Abstract
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.
Abstract (translated)
我们提出了一个创新、简单、有效的无分割方法，用于预测头颈部癌症（HNC）患者的 outcomes。通过利用基于深度学习的特征提取技术和应用到氟氧葡萄糖正电子发射断层扫描（FDG-PET）卷面的多角度最大强度投影（MA-MIPs），我们的方法消除了对感兴趣区域（ROIs）如原发肿瘤和涉及淋巴结的手动分割的需求。相反，通过训练一种最先进的物体检测模型来自动裁剪头颈部PET卷面，该模型从72个多角度轴向旋转的裁剪PET卷面上提取深度特征。这些从PET卷面的多个投影视图中提取的深度特征被汇总和融合，并用于对489名HNC患者进行无复发的生存分析。与目标数据集上最佳表现的方法相比，我们的方法在无复发生存分析任务上表现优异。通过避免在FDG-PET-CT图像上手动划分肿瘤，我们的方法消除了对主观解释的依赖，极大地提高了所提出的生存分析方法的可重复性。
URL
https://arxiv.org/abs/2405.01756
