FemurTumorNet: Bone tumor classification in the proximal femur using DenseNet model based on radiographs

Canyu Pan; Luoyu Lian; Jieyun Chen; Risheng Huang

doi:10.1016/j.jbo.2023.100504

FemurTumorNet: Bone tumor classification in the proximal femur using DenseNet model based on radiographs

J Bone Oncol. 2023 Sep 15:42:100504. doi: 10.1016/j.jbo.2023.100504. eCollection 2023 Oct.

Authors

Canyu Pan¹, Luoyu Lian², Jieyun Chen¹, Risheng Huang¹

Affiliations

¹ Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian Province, China.
² Department of Thoracic Surgery, Quanzhou First Hospital Affiliated to Fujian Medical, University, Quanzhou 362000, Fujian Province, China.

Abstract

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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Artificial intelligence; Bone tumors; Classification; DenseNet; Grad-CAM; Proximal femur; Radiographs.