Department of Radiology, The Third Affiliated Hospital, Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong, China.
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
Cancer Imaging. 2024 Oct 10;24(1):135. doi: 10.1186/s40644-024-00784-7.
Accurately classifying primary bone tumors is crucial for guiding therapeutic decisions. The National Comprehensive Cancer Network guidelines recommend multimodal images to provide different perspectives for the comprehensive evaluation of primary bone tumors. However, in clinical practice, most patients' medical multimodal images are often incomplete. This study aimed to build a deep learning model using patients' incomplete multimodal images from X-ray, CT, and MRI alongside clinical characteristics to classify primary bone tumors as benign, intermediate, or malignant.
In this retrospective study, a total of 1305 patients with histopathologically confirmed primary bone tumors (internal dataset, n = 1043; external dataset, n = 262) were included from two centers between January 2010 and December 2022. We proposed a Primary Bone Tumor Classification Transformer Network (PBTC-TransNet) fusion model to classify primary bone tumors. Areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the model's classification performance.
The PBTC-TransNet fusion model achieved satisfactory micro-average AUCs of 0.847 (95% CI: 0.832, 0.862) and 0.782 (95% CI: 0.749, 0.817) on the internal and external test sets. For the classification of benign, intermediate, and malignant primary bone tumors, the model respectively achieved AUCs of 0.827/0.727, 0.740/0.662, and 0.815/0.745 on the internal/external test sets. Furthermore, across all patient subgroups stratified by the distribution of imaging modalities, the PBTC-TransNet fusion model gained micro-average AUCs ranging from 0.700 to 0.909 and 0.640 to 0.847 on the internal and external test sets, respectively. The model showed the highest micro-average AUC of 0.909, accuracy of 84.3%, micro-average sensitivity of 84.3%, and micro-average specificity of 92.1% in those with only X-rays on the internal test set. On the external test set, the PBTC-TransNet fusion model gained the highest micro-average AUC of 0.847 for patients with X-ray + CT.
We successfully developed and externally validated the transformer-based PBTC-Transnet fusion model for the effective classification of primary bone tumors. This model, rooted in incomplete multimodal images and clinical characteristics, effectively mirrors real-life clinical scenarios, thus enhancing its strong clinical practicability.
准确地对原发性骨肿瘤进行分类对于指导治疗决策至关重要。美国国家综合癌症网络指南建议采用多模态图像为原发性骨肿瘤的综合评估提供不同视角。然而，在临床实践中，大多数患者的医学多模态图像往往并不完整。本研究旨在使用来自 X 射线、CT 和 MRI 的患者不完整的多模态图像以及临床特征构建一种深度学习模型，以对原发性骨肿瘤进行良性、中间性或恶性分类。
本回顾性研究共纳入了 2010 年 1 月至 2022 年 12 月期间来自两个中心的经组织病理学证实的 1305 例原发性骨肿瘤患者（内部数据集，n=1043；外部数据集，n=262）。我们提出了一种基于原发性骨肿瘤分类变压器网络（PBTC-TransNet）的融合模型来对原发性骨肿瘤进行分类。计算了受试者工作特征曲线下的面积（AUC）、准确率、敏感度和特异度，以评估模型的分类性能。
在内部和外部测试集上，PBTC-TransNet 融合模型的微平均 AUC 分别为 0.847（95%CI：0.832，0.862）和 0.782（95%CI：0.749，0.817），表现出令人满意的性能。对于良性、中间性和恶性原发性骨肿瘤的分类，模型在内部/外部测试集上分别获得了 0.827/0.727、0.740/0.662 和 0.815/0.745 的 AUC。此外，在根据成像方式分布对所有患者亚组进行分层后，PBTC-TransNet 融合模型在内部和外部测试集上分别获得了 0.700 至 0.909 和 0.640 至 0.847 的微平均 AUC。在内部测试集上，该模型在仅 X 射线的患者中获得了最高的微平均 AUC 0.909、准确率 84.3%、微平均敏感度 84.3%和微平均特异性 92.1%。在外部测试集上，PBTC-TransNet 融合模型在 X 射线+CT 的患者中获得了最高的微平均 AUC 0.847。
我们成功开发并在外部验证了基于变压器的 PBTC-Transnet 融合模型，用于原发性骨肿瘤的有效分类。该模型基于不完整的多模态图像和临床特征，有效地反映了现实临床情况，从而增强了其强大的临床实用性。
