A Computer-aided Framework for Detecting Osteosarcoma in Computed Tomography Scans

arXiv cs.CV / 3/17/2026

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Key Points

The paper presents a computer-aided framework that uses CNN models to automate osteosarcoma detection in CT scans, incorporating preprocessing, ROI localization, and postprocessing.
It includes data augmentation, region-of-interest identification, and a visualization step that renders a 3D bone model highlighting the affected area.
In a study of 12 patients, the framework achieved an AUC of 94.8% and a specificity of 94.6%, indicating strong performance on limited data.
The work aims to help physicians with faster and more accurate diagnosis and prognosis, potentially enabling earlier treatment decisions.

Abstract

Osteosarcoma is the most common primary bone cancer, mainly affecting the youngest and oldest populations. Its detection at early stages is crucial to reduce the probability of developing bone metastasis. In this context, accurate and fast diagnosis is essential to help physicians during the prognosis process. The research goal is to automate the diagnosis of osteosarcoma through a pipeline that includes the preprocessing, detection, postprocessing, and visualization of computed tomography (CT) scans. Thus, this paper presents a machine learning and visualization framework for classifying CT scans using different convolutional neural network (CNN) models. Preprocessing includes data augmentation and identification of the region of interest in scans. Post-processing includes data visualization to render a 3D bone model that highlights the affected area. An evaluation on 12 patients revealed the effectiveness of our framework, obtaining an area under the curve (AUC) of 94.8\% and a specificity of 94.6\%.