With further confirmation of its accuracy, radiologists using their expertise and the program may eliminate unnecessary and costly biopsies Tiwari said.
Brain biopsies are currently the only definitive test but are highly invasive and risky, causing considerable morbidity and mortality.
To develop the programme, the researchers employed machine learning algorithms in conjunction with radiomics, the term used for features extracted from images using computer algorithms. The team trained the computer to identify radiomic features that discriminate between brain cancer and radiation necrosis, using routine follow-up MRI scans from 43 patients. The team then developed algorithms to find the most discriminating radiomic features, in this case, textures that cannot be seen by simply eyeballing the images.
"What the algorithms see that the radiologists don't are the subtle differences in quantitative measurements of tumour heterogeneity and breakdown in microarchitecture on MRI, which are higher for tumour recurrence," Tiwari said.
In the direct comparison, two physicians and the computer programmes analysed MRI scans from 15 patients from University of Texas Southwest Medical Center. One neuroradiologist diagnosed seven patients correctly, and the second physician correctly diagnosed eight patients. The computer program was correct on 12 of the 15, the study said.