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Artificial intelligence predicts survival outcome of breast carcinomas on whole-slide histopathology images
01 SEPT 2024
Abstract
Background & objectives
We introduce a deep neural network (DNN) specifically designed to assign a survival risk score for breast carcinomas patients directly from whole slide images (WSI) of HE-stained sections of tumor, without any annotations.
Methods
The model integrates two distinct morphological informations: one encapsulates cellular-level informations, while the other encompasses tissue-level ones, using the Cox proportional hazard loss function. It has been trained and evaluated on the publicly available TCGA-BRCA dataset and tested in an external data of 254 HR+/HER2- patients. Cox multivariate analysis was carried out to assess the AI-based pronostic factor.
Results
The AI model demonstrated an average concordance index of 0.682 when assessed on the testing set. The Cox model, incorporating clinical features (age and TNM stage), produced an average c-index of 0.767. Notably, this value rose to 0.786 upon the inclusion of our AI-based risk score. The proposed model outperforms existing models when it comes to predicting survival. The Cox model indicates that our AI-based risk score can be used as an independent prognostic factor for predicting overall survival (p<0.005). Furthermore, we were able to significantly discriminate 2 groups of patients in terms of survival outcome, depending on a AI-based high or low risk.
Conclusion
In this study, we showcased that the algorithm was able to instantly extract prognostic morphological features from H&E whole slide images (WSI) and could be included in the pathology report. This could potentially enhance clinical decision-making, elevating the standard of care. Compared to commonly used molecular signatures, the AI algorithm enables a reduction in response time and cost savings.