A computer-assisted diagnostic procedure helps physicians detect the growth of low-grade brain tumors earlier and at smaller volumes than visual comparison alone, according to a recent study. (Fathallah-Shaykh HM et al. Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study. PLoS Med. 2019; 16(5):e1002810. doi: 10.1371/journal.pmed.1002810).
Low-grade gliomas constitute 15% of all adult brain tumors and cause significant neurological problems. A computer-assisted diagnostic procedure that digitizes the tumor and uses imaging scans to segment the tumor and generate volumetric measures could aid in the objective detection of tumor growth by directing the attention of the physician to changes in volume. In the new study, the authors evaluated 63 patients–56 with grade 2 gliomas and 7 with an imaging abnormality without pathological diagnosis–for a median follow-up period of 150 months, and compared tumor growth detection by seven physicians aided by a computer-assisted diagnostic procedure versus retrospective clinical reports.
The computer-assisted diagnostic procedure involved digitizing magnetic resonance imaging scans of the tumors. Physicians aided by the computer-assisted method diagnosed tumor growth in 13 of 22 glioma patients labeled as clinically stable by the radiological reports, but did not detect growth in the imaging-abnormality group. In 29 of the 34 patients with progression, the median time-to-growth detection was 14 months for the computer-assisted method compared to 44 months for current standard-of-care radiological evaluation. Using the computer-assisted method, accurate detection of tumor enlargement was possible with a median of only 57% change in tumor volume compared to a median of 174% change in volume required using standard-of-care clinical methods. According to the authors, the findings suggest that current clinical practice is associated with significant delays in detecting the growth of low-grade gliomas, and computer-assisted methods could reduce these delays.