Background: Head and neck cancer is highly prevalent in Taiwan. Its treatment mainly relies on clinical staging, usually diagnosed from images. A major part of the diagnosis is whether lymph nodes are involved in the tumor. We present an algorithm for analyzing clinical images that integrates a deep learning model with image processing and attempt to analyze the features it uses to classify lymph nodes. Methods: We retrospectively collected pretreatment computed tomography images and surgery pathological reports for 271 patients diagnosed with, and subsequently treated for, naïve oral cavity, oropharynx, hypopharynx, and larynx cancer between 2008 and 2018. We chose a 3D UNet model trained for semantic segmentation, which was evaluated for inference in a test dataset of 29 patients. Results: We annotated 2527 lymph nodes. The detection rate of all lymph nodes was 80%, and Dice score was 0.71. The model has a better detection rate at larger lymph nodes. For those identified lymph nodes, we found a trend where the shorter the short axis, the more negative the lymph nodes. This is consistent with clinical observations. Conclusions: The model showed a convincible lymph node detection on clinical images. We will evaluate and further improve the model in collaboration with clinical physicians.
背景：头颈癌在台湾地区具有较高的发病率。其治疗方案主要依据临床分期，通常通过影像学检查进行诊断。诊断的关键环节之一是判断淋巴结是否受肿瘤侵犯。本研究提出一种结合深度学习模型与图像处理的临床影像分析算法，并尝试解析其用于淋巴结分类的特征依据。 方法：回顾性收集2008年至2018年间271例初诊口腔、口咽、下咽及喉癌患者的治疗前计算机断层扫描影像及手术病理报告。采用经语义分割训练的3D UNet模型，并在包含29例患者的测试数据集上进行推理评估。 结果：共标注2527个淋巴结。模型对所有淋巴结的检测率为80%，Dice相似系数为0.71。模型对较大淋巴结表现出更优的检测性能。在已识别的淋巴结中，观察到短轴径越小则淋巴结阴性概率越高的趋势，这与临床观察结果一致。 结论：该模型在临床影像中展现出可靠的淋巴结检测能力。我们将与临床医师合作，进一步评估并优化该模型。
肿瘤(癌症)患者之家