Predicting the presence of adenomatous polyps during colonoscopy with National Cancer Institute Colorectal Cancer Risk-Assessment Tool

Development and validation of a nomogram predictive model for colorectal adenoma with low-grade intraepithelial neoplasia using routine laboratory tests: A single-center case-control study in China

Background

Colorectal cancer (CRC) is the third most common cancer in the world and has a high mortality rate. Colorectal adenoma (CRA) is precancerous lesions of CRC. The purpose of the present study was to construct a nomogram predictive model for CRA with low-grade intraepithelial neoplasia (LGIN) in order to identify high-risk individuals, facilitating early diagnosis and treatment, and ultimately reducing the incidence of CRC.

Methods

We conducted a single-center case-control study. Based on the results of colonoscopy and pathology, 320 participants were divided into the CRA group and the control group, the demographic and laboratory test data were collected. A development cohort (n = 223) was used for identifying the risk factors for CRA with LGIN and to develop a predictive model, followed by an internal validation. An independent validation cohort (n = 97) was used for external validation. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate discrimination ability, accuracy and clinical practicability of the model.

Results

Four predictors, namely sex, age, albumin and monocyte count, were included in the predictive model. In the development cohort, internal validation and external validation cohort, the area under the curve (AUC) of this risk predictive model were 0.946 (95%CI: 0.919-0.973), 0.909 (95 % CI: 0.869-0.940) and 0.928 (95%CI: 0.876-0.980), respectively, which demonstrated the model had a good discrimination ability. The calibration plots showed a good agreement and the decision curve analysis (DCA) suggested the predictive model had a high clinical net benefit.

Conclusion

The nomogram model exhibited good performance in predicting CRA with LGIN, which can aid in the early detection of high-risk patients, improve early treatment, and ultimately reduce the incidence of CRC.

Effects of radiofrequency radiation on apoptotic and antiapoptotic factors in colorectal cancer cells

In this study, it is aimed to investigate the effect of radiofrequency radiation (RFR) on apoptotic and antiapoptotic factors under different exposure conditions in human colonic adenocarcinoma cells (Caco-2). We analyzed the effects of 2.5 GHz continuous wave and 3 GPP modulated radiofrequency radiation exposure (15 min on, 15 min off) for 1 h and (1 h on, 1 h off) for 3 hours on Caco-2 cell lines. The cell viability of Caco-2 cells was determined by XTT method. Then, the cells were analyzed by flow cytometry to determine the effects on apoptosis staining with AnnexinV-FITC and PI. Protein expression levels of Bcl-2, Bax, Caspase-3 and Survivin were subsequently analyzed by using flow cytometric methods. Bax, Caspase 8, and Survivin protein levels were also analyzed by western blot. The cell viability rates were not significantly different after 2.5 GHz of RFR exposure for 1 h, but RFR exposure for 3 h at 2.5 GHz frequencies caused a decrease on cell viability of Caco-2 cells. RFR exposure for 1 and 3 hours at 2.5 GHz frequencies resulted in an apoptotic response. Protein analyses of Bcl-2, Bax, Survivin, Caspase-3, and Caspase-8 showed that RFR led to increase the levels of proapoptotic Bax, Caspase-3, and Caspase 8 in Caco-2 cells under different exposure conditions. However, 3-h exposure caused a decrease in antiapoptotic survivin levels. The results of our study indicate that RFR exposure affects the cell death mechanism due to apoptotic pathway.

Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records

BACKGROUND AND AIMS

The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors.

METHODS

We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression).

RESULTS

The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles.

DISCUSSION

Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.

Calcium and dairy products in the chemoprevention of colorectal adenomas: a systematic review and meta-analysis

