HighlightsRectovaginal fistula (RVF) is one of the complications after low anterior resection for rectal cancer.RVF has been considered to be refractory to conservative treatment.We report a case of RVF in which conservative treatment was successful.
Background: Major complications in patients with ulcerative colitis (UC) include UC-associated cancer (UCAC) and postoperative pouchitis. We aimed to identify SNPs associated with UCAC/high-grade dysplasia (HGD) and pouchitis. Methods: Patients with UC who underwent ileal pouch-anal anastomosis (IPAA) with >2 years of follow-up after functioning pouches were included. Pouchoscopies were performed at least once to diagnose pouchitis according to the modified pouchitis disease activity index. SNP genotyping was performed for 8 SNPs reportedly associated with UCAC and pouchitis, namely: ELF1 (rs7329174), FCGR2A, (rs1801274), interleukin-1β (IL-1B; rs1143627), ITLN1 (rs2274910), MHC (rs7765379), TNFα (rs1799964), TNFSF15 (rs3810936), and UHMK1 (rs768910), using TaqMan genotyping technologies. We investigated the association of these SNPs with UCAC/HGD and pouchitis. Patients’ background data were retrospectively collected, including the presence of preoperative extraintestinal manifestation (EIM). Results: A total of 91 Japanese patients with UC were included. None of the 8 SNPs were associated with UCAC/HGD in our cohort. Multivariable analyses proved that the presence of preoperative EIM (hazard ratio [HR] 3.313, 95% CI 1.325–8.289) and IL-1B (rs1143627) TT genotype (HR 2.425, 95% CI 1.049–5.61) were independent predictive factors for the development of overall pouchitis. The presence of preoperative EIM (HR 3.977, 95% CI 1.292–12.24) and IL-1B (rs1143627 TT genotype; HR 3.382, 95% CI 1.101–10.39) were also independent predictive factors for the development of chronic pouchitis. Conclusions: The IL-1B (rs1143627) TT genotype and preoperative EIM were statistically significant predictors of pouchitis development after IPAA in patients with UC.
Background
The diagnosis of colitis-associated cancer or dysplasia is important in the treatment of ulcerative colitis. Immunohistochemistry of p53 along with hematoxylin and eosin (H&E) staining is conventionally used to accurately diagnose the pathological conditions. However, evaluation of p53 immunohistochemistry in all biopsied specimens is expensive and time-consuming for pathologists. In this study, we aimed to develop an artificial intelligence program using a deep learning algorithm to investigate and predict p53 immunohistochemical staining from H&E-stained slides.
Methods
We cropped 25 849 patches from whole-slide images of H&E-stained slides with the corresponding p53-stained slides. These slides were prepared from samples of 12 patients with colitis-associated neoplasia who underwent total colectomy. We annotated all glands in the whole-slide images of the H&E-stained slides and grouped them into 3 classes: p53 positive, p53 negative, and p53 null. We used 80% of the patches for training a convolutional neural network (CNN), 10% for validation, and 10% for final testing.
Results
The trained CNN glands were classified into 2 or 3 classes according to p53 positivity, with a mean average precision of 0.731 to 0.754. The accuracy, sensitivity (recall), specificity, positive predictive value (precision), and F-measure of the prediction of p53 immunohistochemical staining of the glands detected by the trained CNN were 0.86 to 0.91, 0.73 to 0.83, 0.91 to 0.92, 0.82 to 0.89, and 0.77 to 0.86, respectively.
Conclusions
Our trained CNN can be used as a reasonable alternative to conventional p53 immunohistochemical staining in the pathological diagnosis of colitis-associated neoplasia, which is accurate, saves time, and is cost-effective.
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