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.
AI-based solutions for automated Gleason grading have been developed to assist pathologists to make rapid and quantitative assessments, but the generalization across various scanners and updating AI models continuously using new annotated data from end users remains a key bottleneck in the field. We proposed an comprehensive digital pathology workflow for AI-assisted Gleason grading, incorporating an image quality check software A!magQC, a cloud-based annotation platform A!HistoNotes and Pathologist-AI Interaction (PAI) strategy. To demonstrate and validate the pipeline, we employed it on prostate samples obtained from 5 scanners for Gleason grading. After training on 132 prostatectomy specimens scanned by an Akoya Biosciences scanner, validation on 55 prostatectomy specimens and 156 biopsy specimens yielded a sensitivity of 85%, specificity of 96% and F1 score of 78% on Gleason grading for prostatectomy specimens, and 96% sensitivity on tumor detection for biopsy specimens. For images scanned by other 4 scanners, the average F1 score increased from 67% to 75% on Gleason pattern detection after adopting our generalization solution. In clinical experiments conducted with 5 pathologists from Singapore and China, our pipeline accelerated Gleason scoring by 43%. Furthermore, it reduced annotation time by 60% via semi-automatic annotation, leading to improved model performance through incremental learning.
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