The risk of transition to colorectal cancer (CRC) in advanced colorectal adenomas (ACAs) is about 2.5 times higher than the non-advanced ones. This systematic review and meta-analysis was performed to determine the effect of calcium and dairy products on the incidence of CAs and ACAs. Six databases were systematically searched and 37 relevant clinical trials and observational studies involving over 10,964 cases were selected for inclusion. The results showed that calcium consumption reduced the risk of CAs incidence by 8% (RR: 0.92; 95% CI: 0.89-0.96), and calcium intake as a food and dairy product reduced it about 21% (RR: 0.79; 95% CI: 0.72-0.86), and 12% (RR: 0.88; 95% CI: 0.78-0.98), respectively. However, calcium supplementation did not show a significant effect on CAs incidence (RR: 0.97; 95% CI: 0.89-1.05). Results also revealed that total calcium intake markedly reduced the risk of ACAs (RR: 0.79; 95% CI: 0.73-0.85) and the risk of recurrence of adenomas about 12% (RR: 0.88; 95% CI: 0.84-0.93). Our results suggest that natural sources of calcium such as dairy products and foods may have more effective role than supplementary calcium in terms of reducing the risk of incidence and recurrence of colorectal adenomas and advanced adenomas.

ACG Clinical Guidelines: Colorectal Cancer Screening 2021

Colorectal cancer (CRC) is the third most common cancer in men and women in the United States. CRC screening efforts are directed toward removal of adenomas and sessile serrated lesions and detection of early-stage CRC. The purpose of this article is to update the 2009 American College of Gastroenterology CRC screening guidelines. The guideline is framed around several key questions. We conducted a comprehensive literature search to include studies through October 2020. The inclusion criteria were studies of any design with men and women age 40 years and older. Detailed recommendations for CRC screening in average-risk individuals and those with a family history of CRC are discussed. We also provide recommendations on the role of aspirin for chemoprevention, quality indicators for colonoscopy, approaches to organized CRC screening and improving adherence to CRC screening. CRC screening must be optimized to allow effective and sustained reduction of CRC incidence and mortality. This can be accomplished by achieving high rates of adherence, quality monitoring and improvement, following evidence-based guidelines, and removing barriers through the spectrum of care from noninvasive screening tests to screening and diagnostic colonoscopy. The development of cost-effective, highly accurate, noninvasive modalities associated with improved overall adherence to the screening process is also a desirable goal.

Advanced neoplasia in Veterans at screening colonoscopy using the National Cancer Institute Risk Assessment Tool

BACKGROUND

Adapting screening strategy to colorectal cancer (CRC) risk may improve efficiency for all stakeholders however limited tools for such risk stratification exist. Colorectal cancers usually evolve from advanced neoplasms that are present for years. We applied the National Cancer Institute (NCI) CRC Risk Assessment Tool, which calculates future risk of CRC, to determine whether it could be used to predict current advanced neoplasia (AN) in a veteran cohort undergoing a baseline screening colonoscopy.

METHODS

This was a prospective assessment of the relationship between future CRC risk predicted by the NCI tool, and the presence of AN at screening colonoscopy. Family, medical, dietary and physical activity histories were collected at the time of screening colonoscopy and used to calculate absolute CRC risk at 5, 10 and 20 years. Discriminatory accuracy was assessed.

RESULTS

Of 3121 veterans undergoing screening colonoscopy, 94% had complete data available to calculate risk (N = 2934, median age 63 years, 100% men, and 15% minorities). Prevalence of AN at baseline screening colonoscopy was 11 % (N = 313). For tertiles of estimated absolute CRC risk at 5 years, AN prevalences were 6.54% (95% CI, 4.99, 8.09), 11.26% (95% CI, 9.28-13.24), and 14.21% (95% CI, 12.02-16.40). For tertiles of estimated risk at 10 years, the prevalences were 6.34% (95% CI, 4.81-7.87), 11.25% (95% CI, 9.27-13.23), and 14.42% (95% CI, 12.22-16.62). For tertiles of estimated absolute CRC risk at 20 years, current AN prevalences were 7.54% (95% CI, 5.75-9.33), 10.53% (95% CI, 8.45-12.61), and 12.44% (95% CI, 10.2-14.68). The area under the curve for predicting current AN was 0.60 (95% CI; 0.57-0.63, p < 0.0001) at 5 years, 0.60 (95% CI, 0.57-0.63, p < 0.0001) at 10 years and 0.58 (95% CI, 0.54-0.61, p < 0.0001) at 20 years.

CONCLUSION

The NCI tool had modest discriminatory function for estimating the presence of current advanced neoplasia in veterans undergoing a first screening colonoscopy. These findings are comparable to other clinically utilized cancer risk prediction models and may be used to inform the benefit-risk assessment of screening, particularly for patients with competing comorbidities and lower risk, for whom a non-invasive screening approach is preferred.