Risk-stratified selection to colonoscopy in FIT colorectal cancer screening: development and temporal validation of a prediction model

The value of hypersensitivity quantitative fecal immunochemical test in early colorectal cancer detection

At present, both the incidence and mortality rates of colorectal cancer are on the rise, making early screening a crucial tool in reducing the fatality rate. Although colonoscopy is the recommended method according to the guidelines, compliance tends to be poor. The fecal immunochemical test (FIT), a new technology that uses latex immunoturbidimetry to detect fecal blood, offers high specificity and sensitivity. Additionally, it is low-cost, easy to operate, and less likely to be affected by food and drugs, thus improving the compliance rate for population screening. Compared to other screening techniques, FIT represents a safer and more accurate option. This article reviews the application of FIT in early colorectal cancer screening.

Improved use of faecal immunochemical tests for haemoglobin in the Scottish bowel screening programme

OBJECTIVES

This study aimed to develop a risk-scoring model in the Scottish Bowel Screening Programme incorporating faecal haemoglobin concentration with other risk factors for colorectal cancer.

METHODS

Data were collected for all individuals invited to participate in the Scottish Bowel Screening Programme between November 2017 and March 2018 including faecal haemoglobin concentration, age, sex, National Health Service Board, socioeconomic status, and screening history. Linkage with The Scottish Cancer Registry identified all screening participants diagnosed with colorectal cancer. Logistic regression was performed to identify which factors demonstrated significant association with colorectal cancer and could be used in the development of a risk-scoring model.

RESULTS

Of 232,076 screening participants, 427 had colorectal cancer: 286 diagnosed following a screening colonoscopy and 141 arising after a negative screening test result giving an interval cancer proportion of 33.0%. Only faecal haemoglobin concentration and age showed a statistically significant association with colorectal cancer. Interval cancer proportion increased with age and was higher in women (38.1%) than men (27.5%). If positivity in women were mirrored in men at each age quintile interval cancer proportion would still have remained higher in women (33.2%). Moreover, an additional 1201 colonoscopies would be required to detect 11 colorectal cancers.

CONCLUSIONS

Development of a risk scoring model using early data from the Scottish Bowel Screening Programme was not feasible due to most variables showing insignificant association with colorectal cancer. Tailoring the faecal haemoglobin concentration threshold according to age could help to diminish some of the disparity in interval cancer proportion between women and men. Strategies to achieve sex equality using faecal haemoglobin concentration thresholds depend considerably on which variable is selected for equivalency and this requires further exploration.

Development and validation of colorectal cancer risk prediction tools: A comparison of models

BACKGROUND

Identification of individuals at elevated risk can improve cancer screening programmes by permitting risk-adjusted screening intensities. Previous work introduced a prognostic model using sex, age and two preceding faecal haemoglobin concentrations to predict the risk of colorectal cancer (CRC) in the next screening round. Using data of 3 screening rounds, this model attained an area under the receiver-operating-characteristic curve (AUC) of 0.78 for predicting advanced neoplasia (AN). We validated this existing logistic regression (LR) model and attempted to improve it by applying a more flexible machine-learning approach.

METHODS

We trained an existing LR and a newly developed random forest (RF) model using updated data from 219,257 third-round participants of the Dutch CRC screening programme until 2018. For both models, we performed two separate out-of-sample validations using 1,137,599 third-round participants after 2018 and 192,793 fourth-round participants from 2020 onwards. We evaluated the AUC and relative risks of the predicted high-risk groups for the outcomes AN and CRC.

RESULTS

For third-round participants after 2018, the AUC for predicting AN was 0.77 (95% CI: 0.76-0.77) using LR and 0.77 (95% CI: 0.77-0.77) using RF. For fourth-round participants, the AUCs were 0.73 (95% CI: 0.72-0.74) and 0.73 (95% CI: 0.72-0.74) for the LR and RF models, respectively. For both models, the 5% with the highest predicted risk had a 7-fold risk of AN compared to average, whereas the lowest 80% had a risk below the population average for third-round participants.

CONCLUSION

The LR is a valid risk prediction method in stool-based screening programmes. Although predictive performance declined marginally, the LR model still effectively predicted risk in subsequent screening rounds. An RF did not improve CRC risk prediction compared to an LR, probably due to the limited number of available explanatory variables. The LR remains the preferred prediction tool because of its interpretability.

Risk Scoring Systems for Predicting the Presence of Colorectal Neoplasia by Fecal Immunochemical Test Results in Chinese Population

INTRODUCTION

Adherence to colonoscopy screening for colorectal cancer (CRC) is low in general populations, including those tested positive in the fecal immunochemical test (FIT). Developing tailored risk scoring systems by FIT results may allow for more accurate identification of individuals for colonoscopy.

METHODS

Among 807,109 participants who completed the primary tests in the first-round Shanghai CRC screening program, 71,023 attended recommended colonoscopy. Predictors for colorectal neoplasia were used to develop respective scoring systems for FIT-positive or FIT-negative populations using logistic regression and artificial neural network methods.

RESULTS

Age, sex, area of residence, history of mucus or bloody stool, and CRC in first-degree relatives were identified as predictors for CRC in FIT-positive subjects, while a history of chronic diarrhea and prior cancer were additionally included for FIT-negative subjects. With an area under the receiver operating characteristic curve of more than 0.800 in predicting CRC, the logistic regression-based systems outperformed the artificial neural network-based ones and had a sensitivity of 68.9%, a specificity of 82.6%, and a detection rate of 0.24% by identifying 17.6% subjects at high risk. We also reported an area under the receiver operating characteristic curve of about 0.660 for the systems predicting CRC and adenoma, with a sensitivity of 57.8%, a specificity of 64.6%, and a detection rate of 6.87% through classifying 38.1% subjects as high-risk individuals. The performance of the scoring systems for CRC was superior to the currently used method in Mainland, China, and comparable with the scoring systems incorporating the FIT results.

DISCUSSION

The tailored risk scoring systems may better identify high-risk individuals of colorectal neoplasia and facilitate colonoscopy follow-up. External validation is warranted for widespread use of the scoring systems.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.