Abstract
PurposeEarly detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs.MethodsStandard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors.ResultsThe area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P<0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively.ConclusionsThe AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.
Highlights
The proximal part of the femur, i.e., the head, neck, and trochanteric areas, is one of the most common anatomic locations for benign bone tumors and tumor-like conditions [1] and a common location for bone metastasis of malignant tumors from other organs
Our findings suggest that artificial intelligence (AI)-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology
Plain radiographs are widely used for routine screening for bone tumors, a considerable rate of misdiagnosis upon visual examination has been reported, as bone tumors show various morphologies and common ambiguous features [4, 7, 8]
Summary
The proximal part of the femur, i.e., the head, neck, and trochanteric areas, is one of the most common anatomic locations for benign bone tumors and tumor-like conditions [1] and a common location for bone metastasis of malignant tumors from other organs. Primary malignancies, such as osteosarcoma, chondrosarcoma, and Ewing’s sarcoma can develop at the proximal femur [2,3,4]. As high mechanical stress is concentrated during weight-bearing activities, it is the most common site of pathological fractures secondary to bone tumors [5, 6]. Computed tomography (CT), magnetic resonance imaging (MRI), bone scan, and positron emission tomography (PET) are more sensitive in detecting bone tumors; the routine use of advanced imaging modalities is costly and time-consuming
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