Intraoperative assessment of canine soft tissue sarcoma by deep learning enhanced optical coherence tomography

Soft tissue sarcoma (STS) is a locally aggressive and infiltrative tumour in dogs. Surgical resection is the treatment of choice for local tumour control. Currently, post-operative pathology is performed for surgical margin assessment. Spectral-domain optical coherence tomography (OCT) has recently been evaluated for its value for surgical margin assessment in some tumour types in dogs. The purpose of this study was to develop an automatic diagnosis system that can assist clinicians in real-time for OCT image interpretation of tissues at surgical margins. We utilized a ResNet-50 network to classify healthy and cancerous tissues. A patch-based approach was adopted to achieve accurate classification with limited training data (80 cancer images, 80 normal images) and the validation set (20 cancer images, 20 normal images). The proposed method achieved an average accuracy of 97.1% with an excellent sensitivity of 94.3% on the validation set; the quadratic weighted κ was 0.94 for the STS diagnosis. In an independent test data set of 20 OCT images (10 cancer images, 10 normal images), the proposed method correctly differentiated all the STS images. Furthermore, we proposed a diagnostic curve, which could be evaluated in real-time to assist clinicians in detecting the specific location of a lesion. In short, the proposed method is accurate, operates in real-time and is non-invasive, which could be helpful for future surgical guidance.