Pan Canyu, Lian Luoyu, Chen Jieyun, Huang Risheng
Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian Province, China.
Department of Thoracic Surgery, Quanzhou First Hospital Affiliated to Fujian Medical, University, Quanzhou 362000, Fujian Province, China.
J Bone Oncol. 2023 Sep 15;42:100504. doi: 10.1016/j.jbo.2023.100504. eCollection 2023 Oct.
BACKGROUND & PURPOSE: For the best possible outcomes from therapy, proximal femur bone cancers must be accurately classified. This work creates an artificial intelligence (AI) model based on plain radiographs to categorize bone tumor in the proximal femur.
A tertiary referral center's standard anteroposterior hip radiographs were employed. A dataset 538 images of the femur, including malignant, benign, and tumor-free cases, was employed for training the AI model. There is a total of 214 images showing bone tumor. Pre-processing techniques were applied, and DenseNet model utilized for classification. The performance of the DenseNet model was compared to that of human doctors using cross-validation, further enhanced by incorporating Grad-CAM to visually indicate tumor locations.
For the three-label classification job, the suggested method boasts an excellent area under the receiver operating characteristic (AUROC) of 0.953. It scored much higher (0.853) than the diagnosis accuracy of the human experts in manual classification (0.794). The AI model outperformed the mean values of the clinicians in terms of sensitivity, specificity, accuracy, and F1 scores.
The developed DenseNet model demonstrated remarkable accuracy in classifying bone tumors in the proximal femur using plain radiographs. This technology has the potential to reduce misdiagnosis, particularly among non-specialists in musculoskeletal oncology. The utilization of advanced deep learning models provides a promising approach for improved classification and enhanced clinical decision-making in bone tumor detection.
为使治疗取得最佳效果，必须对股骨近端骨癌进行准确分类。本研究基于X线平片创建了一种人工智能（AI）模型，用于对股骨近端的骨肿瘤进行分类。
采用某三级转诊中心的标准髋关节前后位X线片。使用一个包含538张股骨图像的数据集（包括恶性、良性和无肿瘤病例）来训练AI模型。共有214张显示骨肿瘤的图像。应用了预处理技术，并使用DenseNet模型进行分类。通过交叉验证将DenseNet模型的性能与人类医生的性能进行比较，并通过结合Grad-CAM进一步增强，以直观显示肿瘤位置。
对于三标签分类任务，所提出的方法在受试者工作特征曲线下面积（AUROC）方面表现出色，达到0.953。其得分（0.853）远高于人类专家手动分类的诊断准确率（0.794）。在敏感性、特异性、准确性和F1分数方面，AI模型均优于临床医生的平均值。
所开发的DenseNet模型在使用X线平片对股骨近端骨肿瘤进行分类方面显示出显著的准确性。这项技术有可能减少误诊，特别是在肌肉骨骼肿瘤学非专科医生中。先进深度学习模型的应用为改善骨肿瘤检测中的分类和增强临床决策提供了一种有前景的方法。
