■大多数原发性骨肿瘤通常在膝关节周围的骨骼中发现。然而,对于没有经验的或初级的放射科医生来说,在X光片上检测原发性骨肿瘤可能是一项挑战。这项研究旨在开发一种深度学习(DL)模型,用于在X射线照片上检测膝关节周围的原发性骨肿瘤。
■来自四个三级转诊中心,我们招募了687例诊断为骨肿瘤(包括骨肉瘤,软骨肉瘤,骨巨细胞瘤,骨囊肿,内生软骨瘤,纤维发育不良,等。417名男性,270名女性;平均年龄22.8±13.2岁),根据术后病理或临床影像学/随访,和1,988名具有正常骨骼X光片的参与者(1,152名男性,836名女性;平均年龄27.9±12.2岁)。数据集被分成一个训练集,用于模型开发,用于模型验证的内部独立测试集和外部测试集。经过训练的模型定位骨肿瘤病变,然后检测肿瘤患者。接收器工作特性曲线和Cohen的kappa系数用于评估检测性能。我们使用置换测试将模型的检测性能与内部测试集中的两名初级放射科医生的检测性能进行了比较。
■DL模型在内部和外部测试集中的X射线照片上正确定位了94.5%和92.9%的骨肿瘤,分别。对于内部和外部测试集,在骨肿瘤患者的DL检测中,准确度为0.964/0.920,接受者工作特征曲线下面积(AUC)为0.981/0.990,分别。内部测试集中模型的Cohen\的kappa系数显着高于具有4年和3年肌肉骨骼放射学经验的两名初级放射科医师的kappa系数(模型与读者A,0.927vs.0.777,P<0.001;模型与读者B,0.927vs.0.841,P=0.033)。
■DL模型在检测膝关节周围的原发性骨肿瘤方面取得了良好的性能。该模型比初级放射科医生的性能更好,表明在X光片上检测骨肿瘤的可能性。
UNASSIGNED: Most primary bone tumors are often found in the bone around the knee joint. However, the detection of primary bone tumors on radiographs can be challenging for the inexperienced or junior radiologist. This study aimed to develop a deep learning (DL) model for the detection of primary bone tumors around the knee joint on radiographs.
UNASSIGNED: From four tertiary referral centers, we recruited 687 patients diagnosed with bone tumors (including osteosarcoma, chondrosarcoma, giant cell tumor of bone, bone cyst, enchondroma, fibrous dysplasia, etc.; 417 males, 270 females; mean age 22.8±13.2 years) by postoperative pathology or clinical imaging/follow-up, and 1,988 participants with normal bone radiographs (1,152 males, 836 females; mean age 27.9±12.2 years). The dataset was split into a training set for model development, an internal independent and an external test set for model validation. The trained model located bone tumor lesions and then detected tumor patients. Receiver operating characteristic curves and Cohen\'s kappa coefficient were used for evaluating detection performance. We compared the model\'s detection performance with that of two junior radiologists in the internal test set using permutation tests.
UNASSIGNED: The DL model correctly localized 94.5% and 92.9% bone tumors on radiographs in the internal and external test set, respectively. An accuracy of 0.964/0.920, and an area under the receiver operating characteristic curve (AUC) of 0.981/0.990 in DL detection of bone tumor patients were for the internal and external test set, respectively. Cohen\'s kappa coefficient of the model in the internal test set was significantly higher than that of the two junior radiologists with 4 and 3 years of experience in musculoskeletal radiology (Model vs. Reader A, 0.927 vs. 0.777, P<0.001; Model vs. Reader B, 0.927 vs. 0.841, P=0.033).
UNASSIGNED: The DL model achieved good performance in detecting primary bone tumors around the knee joint. This model had better performance than those of junior radiologists, indicating the potential for the detection of bone tumors on radiographs.