目的:使用对比增强计算机断层扫描(CECT)和深度学习技术来开发深度学习影像组学列线图(DLRN),以术前预测胸腺上皮肿瘤(TET)患者的风险状况。
方法:在2008年10月至2020年5月之间,从三个医疗中心招募了257例经手术和病理证实的TET患者。我们使用基于变压器的卷积神经网络从所有病变中提取深度学习特征,并使用选择器算子回归和最小绝对收缩创建深度学习签名(DLS)。结合临床特征的DLRN的预测能力,通过受试者工作特征曲线的曲线下面积(AUC)评估主观CT表现和DLS.
结果:要构建DLS,从116个低风险TET中选择了25个系数为非零的深度学习特征(亚型A,AB,和B1)和141个高风险TET(亚型B2、B3和C)。诸如浸润和DLS的主观CT特征的组合在区分TET风险状态方面表现最佳。训练中的AUC,内部验证,外部验证1和2个队列为0.959(95%置信区间[CI]:0.924-0.993),0.868(95%CI:0.765-0.970),0.846(95%CI:0.750-0.942),和0.846(95%CI:0.735-0.957),分别。曲线分析中的DeLong检验和决策揭示DLRN是最具预测性和临床上有用的模型。
结论:由CECT衍生的DLS和主观CT表现组成的DLRN在预测TET患者的风险状态方面表现出很高的性能。
结论:对胸腺上皮性肿瘤(TET)进行准确的风险状态评估可能有助于确定术前是否需要新辅助治疗。结合基于增强CT的深度学习特征的深度学习影像组学列线图,临床特征,和主观的CT检查结果有可能预测TET的组织学亚型,这可以促进临床实践中的决策和个性化治疗。
结论:•可以预测病理风险状态的非侵入性诊断方法可能对TET患者的预处理分层和预后评估有用。•与深度学习签名相比,DLRN在区分TET的风险状态方面表现出卓越的性能。影像组学签名,或临床模型。•曲线分析中的DeLong测试和决策揭示DLRN在区分TET的风险状态方面最具预测性和临床有用性。
OBJECTIVE: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).
METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve.
RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model.
CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs.
CONCLUSIONS: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice.
CONCLUSIONS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.