We introduce an innovative, simple, effective segmentation-free approach for outcome prediction in head \& neck cancer (HNC) patients. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) volumes, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained to perform automatic cropping of the head and neck region on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method.

翻译：本文提出了一种创新、简洁且有效的免分割方法，用于头颈癌患者的预后预测。该方法利用基于深度学习的特征提取技术，结合应用于氟代脱氧葡萄糖正电子发射断层扫描体积的多角度最大强度投影，无需对原发肿瘤及受累淋巴结等感兴趣区域进行人工分割。取而代之的是，我们训练了一个先进的目标检测模型，在PET体积上自动裁剪头颈部区域。随后，采用预训练的深度卷积神经网络骨干，从经过72次多角度轴向旋转的裁剪后PET体积所获得的MA-MIP中提取深度特征。从PET体积多个投影视角提取的这些深度特征经过聚合与融合后，应用于489例头颈癌患者的无复发生存分析。所提出的方法在目标数据集的无复发生存分析任务中，性能优于现有最佳方法。通过规避FDG PET-CT图像上恶性肿瘤的人工勾画，本方法消除了对主观解释的依赖，显著提升了所提生存分析方法的可重复性。