Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22–0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18–0.34 and 0.18–0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002andp=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.
基于神经网络的结果预测有望进一步实现头颈癌患者的个体化治疗。在病例数量有限的情况下，神经网络模型的开发面临挑战。为此，本研究探讨通过同步优化两个独立结局目标（多结局）并结合肿瘤分割任务的多任务学习策略，能否提升卷积神经网络（CNN）和视觉变换器（ViT）的性能。模型训练分别在两个多中心数据集上进行，分别以局部区域控制（LRC）和无进展生存期（PFS）为研究终点。第一个数据集包含290例患者的治疗前计算机断层扫描（CT）影像，第二个数据集包含224例患者的正电子发射断层扫描（PET）/CT融合影像。通过一致性指数（C-index）评估模型判别性能，并采用对数秩检验进行风险分层评估。在两个数据集中，CNN与ViT集成模型均取得相近结果。多任务学习策略在多数研究中表现优异，其中结合分割损失训练的多结局CNN模型被确定为跨队列最优策略。在PET/CT数据集上，采用分割损失训练的多结局CNN集成模型获得最佳判别性能（C-index：0.29，95%置信区间：0.22–0.36），并能成功将患者划分为疾病进展低风险组与高风险组（p=0.003）。在CT数据集上，采用分割损失训练的多结局CNN集成模型与单结局ViT集成模型表现最优（C-index分别为0.26和0.26，置信区间分别为0.18–0.34和0.18–0.35），在独立验证中均对LRC实现显著风险分层（p=0.002和p=0.011）。基于近期已完成入组的前瞻性验证研究，计划对开发的多任务学习模型开展进一步验证。
肿瘤(癌症)患者之家