Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features

Abstract

Abstract Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence‐assisted accurate prediction of tumour outcomes is increasing. Thus, the present study constructed a quantitative histological image classifier with tumour nuclear features to predict osteosarcoma outcomes using haematoxylin and eosin (H&E)‐stained whole‐slide images (WSIs) from 150 osteosarcoma patients. We first segmented eight distinct tissues in osteosarcoma H&E‐stained WSIs, with an average accuracy of 90.63% on the testing set. The tumour areas were automatically and accurately acquired, facilitating the tumour cell nuclear feature extraction process. Based on six selected tumour nuclear features, we developed an osteosarcoma histological image classifier (OSHIC) to predict the recurrence and survival of osteosarcoma following standard treatment. The quantitative OSHIC derived from tumour nuclear features independently predicted the recurrence and survival of osteosarcoma patients, thereby contributing to precision oncology. Moreover, we developed a fully automated workflow to extract quantitative image features, evaluate the diagnostic values of feature sets and build classifiers to predict osteosarcoma outcomes. Thus, the present study provides a novel tool for predicting osteosarcoma outcomes, which has a broad application prospect in clinical practice.

Keywords

• diseases
• learning (artificial intelligence)
• surgery
• tumours