In contrast to previous prediction models that incorporate only baseline f-Hb concentration and personal characteristics in the model, the proposed risk prediction model for CRC in this thesis is specific and considers the FIT results with time-varying characteristics. By combining information on both of baseline and updated f-Hb concentrations from population-based biennial FIT screening with time-dependent analysis that provides a feasible way for identifying the effect of f-Hb on the risk of CRC at pre-clinical and clinical phases. The advent of population-based screening with the FIT provides an opportunity to fully assess the usage of an incremental category of not only baseline but also subsequent measurements of f-Hb concentration on the risk of colorectal neoplasia using a longitudinal follow-up on those who
potentially at risk to develop the colorectal neoplasia.
The role of baseline quantitative f-Hb concentration on the prediction of colorectal neoplasia.
The present study firstly demonstrated that a dose-response relationship between baseline f-Hb and the risk of developing colorectal neoplasia. We have shown that a quantitative baseline f-Hb concentration is an independent predictive factor of colorectal neoplasia risk, including both invasive colorectal cancer and
adenoma. The AUC was 68.1% for baseline f-Hb using time-independent model. The incremental f-Hb concentration in association with the risk for colorectal cancer is consistent with the findings in the previous literatures (L.-S. Chen et al., 2011) (Yen et al., 2014) The possible causal effect of an incremental increase in fecal hemoglobin on advanced CRC incidence was also assessed by our study. The accuracy of predicting incident advanced CRC was assessed by using time-invariant receive operating characteristics curve analysis. The 69.8% of AUC implies that quantitative f-Hb alone is the major factor for predicting risk of advanced colorectal cancer.
The odds ratios for colorectal cancer increased with f-Hb concentration from 6-9 ug/g to 450 ug/g or above, dropped a bit for those with 100-149 ug/g, and then rebounded for those with 150-249 ug/g or above. The drop of odds ratios for
colorectal cancer could be possibly compensated by the early detection of adenoma.
However, it is interesting to see that the hazard ratios for colorectal cancer increased with f-Hb concentration from 6-9 ug/g to 450 ug/g or above by Cox proportional hazards regression model once taking follow-up time into account. Additionally, the odds ratios increased with f-Hb concentrations and are generally lower for each category of f-Hb concentration in advanced colorectal cancer compared with colorectal cancer.
Dynamic change of f-HB concentration for prediction of colorectal cancer
When the prevalent CRC cases were excluded from analysis, we found that the effect sizes of each category of f-Hb concentration on the incident CRCs after follow-up on those who test negative at prevalent screen or with a negative finding on colonoscopy were smaller than combined prevalent and incident cases. The predictive ability should be different for baseline and subsequent f-Hb concentration. A time-dependent analysis was conducted to include the repeated measurements of f-Hb concentration taken during the follow-up period at repeated screening. The findings were similar to those already described, but with larger HRs for f-Hb higher than 20-49 ug/g or above.
It should be noted that our dynamic logistic regression model with Markov process revealed the role of f-Hb not only in the initiation of PCDP but also in disease progress from PCDP to CP. A dose-response manner of f-Hb concentration on each process was also found. Moreover, the high predictive ability of this model was demonstrated by 88% of AUC in ROC analysis. These findings have significant implications for the use of f-Hb concentrations to distinguish the different risk groups according to the health status which provides opportunities for individually tailored screening strategy including the age-and sex- specific inter-screening interval by using the full information on f-Hb concentration measurements. The higher the risk
predicted by the proposed model, the shorter inter-screening interval, and the more advanced detection methods should be considered. Such a prediction model might be useful for alerting someone even having lower f-Hb but with elevated f-Hb at
repeated screen or improving patients’ awareness for attending the repeated screen and thereby detecting CRC earlier. An application is like that, the individual time-dependent risk score can be generated from both of first screening and subsequent screenings by the proposed risk prediction model which can then be applied in an individual-risk-guided invitation to a large population-based screening program.
Strengths and Limitations
The strength of our risk prediction model is that it was developed by using large population-based screening data. Large population-based study has also gained sufficient statistical power for building a prediction model for CRC. There are two limitations in the current thesis. The developed prediction models did not validate by other datasets as one of our limitation. The cross-validation and external validation are further required. The predictive abilities of FIT by time-dependent Cox
proportional hazard regression model and dynamic logistic regression model with Markov process are limited to the colorectal cancer. The study on the performance of FIT for colorectal neoplasia should be carried out in the future.
In conclusion, the results of this large population-based prospective cohort study demonstrated not only a dose-response relationship between f-Hb and incident CRC but also the dose-response manner in f-Hb concentration which plays the role on both of initiator and promoter of CRC by the novel dynamic logistic regression
model. These findings have significant implications for the better use of f-Hb concentrations on the individual-tailor CRC screening strategy.
