Automated Deep Aberration Detection from Chromosome Karyotype Images. (arXiv:2211.14312v1 [q-bio.QM])

Chromosome analysis is essential for diagnosing genetic disorders. For
hematologic malignancies, identification of somatic clonal aberrations by
karyotype analysis remains the standard of care. However, karyotyping is costly
and time-consuming because of the largely manual process and the expertise
required in identifying and annotating aberrations. Efforts to automate
karyotype analysis to date fell short in aberration detection. Using a training
set of ~10k patient specimens and ~50k karyograms from over 5 years from the
Fred Hutchinson Cancer Center, we created a labeled set of images representing
individual chromosomes. These individual chromosomes were used to train and
assess deep learning models for classifying the 24 human chromosomes and
identifying chromosomal aberrations. The top-accuracy models utilized the
recently introduced Topological Vision Transformers (TopViTs) with
2-level-block-Toeplitz masking, to incorporate structural inductive bias.
TopViT outperformed CNN (Inception) models with >99.3% accuracy for chromosome
identification, and exhibited accuracies >99% for aberration detection in most
aberrations. Notably, we were able to show high-quality performance even in
“few shot” learning scenarios. Incorporating the definition of clonality
substantially improved both precision and recall (sensitivity). When applied to
“zero shot” scenarios, the model captured aberrations without training, with
perfect precision at >50% recall. Together these results show that modern deep
learning models can approach expert-level performance for chromosome aberration
detection. To our knowledge, this is the first study demonstrating the
downstream effectiveness of TopViTs. These results open up exciting
opportunities for not only expediting patient results but providing a scalable
technology for early screening of low-abundance chromosomal lesions.