Computed tomography diagnosed first time diverticulitis and colorectal cancer

Italian guidelines for the diagnosis and management of colonic diverticulosis and diverticular disease

Colonic diverticulosis and diverticular disease are among the most common gastrointestinal disorders encountered in clinical practice. These Italian guidelines focus on the diagnosis and management of diverticulosis and diverticular disease in the adult population, providing practical and evidence-based recommendations for clinicians. Experts from five Italian scientific societies, constituting a multidisciplinary panel, conducted a comprehensive review of meta-analyses, systematic reviews, randomised controlled trials, and observational studies to formulate 14 PICO questions. The assessment of the quality of the evidence and the formulation of the recommendations were carried out using an adaptation of the GRADE methodology. The guidelines covered the following topics: i) Management of diverticulosis; ii) Symptomatic uncomplicated diverticular disease: diagnosis and treatment; iii) Acute diverticulitis: diagnosis and treatment; iv) Management of diverticular disease complications; v) Prevention of recurrent acute diverticulitis; vi) Interventional management of diverticular disease.

Medical Tumor Image Classification Based on Few-Shot Learning

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Diverticulitis Is Associated with Increased Risk of Colon Cancer-A Nationwide Register-Based Cohort Study

Background: An association between diverticulitis and colon cancer has been proposed. The evidence is conflicting, and the guidelines differ regarding recommended follow-up with colonoscopy after an episode of diverticulitis. To guide regimes for follow-up, this study aimed to investigate if patients with diverticulitis have an increased risk of colon cancer. Methods: This study is reported according to the RECORD statement. We performed a cohort study with linked data from nationwide Danish registers. The inclusion period was 1997-2009, and the complete study period was 1995-2013. The primary outcome was the risk of developing colon cancer estimated using a Cox regression analysis with time-varying covariates. We performed a sensitivity analysis on a cohort of people with prior colonoscopies, comparing the risk of colon cancer between the diverticulitis group and the control group. Results: We included 29,173 adult males and females with diverticulitis and 145,865 controls matched for sex and age. The incidence proportion of colon cancer was 2.1% (95% confidence interval (CI) 1.9-2.3) in the diverticulitis group and 1.5% (95% CI 1.4-1.5) in the matched control group (hazard ratio 1.6; 95% CI 1.5-1.8). The risk of having a colon cancer diagnosis was significantly increased in the first six months after inclusion (hazard ratio 1.7; 95% CI 1.5-1.8), and hereafter there was a lower risk in the diverticulitis group compared with controls (hazard ratio 0.8; 95% CI 0.7-0.9). This protective effect lasted eight years. The increased risk of colon cancer during the first six months after diverticulitis was also found in the cohort with prior colonoscopies. Conclusions: The risk of a colon cancer diagnosis was significantly increased for patients with diverticulitis 0-6 months after the diagnosis of diverticulitis. Hereafter, we found a protective effect of diverticulitis until eight years later, possibly due to a screening effect. We recommend a follow-up colonoscopy after the first diagnosis of diverticulitis.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images

Importance

Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care.

Objective

To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study.

Design, Setting, and Participants

In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support.

Main Outcomes and Measures

To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons.

Results

A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001).

Conclusions and Relevance

The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.