Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision.

Aim: Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.

Methods And Results: A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA).

The nucleolar-related protein Dyskerin pseudouridine synthase 1 (DKC1) predicts poor prognosis in breast cancer.

Background: Hypertrophy of the nucleolus is a distinctive cytological feature of malignant cells and corresponds to aggressive behaviour. This study aimed to identify the key gene associated with nucleolar prominence (NP) in breast cancer (BC) and determine its prognostic significance.

Methods: From The Cancer Genome Atlas (TCGA) cohort, digital whole slide images identified cancers having NP served as label and an information theory algorithm was applied to find which mRNA gene best explained NP.

Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study.

Aims: Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles.

Methods And Results: The Cancer Genome Atlas (TCGA) BC cohort was used (n = 743) and morphological features, including Nottingham grade and its components and nucleolar prominence, were assessed utilising whole-slide images (WSIs).

Prognostic significance of nucleolar assessment in invasive breast cancer.

Aims: Nucleolar morphometric features have a potential role in the assessment of the aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (BC) is still unclear. The aims of this study were to investigate the optimal method for scoring nucleoli in IBC and their prognostic significance, and to refine the grading of breast cancer (BC) by incorporating nucleolar score.

Breast cancer histologic grading using digital microscopy: concordance and outcome association.

Aims: Virtual microscopy utilising digital whole slide imaging (WSI) is increasingly used in breast pathology. Histologic grade is one of the strongest prognostic factors in breast cancer (BC). This study aims at investigating the agreement between BC grading using traditional light microscopy (LM) and digital WSI with consideration of reproducibility and impact on outcome prediction.

The Advantage of Discordance: An Example Using the Highly Subjective Nuclear Grading of Breast Cancer.

Discordance among multiple assessments has been a reason to criticize a biomarker. But, if different assessments are all relevant, the meaning of discordance requires explanation. As an example, for 1085 breast cancers, a low (score 1), intermediate (score 2) or high nuclear grade (NG) (score 3) was assigned in years 2013, 2015, 2016.