27
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Developing a Nomogram for Predicting Colorectal Cancer and Its Precancerous Lesions Based on Data from Three Non-Invasive Screening Tools, APCS, FIT, and sDNA

ORCID Icon, , &
Pages 2891-2901 | Received 15 Apr 2024, Accepted 29 May 2024, Published online: 13 Jun 2024

Figures & data

Table 1 The Demographic Data for the Enrolled Participants (n = 179)

Figure 1 Flowchart of the participants.

Abbreviations: CRC, colorectal cancer; AA, advanced adenoma; HGIN, high-grade intraepithelial neoplasia; LGIN, low-grade intraepithelial neoplasia.
Figure 1 Flowchart of the participants.

Table 2 Univariate and Multivariate Analysis of Predictors for Positive Colonoscopy Results in the Training Cohort

Figure 2 The nomograms for predicting positive colonoscopy results.

Abbreviations: APCS, Asia-Pacific colorectal screening; FIT, fecal immunochemical testing; sDNA, stool deoxyribonucleic acid.
Figure 2 The nomograms for predicting positive colonoscopy results.

Figure 3 ROC curves of the established model in the training set (A) and validation set (B).

Figure 3 ROC curves of the established model in the training set (A) and validation set (B).

Figure 4 Calibration curve of the model in the validation set.

Figure 4 Calibration curve of the model in the validation set.

Figure 5 Decision curve analysis (DCA) for the predictive model. The net benefit was produced against the high-risk threshold.

Figure 5 Decision curve analysis (DCA) for the predictive model. The net benefit was produced against the high-risk threshold.

Figure 6 Comparison of ROC curves of the established model and FIT + sDNA.

Abbreviations: ROC, receiver operating characteristic; FIT, fecal immunochemical testing; sDNA, stool deoxyribonucleic acid.
Figure 6 Comparison of ROC curves of the established model and FIT + sDNA.