Objective: To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value. Methods: In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated. Results: Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma. Conclusion: Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.
目的： 验证基于快速区域卷积神经网络（Faster R-CNN）胰腺癌增强CT自动识别系统，并探讨其临床应用价值。 方法： 回顾性收集青岛大学附属医院2013年1月至2016年5月收治的315例胰腺癌患者的4 024张增强CT影像序列，将2 614张影像序列作为训练组输入Faster R-CNN系统，建立影像自动识别模型，通过读取135例胰腺癌的1 410张增强CT影像进行验证。为了进一步测试其临床应用效果，读取150例胰腺占位患者的3 750张增强CT影像并对其诊断结果进行随访。记录结节类别的精准率和召回率，绘制精确回归曲线，分析Faster R-CNN诊断的准确性、灵敏度、特异度，生成受试者工作特征（ROC）曲线，并计算曲线下面积。 结果： 基于135例胰腺癌增强CT影像，得到Faster R-CNN的人工智能辅助诊断的ROC曲线的曲线下面积为0.927，准确性、特异度、灵敏度分别为0.902、0.913、0.801。经过150例胰腺占位患者资料的验证，判定阳性893张，阴性2 857张，Faster R-CNN诊断为胰腺癌患者98例，对其诊断结果进行随访，其中53例经外科手术后病理证实为胰腺导管癌、21例为胰腺囊腺癌、12例为胰腺囊腺瘤、5例为胰腺囊肿，7例患者未手术治疗。在术后5~17个月内6例死于腹腔肿瘤浸润、肝转移或肺转移。在Faster R-CNN诊断为阴性的52例患者中，有9例经外科术后证实为胰腺导管癌。 结论： Faster R-CNN系统能够帮助影像科医师对胰腺癌进行诊断，具有一定的临床应用价值。.
Keywords: Clinical application; Diagnosis; Faster R-CNN; Pancreatic neoplasms.