Li Qi, Zhou Zhenghao, Chen Yukun, Yu Jieyu, Zhang Hao, Meng Yinghao, Zhu Mengmeng, Li Na, Zhou Jian, Liu Fang, Fang Xu, Li Jing, Wang Tiegong, Lu Jianping, Zhang Teng, Xu Jun, Shao Chengwei, Bian Yun
Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China.
Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, No. 219 Ning Liu Road, Nanjing, 210044, Jiangsu, China.
Abdom Radiol (NY). 2023 Jun;48(6):2074-2084. doi: 10.1007/s00261-023-03801-8. Epub 2023 Mar 25.
To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).
This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models' performances were determined from their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis.
Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47 years; 251 men) and PASC (age, 61.99 ± 9.82 years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model.
Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC.
开发并验证一种基于自动磁共振成像（MRI）的模型，以在术前区分胰腺腺鳞癌（PASC）和胰腺导管腺癌（PDAC）。
这项回顾性研究纳入了2011年1月至2020年12月期间接受MRI检查、经手术切除且组织病理学确诊为PASC或PDAC的患者。根据治疗时间，将他们分为训练集和验证集。基于深度学习的自动人工智能用于胰腺肿瘤分割。利用传统MRI和放射组学特征进行线性判别分析，在训练集中建立临床、放射组学和混合模型。通过判别能力和临床实用性来确定模型的性能。采用Kaplan-Meier法和对数秩检验进行生存分析。
总体上，分别纳入了389例PDAC患者（年龄61.37±9.47岁；男性251例）和123例PASC患者（年龄61.99±9.82岁；男性78例）；他们被分为训练集（n = 358）和验证集（n = 154）。混合模型在训练集和验证集中均表现出良好的性能（曲线下面积分别为0.94和0.96）。训练集的敏感性、特异性和准确性分别为76.74%、93.38%和89.39%，验证集的敏感性、特异性和准确性分别为67.57%、97.44%和90.26%。在验证集中，混合模型的表现优于临床模型（p = 0.001）和放射组学模型（p = 0.04）。根据混合模型，对数秩检验显示预测的PDAC组的生存期明显长于预测的PASC组（p = 0.003）。
我们的混合模型结合了MRI和放射组学特征，可用于区分PASC和PDAC。
