预后预测对于指导局部区域进展性鼻咽癌（LA-NPC）患者的个体化治疗至关重要。最近，多任务深度学习在各种癌症中进行了联合预后预测和肿瘤分割的探索，取得了令人满意的表现。本研究旨在评估多任务深度学习在LA-NPC患者预后预测中的临床价值。 从两家医疗中心获得的886例LA-NPC患者被纳入研究，包括临床数据、[18F]FDG PET/CT图像和进展无病生存期（PFS）的随访数据。我们采用了深度多任务生存模型（DeepMTS）来同时进行预后预测（DeepMTS得分）和FDG-PET/CT图像的肿瘤分割。利用DeepMTS生成的分割掩膜提取手工制作的放射学特征，这些特征也被用于预后预测（AutoRadio得分）。最后，我们通过整合DeepMTS得分、AutoRadio得分和临床数据，开发了基于多任务深度学习的放射学（MTDLR）标度表。采用Harrell的一致性指数（C-index）和独立于时间的受试者工作特征曲线（ROC）分析来评估所提出的MTDLR标度表的判别能力。为了进行患者分层，采用Kaplan-Meier方法计算高危和低危患者的PFS率，并与观察到的PFS概率进行比较。 我们的MTDLR标度表在训练、内部验证和外部验证队列中分别达到了0.818（95%置信区间（CI）：0.785-0.851）、0.752（95% CI：0.638-0.865）和0.717（95% CI：0.641-0.793）的C-index和0.859（95% CI：0.822-0.895）、0.769（95% CI：0.642-0.896）和0.730（95% CI：0.634-0.826）的曲线下面积（AUC），这显示出与传统放射学标度表相比具有显著的改进。我们的标度表还将患者分为显著不同的高危和低危组。 我们的研究表明，MTDLR标度表可以在LA-NPC患者中进行可靠准确的预后预测，并实现更好的患者分层，这有助于个性化治疗规划。

Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability.Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups.Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.© 2023. The Author(s).