PDF(Pubmed)
Abstract：
UNASSIGNED: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC.
UNASSIGNED: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC.
UNASSIGNED: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD).
UNASSIGNED: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models.
UNASSIGNED: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.
UNASSIGNED: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC.
UNASSIGNED: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD).
UNASSIGNED: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models.
UNASSIGNED: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.
摘要：
■准确的肿瘤目标轮廓和T分期对于鼻咽癌（NPC）的精确放射治疗至关重要。手动识别T期和勾画大体肿瘤体积(GTV)是费力且非常耗时的过程。以前的基于深度学习的研究主要集中在肿瘤分割上，很少有研究专门针对NPC的肿瘤分期。
■为了弥合这一差距，我们的目标是设计一个模型，可以同时识别T阶段，并在NPC中执行GTV的准确分割。
■我们开发了一种基于变压器的多任务深度学习模型，该模型可以同时执行两项任务：勾画肿瘤轮廓和识别T分期。我们的回顾性研究涉及2017年至2020年在我们机构收集的320名NPC患者（T期：T1-T4）的对比增强T1加权图像（CE-T1WI）。被随机分配到三个队列中进行三次交叉验证，并使用独立的测试集进行外部验证。我们使用接收器工作特征曲线下面积（ROC-AUC）和准确性（ACC）评估了预测性能，95%的置信区间(CI)，以及使用Dice相似系数（DSC）和平均表面距离（ASD）的轮廓性能。
■我们的多任务模型在320名患者的GTV轮廓（中位DSC：0.74；ASD：0.97mm）和T分期（AUC：0.85，95％CI：0.82-0.87）方面表现良好。在早期T类肿瘤中，该模型的DSC中位数为0.74，ASD为0.98mm，而在晚期T类肿瘤中，它达到0.74的中值DSC和0.96mm的ASD。早期阶段（T1-T2）自动T分期的准确性为76％（166个中的126个），晚期阶段（T3-T4）为64％（154个中的99个）。此外，实验结果表明，我们的多任务模型优于其他单任务模型。
■这项研究强调了多任务模型同时描绘肿瘤轮廓和识别T分期的潜力。多任务模型利用这些相互关联的学习任务之间的协同作用，从而提高了这两项任务的性能。该性能证明了我们的工作在描绘肿瘤轮廓和识别T分期方面的潜力，并表明它可以成为支持临床精确放射治疗的实用工具。
■为了弥合这一差距，我们的目标是设计一个模型，可以同时识别T阶段，并在NPC中执行GTV的准确分割。
■我们开发了一种基于变压器的多任务深度学习模型，该模型可以同时执行两项任务：勾画肿瘤轮廓和识别T分期。我们的回顾性研究涉及2017年至2020年在我们机构收集的320名NPC患者（T期：T1-T4）的对比增强T1加权图像（CE-T1WI）。被随机分配到三个队列中进行三次交叉验证，并使用独立的测试集进行外部验证。我们使用接收器工作特征曲线下面积（ROC-AUC）和准确性（ACC）评估了预测性能，95%的置信区间(CI)，以及使用Dice相似系数（DSC）和平均表面距离（ASD）的轮廓性能。
■我们的多任务模型在320名患者的GTV轮廓（中位DSC：0.74；ASD：0.97mm）和T分期（AUC：0.85，95％CI：0.82-0.87）方面表现良好。在早期T类肿瘤中，该模型的DSC中位数为0.74，ASD为0.98mm，而在晚期T类肿瘤中，它达到0.74的中值DSC和0.96mm的ASD。早期阶段（T1-T2）自动T分期的准确性为76％（166个中的126个），晚期阶段（T3-T4）为64％（154个中的99个）。此外，实验结果表明，我们的多任务模型优于其他单任务模型。
■这项研究强调了多任务模型同时描绘肿瘤轮廓和识别T分期的潜力。多任务模型利用这些相互关联的学习任务之间的协同作用，从而提高了这两项任务的性能。该性能证明了我们的工作在描绘肿瘤轮廓和识别T分期方面的潜力，并表明它可以成为支持临床精确放射治疗的实用工具。
