目的:开发用于自动和同时检测的集成多任务深度学习(DL)框架,分割,基于多中心多参数MRI的原发性骨肿瘤(PBT)和骨感染的分类。
方法:这项回顾性研究将来自两家医院的749例PBT或骨感染患者分为一组训练组(N=557),内部验证集(N=139),和外部验证集(N=53)。集成框架是使用T1加权图像(T1WI)构建的,T2加权图像(T2WI),和临床特征为二元(PBT/骨感染)和三类(良性/中度/恶性PBT)分类。使用联合交集(IoU)和Dice评分评估检测和分割性能。使用接收器工作特征(ROC)曲线评估分类性能,并与放射科医生的解释进行比较。
结果:在外部验证集上,单个基于T1WI和基于T2WI的多任务模型在检测时获得的IoU为0.71±0.25/0.65±0.30,在分割时获得的Dice评分为0.75±0.26/0.70±0.33。该框架的AUC为0.959(95CI,0.955-1.000)/0.900(95CI,0.773-0.100),准确率为90.6%(95CI,79.7-95.9%)/78.3%(95CI,58.1-90.3%)。同时,对于三类分类,该框架的性能优于三名初级放射科医生(准确率:65.2%,69.6%,和69.6%,分别),并与两名高级放射科医师的准确率相当(准确率:78.3%和78.3%)。
结论:基于MRI的集成多任务框架在自动和同时检测方面显示出有希望的性能,分段,并对PBTs和骨感染进行分类,比初级放射科医生更可取。
结论:与初级放射科医生相比,集成多任务深度学习框架有效地提高了原发性骨肿瘤或骨感染患者的鉴别诊断。这一发现可以帮助医生做出治疗决定,并能够及时治疗患者。
结论:•融合多参数MRI和临床特征的集成框架有效地提高了单模态模型的分类能力。•集成多任务深度学习框架在检测方面表现良好,分段,并对原发性骨肿瘤和骨感染进行分类。•集成框架实现了优于初级放射科医师解释的最佳分类性能,协助原发性骨肿瘤和骨感染的临床鉴别诊断。
OBJECTIVE: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center.
METHODS: This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations.
RESULTS: On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%).
CONCLUSIONS: The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists.
CONCLUSIONS: Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients.
CONCLUSIONS: • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists\' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.