Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative. (August 2021) - Zukotynski, Katherine; Black, Sandra E.; Kuo, Phillip H.; Bhan, Aparna; Adamo, Sabrina; Scott, Christopher J.M.; Lam, Benjamin; Masellis, Mario; Kumar, Sanjeev; Fischer, Corinne E.; Tartaglia, Maria Carmela; Lang, Anthony E.; et al.
Abstract : Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter observational study: 19 cognitively normal, 25 mild cognitive impairment, and 22 dementia (11 Alzheimer disease, 3 subcortical vascular cognitive impairment, and 8 Parkinson–Lewy Body spectrum disorder). As part of the neurocognitive and imaging evaluation, each participant had an 18 F-flutemetamol (Vizamyl, GE Healthcare) brain PET. All studies were processed using Cortex ID software (General Electric Company, Boston, MA) to calculate SUV ratios in 19 regions of interest and clinically interpreted by 2 dual-certified radiologists/nuclear medicine physicians, using MIM software (MIM Software Inc, Cleveland, OH), blinded to the quantitative analysis, with final interpretation based on consensus. K-means clustering was retrospectively used to classify the studies from the quantitative data. Results: Based on clinical interpretation, 46 brain PETs were negative and 20 were positive for amyloid deposition. Of 19 cognitively normal participants, 1 (5%) had a positive 18 F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18 F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18 F-flutemetamol brain PET; 7 of 11 participants with Alzheimer disease (64%), 1 of 3 participants with vascular cognitive impairment (33%), and 2 of 8 participants with Parkinson–Lewy Body spectrum disorder (25%) had a positive 18 F-flutemetamol brain PET. Using clinical interpretation as the criterion standard, K-means clustering (K = 2) gave sensitivity of 95%, specificity of 98%, and accuracy of 97%. Conclusions: K-means clustering may be a powerful algorithm for classifying amyloid brain PET.