Table 1 Number of participants, colorectal cancer (CRC) case, adenoma (non-advanced and advanced) cases in a CRC screening program
Characteristics Participants Adenoma % Advanced
Adenoma % Colorectal
Table 2 The demographic characteristics (site and stage) in Colorectal cancer cases
Table 3 Numbers of repeated visit and follow-up time of nationwide colorectal cancer screening
from 2004 to 2014
Free of CRC CRC
N / mean (% / std) N / mean (% / std)
Number of visit 1 215629 97.7% 5075 2.3%
2 247310 98.9% 2791 1.1%
3 259876 99.5% 1282 0.5%
4 138341 99.7% 455 0.3%
5 39300 99.7% 112 0.3%
6 8949 99.9% 13 0.1%
7 1734 99.8% 3 0.2%
8 76 100.0% 0 0.0%
Follow up time 7.76 1.54 4.13 2.79
Table 4 Crude ORs and adjusted ORs (aOR) for Adenoma
Adenoma
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 5 Crude ORs and adjusted ORs (aOR) for Colorectal cancer
Colorectal cancer
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 6 Crude ORs and adjusted ORs (aOR) for Neoplasia
Neoplasia
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 7 Crude ORs and adjusted ORs (aOR) for Advanced Adenoma
Advanced Adenoma
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 8 Crude ORs and adjusted ORs (aOR) for Advanced Neoplasia (AA + CRC)
Advanced Neoplasia
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 9 Crude ORs and adjusted ORs (aOR) for Advanced CRC (above stage2)
Advanced CRC
Univariate analysis Multivariate analysis
Variables OR 95% CI aOR 95% CI
Table 10 AUC of univariate and multivariate logistic regression
Adenoma Colorectal cancer Neoplasia Advanced Adenoma Advanced Neoplasia Advanced colorectal cancer
AUC 95% CI AUC 95% CI AUC 95% CI AUC 95% CI AUC 95% CI AUC 95% CI
Age 0.5497 0.5469 0.5525 0.6015 0.5961 0.6070 0.5522 0.5497 0.5548 0.5481 0.5424 0.5538 0.5692 0.5653 0.5731 0.6098 0.6021 0.6176 Gender 0.5755 0.5729 0.5780 0.5552 0.5502 0.5602 0.5726 0.5703 0.5749 0.5955 0.5904 0.6006 0.5754 0.5718 0.5790 0.5484 0.5413 0.5556 f-Hb 0.6773 0.6742 0.6803 0.6998 0.6940 0.7056 0.6851 0.6824 0.6878 0.7330 0.7269 0.7390 0.7180 0.7138 0.7222 0.6968 0.6885 0.7050 Combined 0.7173 0.7144 0.7202 0.7355 0.7299 0.7410 0.7180 0.7154 0.7207 0.7652 0.7595 0.7709 0.7449 0.7408 0.7490 0.7359 0.7280 0.7438
Table 11 Estimated effect of baseline f-Hb on colorectal cancer incidence using Cox proportional hazards regression model
Univariate analysis Multivariate analysis
Variables HR 95% CI aHR 95% CI
Table 12 Estimated effect of baseline f-Hb on colorectal cancer incidence using Cox proportional hazards regression model (exclude prevalent CRC case)
Univariate Multivariate
Table 13 Crude HRs and adjusted HRs (aHR) for Advanced Colorectal cancer
Advanced CRC
Univariate analysis Multivariate analysis
Variables HR 95% CI aHR 95% CI
Table 14 Estimated effect of f-Hb as time-varying covariate on colorectal cancer incidence using Poisson regression model
Time-dependent Time-dependent
Table 15 Estimated results on the risk of CRC evolution based on three-state Markov model with compound rate for interval cancer
Normal to PCDP PCDP to CP
Regression coefficient aHR Regression coefficient aHR
Estimate 95 CI Estimate 95 CI Estimate 95 CI Estimate 95 CI
Figure 1 Work flow of the Taiwanese nationwide colorectal cancer screening program.
FIT indicates fecal immunochemical testing
Figure 2 Receiver operating characteristic (ROC) curve of FIT for Adenoma
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7173 Age AUC = 0.5497
Gender AUC = 0.5755 fHbC AUC = 0.6773
Figure 3 Receiver operating characteristic (ROC) curve of FIT for colorectal cancer
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7355 Age AUC = 0.6015
Gender AUC = 0.5552 fHbC AUC = 0.6998
Figure 4 Receiver operating characteristic (ROC) curve of FIT for neoplasia
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7180 Age AUC = 0.5522
Gender AUC = 0.5720 fHbC AUC = 0.6851
Figure 5 Receiver operating characteristic (ROC) curve of FIT for advanced adenoma
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7652 Age AUC = 0.5481
Gender AUC = 0.5955 fHbC AUC = 0.7330
Figure 6 Receiver operating characteristic (ROC) curve of FIT for advanced Neoplasia
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7449 Age AUC = 0.5692
Gender AUC = 0.5754 fHbC AUC = 0.7180
Figure 7 Receiver operating characteristic (ROC) curve of FIT for advanced colorectal cancer
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Sensitivity
1-Specificity
Age+ Gender + fHBC AUC=0.7355 Age AUC = 0.6096
Gender AUC = 0.5484 fHbC AUC = 0.6964
Figure 8 Receiver operating characteristic (ROC) curve of predicting colorectal cancer incidence by age, gender, and time-varying f-Hb in 2 years, 4 years, and 6 years
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Sensitivity
1-Spec
2 year AUC: 91.2 (95%CI: 91.0-91.3)
4 year AUC: 87.9 (95%CI:82.3-93.4)
6 year AUC: 83.8 (95%CI: 79.4-88.4)
Figure 9 Receiver operating characteristic curve (ROC) of FIT considering age and gender for CRC with Markov process of dynamic logistic regression approach
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Sensitivity
1-Specificity
AUC:88.0%(87.3%-88.6%)
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