiomics. This field is in its infancy compared to the two already discussed above. In simple terms radiomics involves extracting information from an image of a tumour. Now the obvious parameter to extract is the size of the tumour through measuring its volume, a parameter termed Gross Tumour Volume, see here for a more detailed description. In addition to this though, like in the cardiac story, you can derive many more parameters, from the imaging signal. Again similar to the cardiac story you can apply machine learning techniques on the large data-set created to predict events of interest such as patient survival.

The obvious question to ask is: what do you gain over using the original parameter that is derived, Gross Tumour Volume? Well it appears the answer is very little, see supplementary table 1 from this article here for a first example. Within the table the authors calculate the concordance index for each model. (A concordance index value of 0.5 is random chance whereas a value of 1 implies perfect association, the closer to 1 the better.) The table includes p-values as well as the concordance index, let’s ignore the p-values and focus on the size of effect. What the table shows is that tumour volume is as good as the radiomics model in 2 out of the 3 data-sets, Lung2 and H&N1, and in the 3^{rd}, H&N2, TNM is as good as radiomics:

TNM
(Tumour Staging) |
Volume | Radiomics | TNM + Radiomics | Volume + Radiomics | |

Lung2 | 0.60 | 0.63 | 0.65 | 0.64 | 0.65 |

H&N1 | 0.69 | 0.68 | 0.69 | 0.70 | 0.69 |

H&N2 | 0.66 | 0.65 | 0.69 | 0.69 | 0.68 |

They then went on and combined the radiomics model with the other two but did not compare what happens when you combine TNM and tumour volume, a 2 variable model, to all other options. The question we should ask is why didn’t they? Also is there more evidence on this topic?

A more recent paper, see here, from within the field assessed the difference in prognostic capabilities between radiomics, genomics and the “clinical model”. This time tumour volume was not explored, why wasn’t it? Especially given that it looked so promising in the earlier study. The “clinical model” in this case consisted of two variables, TNM and histology, given we collect so much more than this, is this really a fair representation of a “clinical model”? The key result here was that radiomics only made a difference once you also included a genomic model too over the “clinical model” see Figure 5 in the paper. Even then the size of improvement was very small. I wonder what the performance of a simple model that involved TNM and tumour volume would have looked like, don’t you?

Radiomics meet Recidivism-Omics and Action-Potential-Omics you have more in common with them than you realise i.e. simplicity may beat complexity yet again!