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

TL;DR

This paper introduces a machine learning framework utilizing CNNs to automate osteosarcoma detection in CT scans, aiming for quick, accurate diagnosis to improve patient outcomes.

Contribution

It presents a novel pipeline combining preprocessing, CNN-based classification, and 3D visualization for osteosarcoma detection in CT scans.

Findings

Achieved 94.8% AUC in detection accuracy

Attained 94.6% specificity in identifying osteosarcoma

Demonstrated effectiveness on a dataset of 12 patients

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\%.