Patent ID: 11972867
Assignee: UNIVERSITEIT MAASTRICHT
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method of training a machine learning data processing model for determining a hypoxia status of a neoplasm in a human or animal body, wherein the machine learning data processing model is a random forest data processing model, the method comprising:
obtaining, for each of a plurality of neoplasms, at least one data sample comprising three dimensional imaging data of said respective neoplasm;
determining, for each data sample, a hypoxic fraction of the neoplasm by identifying a volume fraction of a total volume of the neoplasm which is indicative of hypoxia, and associating the determined hypoxic fraction with the respective data sample;
determining, for each data sample, a set of image features comprising a plurality of image features associated with the neoplasm, and wherein each image feature of the set of image features is characterized by an image feature parameter value that is derived from the image data;
iterating a sequence of training steps, wherein the sequence in each iteration includes:
selecting, for the respective iteration, a subset of image features from the set of image features, wherein at least a part of the selected image features for the subset has not been selected during a preceding iteration;
eliminating, for each data sample, the subset of image features from the set of image features to yield a reduced set of image features;
generating, based on the reduced sets of image features for the plurality of data samples and the hypoxic fractions associated with the data samples, a plurality of decision trees and providing a momentary random forest model based on the decision trees;
submitting, for at least one test data sample, a test set of image features to the momentary random forest model to determine the hypoxia status for the at least one test data sample, and compare the determined hypoxia status with the hypoxia fraction associated with the test data sample to yield a performance value;

continuing the step of iterating until each of the image features has been selected for a subset at least once;
selecting, by evaluating the performance values, a plurality of preferred image features from the set of image features for providing a radiomics feature signature including the preferred image features; and
providing the trained random forest data processing model based on decision trees associated with the preferred image features of the radiomics feature signature.