Xia Xianwu, Wang Jiazhou, Liang Sheng, Ye Fangfang, Tian Min-Ming, Hu Weigang, Xu Leiming
The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Department of Oncology Intervention, The Affiliated Municipal Hospital of Taizhou University, Taizhou, China.
Front Oncol. 2022 Nov 24;12:1028382. doi: 10.3389/fonc.2022.1028382. eCollection 2022.
A parotid neoplasm is an uncommon condition that only accounts for less than 3% of all head and neck cancers, and they make up less than 0.3% of all new cancers diagnosed annually. Due to their nonspecific imaging features and heterogeneous nature, accurate preoperative diagnosis remains a challenge. Automatic parotid tumor segmentation may help physicians evaluate these tumors. Two hundred eighty-five patients diagnosed with benign or malignant parotid tumors were enrolled in this study. Parotid and tumor tissues were segmented by 3 radiologists on T1-weighted (T1w), T2-weighted (T2w) and T1-weighted contrast-enhanced (T1wC) MR images. These images were randomly divided into two datasets, including a training dataset (90%) and an validation dataset (10%). A 10-fold cross-validation was performed to assess the performance. An attention base U-net for parotid tumor autosegmentation was created on the MRI T1w, T2 and T1wC images. The results were evaluated in a separate dataset, and the mean Dice similarity coefficient (DICE) for both parotids was 0.88. The mean DICE for left and right tumors was 0.85 and 0.86, respectively. These results indicate that the performance of this model corresponds with the radiologist's manual segmentation. In conclusion, an attention base U-net for parotid tumor autosegmentation may assist physicians to evaluate parotid gland tumors.
腮腺肿瘤是一种罕见疾病，仅占所有头颈癌的不到3%，且在每年新诊断的所有癌症中占比不到0.3%。由于其非特异性的影像学特征和异质性，术前准确诊断仍然是一项挑战。自动腮腺肿瘤分割可能有助于医生评估这些肿瘤。本研究纳入了285例诊断为良性或恶性腮腺肿瘤的患者。3名放射科医生在T1加权（T1w）、T2加权（T2w）和T1加权对比增强（T1wC）磁共振成像上对腮腺和肿瘤组织进行分割。这些图像被随机分为两个数据集，包括一个训练数据集（90%）和一个验证数据集（10%）。进行了10折交叉验证以评估性能。基于注意力机制的U-net在MRI T1w、T2和T1wC图像上创建用于腮腺肿瘤自动分割。在一个单独的数据集中对结果进行评估，双侧腮腺的平均Dice相似系数（DICE）为0.88。左右肿瘤的平均DICE分别为0.85和0.86。这些结果表明该模型的性能与放射科医生的手动分割相当。总之，基于注意力机制的U-net用于腮腺肿瘤自动分割可能有助于医生评估腮腺肿瘤。
