Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology

This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results on whole slide images (WSIs), and explore the clinical significance of obtained features. This study included 95 patients with cervical cancer who received radiotherapy between April 2013 and December 2020. Hematoxylin-eosin stained biopsies were digitized to WSIs and divided into small tiles. Our model adopted the feature extractor of DenseNet121 and the classifier of the support vector machine. About 12 400 tiles were used for training the model and 6000 tiles for testing. The model performance was assessed on a per-tile and per-WSI basis. The resultant probability was defined as radiotherapy status probability (RSP) and its color map was visualized on WSIs. Survival analysis was performed to examine the clinical significance of the RSP. In the test set, the trained model had an area under the receiver operating characteristic curve of 0.76 per-tile and 0.95 per-WSI. In visualization, the model focused on viable tumor components and stroma in tumor biopsies. While survival analysis failed to show the prognostic impact of RSP during treatment, cases with low RSP at diagnosis had prolonged overall survival compared to those with high RSP (P = 0.045). In conclusion, we successfully developed a model to classify biopsies before and during radiotherapy and visualized the result on slide images. Low RSP cases before treatment had a better prognosis, suggesting that tumor morphologic features obtained using the model may be useful for predicting prognosis.