An Evolutionary Random Forest to measure the Dworak tumor regression grade applied to colorectal cancer

In order to optimize a patient's cancer treatment, the prediction of the tumor's response to a planned radiotherapy is vital. For patients with rectal cancer, the quality of life depends heavily on the surgery leaving some patients with a stoma or other problems. If we would be able to predict the response upfront, we would be able to personalize the treatment and therefore preserve the QOL as much as possible. The Dworak tumor regression grade is a typical diagnostic tool to assess the tumor response of colorectal cancer patients. However, the Dworak grade is determined by a pathologist by inspection of the tumor biopsy without a dedicated measurement instrument. The measurement of the Dworak grade in an automated way by using the pre-operative CT scans, is a challenge. In this paper, we propose a novel methodology to measure the Dworak grade based on a customized random forest. We created a new random forest based on the methods from Breiman and Ho. These methods are further enhanced by evolutionary computation. We give the results of both our new method and other classical classification methods and highlight that the choice of classifier and knowledge is crucial. We extracted 111 radiomic features from 141 patients with colorectal cancer, of which 97 bad and 44 good responders. The evaluation gave a 77.507% accuracy and the cross-validation used for validation, gave a 67.081% accuracy. Our results were the best compared with the some other classification methods showing the need of a new method.