Deep learning predicts chromosomal instability from histopathology images.

TitleDeep learning predicts chromosomal instability from histopathology images.
Publication TypeJournal Article
Year of Publication2021
AuthorsXu Z, Verma A, Naveed U, Bakhoum SF, Khosravi P, Elemento O
Date Published2021 May 21

Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.

Alternate JournaliScience
PubMed ID33997679
PubMed Central IDPMC8099498
Grant ListDP5 OD026395 / OD / NIH HHS / United States
P30 CA008748 / CA / NCI NIH HHS / United States
P50 CA247749 / CA / NCI NIH HHS / United States