Department of Orthopaedics and Sports Orthopaedic, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany;
Department of Orthopaedics and Sports Orthopaedic, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Anticancer Res. 2022 Sep;42(9):4371-4380. doi: 10.21873/anticanres.15937.
BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma.
A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively.
The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making.
Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm was able to cope with very limited data. However, a systematic and structured data acquisition is necessary to further develop the algorithm and increase results to clinical relevance.
背景/目的：尤因肉瘤是一种主要发生在儿童中的高度恶性肿瘤。这种恶性肿瘤的放射学征象可能被误诊为急性骨髓炎。这两种实体需要截然不同的治疗方法，导致完全不同的预后。本研究的目的是开发一种人工智能算法，可以从普通 X 光片中确定成像特征，从而区分骨髓炎和尤因肉瘤。
从我们的肉瘤中心收集了 182 张 X 光片（118 张健康，44 张尤因肉瘤，20 张骨髓炎），来自 58 名不同的儿科（≤18 岁）患者。所有部位都被考虑在内。包括急性、慢性骨髓炎合并急性骨髓炎和骨内尤因肉瘤。排除慢性骨髓炎、骨外尤因肉瘤、恶性小细胞肿瘤和基于软组织的原始神经外胚层肿瘤。算法开发分为两个阶段，并构建了两种不同的分类器，并结合迁移学习方法来应对非常有限的数据量。在第一阶段，将病理发现与健康发现区分开来。在第二阶段，将骨髓炎与尤因肉瘤区分开来。实施了数据扩充和中值频率平衡。分别应用了 70%、15%、15%的数据分割用于训练、验证和保留测试。
在第一阶段，算法在验证和测试数据上的准确率分别为 94.4%和 90.6%。在第二阶段，验证和测试数据的准确率分别为 90.3%和 86.7%。Grad-CAM 结果显示了对算法决策有重要意义的区域。
我们的人工智能算法可以成为任何参与治疗肌肉骨骼病变的医生的有价值的支持，以支持骨髓炎和尤因肉瘤的检测和鉴别诊断过程。通过迁移学习方法，该算法能够应对非常有限的数据。然而，为了进一步开发算法并将结果提高到临床相关性，需要进行系统和结构化的数据采集。
