An enrichment model using regular health examination data for early detection of colorectal cancer

Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review

The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.

Recent developments in antibody derivatives against colorectal cancer; A review

Colorectal cancer (CRC) is the fourth most common cause of cancer and mortality worldwide and is the third most common cancer in men and women. Surgery, radiotherapy, and chemotherapy are conventionally used for the treatment of colorectal cancer. However, these methods are associated with various side effects on normal cells. Thus, new studies are moving towards more effective and non-invasive methods for treatment of colorectal cancer. Targeted therapy of CRC is a promising new approach to enhance the efficiency and decrease the toxicity of the treatment. In targeted therapy of CRC, antibody fragments can directly inhibit tumor cell growth and proliferation. They also can act as an ideal carrier for targeted delivery of anticancer drugs. In the present study, the structure and function of different formats of antibody fragments, immune-targeted therapy of CRC using antibody fragments will be discussed.

Development and validation of two aspiration prediction models in patients receiving nasogastric feeding

AIM

To develop and validate two aspiration prediction models in patients receiving nasogastric feeding.

BACKGROUND

Aspiration is one of the most serious complications of nasogastric feeding. However, there is a lack of aspiration prediction models for nasogastric feeding.

METHODS

A total of 515 patients receiving nasogastric feeding were randomly selected for this unmatched case-control study, with 103 patients in the case group and 412 patients in the control group. Logistic regression was used to develop nomogram and Classification And Regression Tree (CART) models. The performances of the models were internally validated using 1,000 bootstrapped samples.

RESULTS

The predictive accuracy of the CART model (94.5%) was higher than that of the nomogram model (89.1%). The area under the receiver operating characteristic curve of the CART model (0.96) was slightly higher than that of the nomogram model (0.93).

CONCLUSIONS

The intubation depth, number of comorbidities, aspiration history, indwelling days, food type and the use of sedative-hypnotics may be used to identify aspiration risk.

IMPLICATIONS FOR NURSING MANAGEMENT

Two aspiration prediction models are provided for nurses to evaluate aspiration risk and increase the quality of nursing management.

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence

Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.