目的 探讨阑尾低级别粘液性肿瘤(LAMN)的CT表现,提高对本病的诊断和认识水平.方法 回顾性分析浙江省湖州市中心医院2018年6月～2022年6月27例经病理证实的LAMN患者的临床、影像及病理资料.结果 27例LAMN均位于右下腹回盲区,呈单房囊性肿块,最长径(25.8±7.8)mm;表现为长管状17例,不规则囊状7例,类圆形3例;边界清楚25例,边界不清2例.CT平扫囊壁均匀或轻度不均匀增厚,其中5例伴钙化,2例伴壁结节;囊内容物密度均匀,CT值(19.8±3.7)HU.增强扫描23例囊壁或壁结节在动脉期和门脉期呈渐进性轻度强化,4例无明显强化.3例合并肠套叠,3例合并穿孔,1例瘤体破裂伴盆腔假性粘液瘤种植.结论 当回盲部囊性肿块CT影像表现为长管状或不规则囊状,边界清楚,最长径＞20mm,增强呈渐进性轻度强化时,应考虑到LAMN的可能.

目的 分析高场强MRI联合血清细胞角蛋白19片段(CYFRA21-1)、糖类抗原72-4(CA72-4)、癌症抗原125(CA125)鉴别食管癌T分期的价值.方法 选取2017年7月—2020年1月在上海交通大学医学院附属瑞金医院、湖州市中心医院拟行手术治疗的食管癌患者104例为研究对象,分析高场强MRI的图像质量,高场强MRI及其联合血清CYFRA21-1、CA72-4、CA125,采用Kappa 一致性分析鉴别食管癌T分期的价值.结果 高场强MRI图片质量2分6例、3分16例、4分40例、5分42例;高场强MRI诊断食管癌T分期的准确率为92.30％(96/104),高场强MRI联合血清CYFRA21-1、CA72-4、CA125诊断食管癌T分期的准确率为95.19％(99/104),与单一MRI诊断食管癌T分期的准确率差异有统计学意义(P＜0.05);但诊断T2分期的特异度、准确率、阳性预测值,诊断T3分期的敏感度、特异度、准确率、阳性预测值、阴性预测值,诊断T4a分期的敏感度、准确率、阴性预测值均较单一高场强MRI诊断上升.结论 单一高场强MRI或联合血清CYFRA21-1、CA72-4、CA125鉴别食管癌T分期均可获得良好效能,联合诊断效能略有提升.

目的 探讨基于增强CT图像特征的列线图模型在预测胃肠道间质瘤(GIST)危险度分级中的价值.方法 回顾2017年1月至2021年6月湖州市中心医院行增强CT检查并经内镜或手术病理检查证实为GIST的135例患者资料.根据病理分级将患者分为潜在恶性组74例(极低危险度43例,低危险度31例)和恶性组61例(中危险度24例,高危险度37例).对两组间的增强CT特征进行统计学分析,将差异有统计学意义的特征纳入多因素logistic回归分析,筛选出预测GIST危险度分级的独立危险因素,并构建列线图预测模型.结果 两组患者的肿瘤最大径、部位、生长方式、边界、形态、液化坏死、强化方式、静脉期CT值比较差异均有统计学意义(均P<0.05).多因素logistic回归分析结果显示,肿瘤最大径(OR=2.636,95％CI:1.180～5.890,P<0.05)、形态(OR=0.055,95％CI:0.005～0.570,P<0.05)、液化坏死(OR=0.042,95％CI:0.004～0.434,P<0.05)是预测GIST危险度分级的独立危险因素.利用该3个特征构建术前预测GIST危险度分级的列线图模型,其AUC为0.952,灵敏度为0.950,特异度为0.833,校准曲线与标准曲线拟合度良好.结论 基于增强CT征象的列线图模型对预测GIST危险度分级有较高的价值,可为临床提供一种比较精确的术前量化预测方法.

Background Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options. This study aims to explore the value of a deep learning algorithm for the prediction of hematoma expansion from non-contrast computed tomography (NCCT) scan through external validation. Methods 102 NCCT images of hypertensive intracerebral hemorrhage (HICH) patients diagnosed in our hospital were retrospectively reviewed. The initial computed tomography (CT) scan images were evaluated by a commercial Artificial Intelligence (AI) software using deep learning algorithm and radiologists respectively to predict hematoma expansion and the corresponding sensitivity, specificity and accuracy of the two groups were calculated and compared. Comparisons were also conducted among gold standard hematoma expansion diagnosis time, AI software diagnosis time and doctors’ reading time. Results Among 102 HICH patients, the sensitivity, specificity, and accuracy of hematoma expansion prediction in the AI group were higher than those in the doctor group(80.0% vs 66.7%, 73.6% vs 58.3%, 75.5% vs 60.8%), with statistically significant difference ( p < 0.05). The AI diagnosis time (2.8 ± 0.3 s) and the doctors’ diagnosis time (11.7 ± 0.3 s) were both significantly shorter than the gold standard diagnosis time (14.5 ± 8.8 h) ( p < 0.05), AI diagnosis time was significantly shorter than that of doctors ( p < 0.05). Conclusions Deep learning algorithm could effectively predict hematoma expansion at an early stage from the initial CT scan images of HICH patients after onset with high sensitivity and specificity and greatly shortened diagnosis time, which provides a new, accurate, easy-to-use and fast method for the early prediction of hematoma expansion.

OBJECTIVE:Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade.MATERIALS AND METHODS:A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model.RESULTS:A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948.CONCLUSION:The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.