Computer Bests Neuroradiologists in Distinguishing Tumor Recurrence From Radiation Necrosis

MedicalResearch.com Interview with:

Dr. Pallavi Tiwari PhD Assistant Professor biomedical engineering Case Western Reserve University

Dr. Pallavi Tiwari

Dr. Pallavi Tiwari PhD
Assistant Professor biomedical engineering
Case Western Reserve University

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: One of the biggest challenges in neuro-oncology currently is distinguishing radionecrosis, a side-effect of aggressive radiation, from tumor recurrence on imaging. Surgical intervention is the only means of definitive diagnosis, but suffers from considerable morbidity and mortality. The treatments for radionecrosis and cancer recurrence are very different. Early identification of the two conditions can help speed prognosis, therapy, and improve patient outcomes.

The purpose of this feasibility study was to evaluate the role of machine learning algorithms along with computer extracted texture features, also known as radiomic features, in distinguishing radionecrosis and tumor recurrence on routine MRI scans (T1w, T2w, FLAIR). The radiomic algorithms were trained on 43 studies from our local collaborating institution – University Hospitals Case Medical Center, and tested on 15 studies at a collaborating institution, University of Texas Southwest Medical Center. We further compared the performance of the radiomic techniques with two expert readers.

Our results demonstrated that radiomic features can identify subtle differences in quantitative measurements of tumor heterogeneity on routine MRIs, that are not visually appreciable to human readers. Of the 15 test studies, the radiomics algorithm could identify 12 of 15 correctly, while expert 1 could identify 7 of 15, and expert 2, 8 of 15.

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