The use of weakly supervised machine learning for necrosis assessment in patients with osteosarcoma: A pilot study

Percent necrosis (PN) following chemotherapy is a prognostic factor for survival in osteosarcoma. Pathologists estimate PN by calculating tumor viability over an average of whole-slide images (WSIs). This non-standardized, labor-intensive process requires specialized training and has high interobserver variability. Therefore, we aimed to develop a machine-learning model capable of calculating PN in osteosarcoma with similar accuracy to that of a musculoskeletal pathologist. In this proof-of-concept study, we retrospectively obtained six WSIs from two patients with conventional osteosarcomas. A weakly supervised learning model was trained by using coarse and incomplete annotations of viable tumor, necrotic tumor, and nontumor tissue in WSIs. Weakly supervised learning refers to processes capable of creating predictive models on the basis of partially and imprecisely annotated data. Once "trained," the model segmented areas of tissue and determined PN of the same six WSIs. To assess model fidelity, the pathologist also estimated PN of each WSI, and we compared the estimates using Pearson's correlation and mean absolute error (MAE). MAE was 15% over the six samples, and 6.4% when an outlier was removed, for which the model inaccurately labeled cartilaginous tissue. The model and pathologist estimates were strongly, positively correlated (r = 0.85). Thus, we created and trained a weakly supervised machine learning model to segment viable tumor, necrotic tumor, and nontumor and to calculate PN with accuracy similar to that of a musculoskeletal pathologist. We expect improvement can be achieved by annotating cartilaginous and other mesenchymal tissue for better representation of the histological heterogeneity in osteosarcoma.