Abstract 1101P
Background
Intraoperative diagnosis is essential for providing safe and effective care during staged melanoma resections. The existing workflow for intraoperative diagnosis based on H&E staining of processed tissue is time-, resource-, and labor-intensive. Moreover, interpretation of intraoperative histologic images is dependent on a pathology workforce that is contracting and unevenly distributed across the centers where melanoma excisions are performed worldwide.
Methods
We developed an automated workflow that uses deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real time. Specifically, our CNN, trained retrospectively on over 10,000 H&E images that were collected prospectively across multiple institutions, predicts brain tumor diagnosis in the operating room in under 150 s, which is an order of magnitude faster than conventional techniques.
Results
Our trained CNN was compared to pathologist-confirmed diagnoses in a testing set of 204 patients. Primary endpoint was overall diagnostic accuracy. We show that CNN-based diagnosis of H&E images was noninferior to pathologist-based interpretation of histologic images (overall accuracy, 94.6% vs 95.5%). Additionally, our CNN learned a hierarchy of interpretable histologic feature representations to classify the major histopathologic classes of melanoma. We then developed and implemented a semantic segmentation method that can identify tumor infiltrated and diagnostic regions within the images. Mean intersection over union values was 61 ± 28.6 for ground truth diagnostic class and 86.0 ± 28.6 for tumor-infiltrated regions.
Conclusions
We have demonstrated how the application of deep learning can result in near real-time intraoperative melanoma diagnosis. Our workflow provides a means of delivering expert-level intraoperative diagnosis where dermatopathology resources are scarce and improve diagnostic accuracy and efficiency in resource-rich centers.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.