■由于其侵袭性和不良预后,恶性股骨骨肿瘤存在相当大的障碍。尽早开始治疗对于增强重要和实际结果至关重要。在这次调查中,深度学习算法将用于分析磁共振成像(MRI)数据,以识别恶性骨肿瘤。
■研究队列包括44名患者,年龄从17岁到78岁(22名女性和22名男性)。要对T1和T2加权MRI数据进行分类,本文提出了一种用于骨肿瘤MRI分类的改进DenseNet网络模型,它被命名为GHA-DenseNet。基于原始的DenseNet模型,增加关注模块,解决了深度卷积模型由于局部感受野的限制,在获取股骨肿瘤组织的位置和内容信息时,能够减少关键特征的丢失的问题。此外,稀疏连接模式用于修剪原始模型的连接模式,以便删除不必要的并保留更有用的快速连接模式,缓解了小数据集大小和图像特征带来的过拟合问题。在一个旨在预测肿瘤恶性肿瘤的临床模型中,T1和T2分类器输出值的利用,结合患者特异性临床信息,是一个关键的组成部分。
■在训练阶段,T1分类器的准确率为92.88%,而T2分类器的准确率为87.03%。两种分类器在整个验证阶段都表现出95.24%的准确度。在培训和验证期间,临床模型的准确率分别为82.17%和81.51%,分别。临床模型的接受者工作特征(ROC)曲线显示了其分离类别的能力。
■所提出的方法不需要对MRI扫描进行手动分割,因为它利用了预训练的深度学习分类器。这些算法具有预测肿瘤恶性和缩短诊断和治疗周转时间的能力。虽然手术只需要很少的放射科医生参与,需要对更大的患者队列进行更多的测试以确认其疗效.
UNASSIGNED: Due to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant.
UNASSIGNED: The study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component.
UNASSIGNED: The T1 classifier\'s accuracy during the training phase was 92.88% whereas the T2 classifier\'s accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model\'s accuracy was 82.17% and 81.51%, respectively. The clinical model\'s receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes.
UNASSIGNED: The proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists\' involvement, more testing on a larger patient cohort is required to confirm its efficacy.