Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Abstract

AbstractIntroductionNottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology. However, manual NHG assessment of biopsies is challenging and has a large inter-assessor variability with a large proportion being classified as NHG2 (intermediate grade). Here, we evaluate whether DeepGrade, a previously developed model for the risk stratification of resected tumour specimens, could be applied to risk-stratify biopsy specimens.MethodsA total of 11,943,905 tiles from 1171 whole slide images (WSIs) of preoperative biopsies from 897 patients diagnosed with breast cancer in Stockholm, Sweden, were included in this retrospective observational study. DeepGrade, a deep convolutional neural network model, was applied for classification of low and high risk tumours and evaluated against clinically assigned grades 1 and 3 using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.ResultsThe DeepGrade model classified resected tumour cases with grades NHG1 and NHG3 using only biopsy specimens with an AUC of 0.903 (95% CI: 0.88;0.93). The model could also classify the biopsy NHG (1 and 3) assessed on the biopsy of 186 patients with an AUC of 0.959 (95% CI: 0.93; 0.99). Furthermore, out of the 434 NHG2 tumours, 255 (59%) were classified as DeepGrade2-low, and 179 (41%) were classified as DeepGrade2-high. Using a multivariable Cox proportional hazards model the hazard ratio between low- and high-risk groups was estimated as 2.01 (p-value = 0.036).ConclusionsDeepGrade could predict the resected tumour grades NHG1 and NHG3 using only the biopsy specimen and sub-classify grade 2 tumours into low and high risks. The results demonstrate that the DeepGrade model can provide decision support for biopsy grading, and potentially provide decision support in the clinical setting to identifying high-risk tumours based on preoperative breast biopsies, thus improving information available for clinical treatment decisions.