Objective: Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). It has a high diagnostic accuracy rate in differentiating gastric gastrointestinal stromal tumor (GIST) with a diameter greater than 5 cm from gastric benign SMT. This study aimed to use deep learning algorithms to establish a diagnosis model (GISTNet) based on contrast-enhanced CT and evaluate its diagnostic value in distinguishing gastric GIST with a diameter ≤ 5 cm and other gastric SMT before surgery. Methods: A diagnostic test study was carried out. Clinicopathological data of 181 patients undergoing resection with postoperative pathological diagnosis of gastric SMT with a diameter ≤ 5 cm at Department of Gastrointestinal Surgery of Renji Hospital from September 2016 to April 2021 were retrospectively collected. After excluding 13 patients without preoperative CT or with poor CT imaging quality, a total of 168 patients were enrolled in this study, of whom, 107 were GIST while 61 were benign SMT (non-GIST), including 27 leiomyomas, 24 schwannomas, 6 heterotopic pancreas and 4 lipomas. Inclusion criteria were as follows: (1) gastric SMT was diagnosed by contrast-enhanced CT before surgery; (2) preoperative gastroscopic examination and biopsy showed no abnormal cells; (3) complete clinical and pathological data. Exclusion criteria were as follows: (1) patients received anti-tumor therapy before surgery; (2) without preoperative CT or with poor CT imaging quality due to any reason; (3) except GIST, other gastric malignant tumors were pathologically diagnosed after surgery. Based on the hold-out method, 148 patients were randomly selected as the training set and 20 patients as the test set of the GISTNet diagnosis model. After the GISTNet model was established, 5 indicators were used for evaluation in the test set, including sensitivity, specificity, positive predictive value, negative predictive value and the area under the receiver operating curve (AUC). Then GISTNet diagnosis model was compared with the GIST-risk scoring model based on traditional CT features. Besides, in order to compare the accuracy of the GISTNet diagnosis model and the imaging doctors in the diagnosis of gastric SMT imaging, 3 radiologists with 3, 9 and 19 years of work experience, respectively, blinded to clinical and pathological information, tested and judged the samples. The accuracy rate between the three doctors and the GISTNet model was compared. Results: The GISTNet model yielded an AUC of 0.900 (95% CI: 0.827-0.973) in the test set. When the threshold value was 0.345, the sensitivity specificity, positive and negative predictive values of the GISTNet diagnosis model was 100%, 67%, 75% and 100%, respectively. The accuracy rate of the GISTNet diagnosis model was better than that of the GIST-risk model and the manual readings from two radiologists with 3 years and 9 years of work experience (83% vs. 75%, 60%, 65%), and was close to the manual reading of the radiologist with 19 years of work experience (83% vs. 80%). Conclusion: The deep learning algorithm based on contrast-enhanced CT has favorable and reliable diagnostic accuracy in distinguishing gastric GIST with a diameter ≤ 5 cm and other gastric SMT before operation.
目的： 增强CT是术前诊断和评估胃黏膜下肿瘤（SMT）恶性潜能的重要检查手段，在区分直径>5 cm胃的胃肠间质瘤（GIST）和胃良性SMT中有较高的诊断准确率。本研究拟使用深度学习算法建立基于增强CT的鉴别诊断模型GISTNet，评估其在术前鉴别直径≤5 cm的胃GIST和其他胃SMT的预测价值。 方法： 采用诊断性试验研究方法。回顾性收集2016年9月至2021年4月期间，上海交通大学医学院附属仁济医院胃肠外科连续性收治的181例接受手术、且术后病理证实为肿瘤直径≤5 cm胃SMT患者，排除13例CT图像质量不佳者，共计168例患者纳入研究。其中107例为GIST，61例为非GIST的SMT（non-GIST），其中术后病理27例为平滑肌瘤，24例为神经鞘瘤，6例为异位胰腺，4例为脂肪瘤。病例纳入标准：（1）手术前经增强CT诊断为胃SMT的患者；（2）术前完善胃镜且活检病理未见异型细胞；（3）临床、病理资料齐全。排除标准：（1）手术前接受过抗肿瘤药物治疗；（2）无影像或任何原因导致的CT图像质量不佳；（3）术后病理诊断为除GIST外的其他胃恶性肿瘤。将研究对象根据留出法（hold-out method）随机划分为GIST鉴别诊断模型（GISTNet）的训练集（148例）和测试集（20例），用于GISTNet诊断模型的训练及其性能评估。GISTNet模型建立后，在测试集采用5个指标进行评估，即灵敏度、特异度、阳性预测值、阴性预测值和受试者工作曲线（ROC）计算的曲线下面积（AUC）。进一步将GISTNet诊断模型与现有文献报道的传统影像学征象所组成的模型比较。此外，为了比较深度学习模型与影像科医生对胃SMT影像诊断的准确性，3位工作经验分别为3、9、19年的影像科医生、在隐藏临床病理信息的情况下，对测试集中的样本进行判断，将3位医生的准确率与GISTNet模型相对比。 结果： GISTNet模型在测试集上获得了0.900（95% CI：0.827~0.973）的AUC，当阈值为0.345时，GISTNet模型的灵敏度为100%，特异度为67%，阳性预测值为75%，阴性预测值为100%。GISTNet模型的准确率为83%，优于GIST-Risk模型（75%）和两位低年资影像科医生（60%和65%），并与工作经验为19年的影像科医生接近（80%）。 结论： 基于增强CT的深度学习算法对术前鉴别直径≤5 cm的胃GIST和其他胃SMT具有良好、可靠的诊断准确率。.
Keywords: Artificial intelligence; Computed tomography; Deep learning; Differential diagnosis; Gastrointestinal stromal tumor, stomach.