未经评估:复发风险评估对于局部晚期宫颈癌(LACC)患者具有临床意义。我们基于计算机断层扫描(CT)和磁共振(MR)图像研究了变压器网络在LACC复发风险分层中的能力。
UNASSIGNED:在2017年7月至2021年12月期间,共有104例经病理诊断为LACC的患者纳入本研究。所有患者均行CT和MR扫描,通过活检确定其复发状态。我们将患者随机分为训练队列(48例,非复发:复发=37:11),验证队列(21例,非复发:复发=16:5),和测试队列(35例,非复发:复发=27:8),在此基础上,我们提取了1989、882和315个补丁用于模型开发,验证和评估,分别。变压器网络由三个模态融合模块组成,用于提取多模态多尺度信息,和一个完全连接的模块来执行复发风险预测。模型的预测性能通过六个指标进行评估,包括接收器工作特性曲线下的面积(AUC),准确度,f1-score,灵敏度,特异性和精确性。采用F检验和T检验进行单因素分析进行统计学分析。
UNASSIGNED:拟议的变压器网络在两种培训中都优于传统的影像组学方法和其他深度学习网络,验证和测试队列。特别是,在测试队列中,变压器网络的AUC最高为0.819±0.038,而四种传统的影像组学方法和两种深度学习网络的AUC分别为0.680±0.050、0.720±0.068、0.777±0.048、0.691±0.103、0.743±0.022和0.733±0.027。
UNASSIGNED:多模态变压器网络在LACC的复发风险分层中显示出有希望的性能,可用作帮助临床医生做出临床决策的有效工具。
UNASSIGNED: Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images.
UNASSIGNED: A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model\'s development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model\'s prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis.
UNASSIGNED: The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively.
UNASSIGNED: The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.