Image source: Wessels et al., PLOS One 2022 (CC BY 4.0)
With clear cell renal cell carcinoma, risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. ‘Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we looked at whether a convolutional neural network (CNN) can extract relevant image features from a typical H&E-stained slide to predict 5-year overall survival,’ said Dr Titus Brinker, head of Digital Biomarkers for Oncology group at the German Cancer Research Center (DKFZ) in Heidelberg.
His CNN-based prognostication of overall survival demonstrated promising results in a study published in August, with a mean weighted accuracy of 72%, sensitivity of 72.4% and specificity of 71.7%. Its generalizability can be combined with existing clinical pathology parameters. ‘While this widely applicable technique shows the potential of AI in image-based outcome prediction, further research is needed to fine-tune this method and improve robustness,’ Brinker concludes.