An AI grade trained from the morphologically distinctive feature of nucleolar prominence could transmit significant patient outcome information across three independent patient cohorts. AI grade shows promise in gene discovery and for second opinions.
Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision
11-04-2021 11:12
Artificial intelligence (AI) based breast cancer grading may help overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.
A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA). The test set, obtained from the Cooperative Human Tissue Network (CHTN), consisted of 1,058 cancers with corresponding survival data. Upon reversal, a CNN was trained from images of 1,537 CHTN cancers and tested on 397 TCGA cancers. In TCGA, mRNA models were trained using AI grade and Nottingham grade (NG) as labels. Performance of mRNA models in predicting patient outcome was evaluated using data from 1,807 cancers from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. In selecting images for training, nucleolar prominence determined high versus low grade cancer cells.
In CHTN, NG corresponded to significant survival stratification in stages 1,2 and 3 cancers while AI grade showed significance in stages 1 and 2 and borderline in stage 3 tumours. In METABRIC, the mRNA model trained from AI grade was not significantly different to the NG based model. The gene which best described AI grade was TRIP13, a gene involved with mitotic spindle assembly.