Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach

SIMPLE SUMMARY: Osteosarcoma is a rare form of bone cancer that primarily affects children and adolescents during their growth years. Known to be one of the most aggressive tumors, its 5-year survival rate ranges from 27% to 65% across all age groups. Despite the availability of treatment options such as surgery, chemotherapy, and limb-salvage surgery, the risk of recurrence and metastasis remains high even after remission. To improve disease prognosis, it is crucial to explore new diagnostic and treatment methods. Machine learning and artificial intelligence hold promise in this regard. In this study, we adopted a comparative methodological approach to evaluate various deep learning networks for disease diagnosis and classification, aiming to contribute to the advancement of these promising technologies in the field of osteosarcoma research. ABSTRACT: Background: Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potential for evaluating and classifying histopathological cross-sections. Methods: This study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas. Results: The classification performance did not necessarily improve when using larger networks on our dataset. In fact, the smallest network combined with the smallest image input size achieved the best overall performance. When trained using 5-fold cross-validation, the MobileNetV2 network achieved 91% overall accuracy. Conclusions: The present study highlights the importance of careful selection of network and input image size. Our results indicate that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks. The identification of an optimal network and training configuration could greatly improve the accuracy of osteosarcoma diagnoses and ultimately lead to better disease outcomes for patients.