【摘要】 目的 评价舒林酸治疗结直肠息肉的有效性和安全性。 方法 计算机检索PubMed、Cochrane Iibrary、Embase、SCI、CNKI、万方、维普、CBM数据库。按Cochrane系统评价的方法评价纳入研究质量，并进行Meta分析。 结果 共纳入7个随机对照试验（RCT），共235例患者。Meta分析结果显示舒林酸治疗腺瘤性息肉病（FAP）在有效率、息肉消失率方面与安慰剂比较，差异无统计学意义（Pgt;0.05）；治疗散发性结肠腺瘤性息肉病（SCAP）在有效率、息肉消失率、腺瘤直径变化方面与安慰剂比较，差异有统计学意义（Plt;0.05）；舒林酸的不良反应多为消化道症状，与安慰剂比较差异有统计学意义（Plt;0.05）。 结论 系统评价结果显示舒林酸对于家族性FAP的疗效尚不确切，而对SCAP有一定的疗效。【关键词】结直肠息肉；舒林酸；有效性；不良反应；系统评价【Abstract】 Objective To assess the efficacy and safety of sulindac on colorectal polyps. Methods The literatures were searched from several databases including PubMed,Cochrane Iibrary,SCI,CNKI,Wanfang,VIP,and CBM. The quality of the researches was evaluated according to Cochrane systematic reviews, and the Meta analysis was performed. Results Seven RCT were enrolled with a total of 235 patients. Meta analysis showed that there was no significant difference in the effective rate and polyps disappearance rate of FAP between the two groups (Pgt;0.05). There were significant differences in the effective rate, polyps disappearance rate and size of adenomas between the two groups (Plt;0.05); the most common adverse event was the symptoms of digestive tract which differed much from that in the placebo group (Plt;0.05). Conclusion The therapeutic effect of sulindac on FAP is not sure, but it is effective on SCAP.
ObjectiveTo investigate the clinical significance of CK20 mRNA expression in blood of patients with colorectal cancer. MethodsThe expressions of CK20 mRNA in blood of twenty healthy volunteers, ten patients with colorectal polyp and sixtyone patients with colorectal cancer were detected by RT-PCR. ResultsThe positive rate of CK20 mRNA in peripheral venous blood and portal venous blood of patients with colorectal cancer were 41.0%(25/61) and 45.9%(28/61), which was not significantly different (Pgt;0.05). The expression of CK20 mRNA in patients with colorectal cancer was associated with clinical TNM stage of tumor, local lymph node metastasis, distance metastasis, and the depth of invasion (Plt;0.05). No expression of CK20 mRNA was detected in blood of twenty healthy volunteer’s and ten patients with colorectal polyp. ConclusionCK20 is a specific marker for detecting blood micrometastasis of colorectal cancer. The expression of CK20 mRNA in blood of patients with colorectal cancer is related with TNM stage, invasion, and metastasis of colorectal cancer.
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network’s ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.