Optimization of a deep convolution neural network (DCNN) for osteosarcoma necrosis quantification and outcome prediction.

Abstract

e13541 Background: Deep convolutional neural network is a form of artificial intelligence that gained traction recently in diagnostic and prognostic applications such as automated tissue segmentation and outcome and response to immunotherapy prediction. Herein, we optimize DCNN to accurately segment and quantify necrosis in osteosarcoma (OSA) archival slides. We also correlate the AI driven necrosis ratios with those derived from experienced sarcoma pathologists (gold standard) and show their value in predicting patient’s outcomes. Methods: A total of 44 patients with bone resections for primary high-grade osteosarcomas post neoadjuvant treatment with conventional chemotherapy were included (Male n= 26, female n=19), median age = 16.4 years. 103 slides from 21 patients were originally manually segmented and used for initial training using 3-fold cross-validation split per patient and per slide. Here, an additional 119 slides from 23 patients (total of 222 slides) were added in the retraining for automated segmentation and to update the necrosis ratio calculation, and the correlation with pathologist calculated necrosis ratio (R spearman correlation). Based on this ratio, we performed a prediction of Overall survival using the Log rank test. Results: The segmentation classifier identified viable tumor, necrosis, normal tissue and artefacts within slides with an area under the curve ranging from 0.91 to 0.96. AI necrosis ratio showed a good correlation with pathologist driven ration (R=0.74 per slide, R=0.69 per patient). Some outliers with highest discrepancy for necrosis ratio included surface high grade periosteal osteosarcomas (known for cartilaginous differentiation that is not part of the original training of the algorithm). Patients with an AI necrosis ratio of ≥ 70% had a better Overall survival at 36 months (p=0.01). Conclusions: Our results show the potential of integration of DCNN for automated segmentation and necrosis quantification of central high OSA resection enhancing efficiency in interpreting these complex surgical specimens. The data also suggests a possible prognostic application of the AI derived necrosis ratio. Further validation of the DCNN performance on an independent cohort is underway.