Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative

Katherine Zukotynski, Sandra E. Black, Phillip H. Kuo, Aparna Bhan, Sabrina Adamo, Christopher J.M. Scott, Benjamin Lam, Mario Masellis, Sanjeev Kumar, Corinne E. Fischer, Maria Carmela Tartaglia, Anthony E. Lang, David F. Tang-Wai, Morris Freedman, Neil Vasdev, Vincent Gaudet

Research output: Contribution to journalArticlepeer-review

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 18F-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 18F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18F-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 18F-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.

Original languageEnglish (US)
Pages (from-to)616-620
Number of pages5
JournalClinical nuclear medicine
Volume46
Issue number8
DOIs
StatePublished - 2021

Keywords

  • K-means clustering
  • brain imaging
  • dementia
  • machine learning
  • unsupervised learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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