Demographic and clinical data of 2070 patients were collected from the medical records at the Dongyang People’s Hospital between November 2012 and June 2022. The inclusion criteria were adult patients (≥ 18 years) who were diagnosed with CRC or CA using histopathology. The exclusion criteria were (1): preoperative anti-tumor treatment (2), patients diagnosed with advanced CRC (Tumor-Node-Metastasis [TNM] stage III-IV) according to TNM staging of the eighth edition of the American Joint Committee on Cancer (3), patients with other primary malignant tumors, and (4) patients with clinical data loss rate >10%. According to the inclusion and exclusion criteria, 500 patients with LCC and 980 patients with CA were finally included in this study ( Figure 1 ).

The study was approved by the ethics committee of Dongyang People’s Hospital. The data analysis was anonymous. Informed consent was waived since this is a retrospective study and the diagnosis as well as treatment of the patients were not affected.

Preoperative data of eligible patients were extracted, including age, sex, routine blood parameters, FOBT, serum albumin, serum glucose, and tumor biomarkers. Routine blood parameters included white blood cell (WBC), neutrophil, and lymphocyte counts, as well as hemoglobin, RDW, platelet count, and mean platelet volume. Selected tumor biomarkers were CEA and carbohydrate antigen 199. FOBT-positive patients were defined as those with a positive immunochemical test and guaiac-based test ≥ 1+. When the proportion of missing values in the variables was less than 10%, the missing data were filled using multiple imputations in the two cohorts respectively (20). For outliers, which were defined as values other than 1-99% in continuous variables winsorized by 1% on both sides (21), artificial discrimination was used in categorical variables. In addition, data on smoking, drinking, diabetes, and hypertension statuses of the eligible patients were collected. A history of smoking or drinking was noted if smoking or the use of alcohol was reported in a patient’s medical record.

Included patients were randomly divided into the training (70%) and validation (30%) cohorts. Data were analyzed using Stata version 14.0 (Stata Corp LP, College Station, TX, USA) and R software version 4.1.0. Continuous variables are expressed as means and standard deviations or medians and interquartile intervals as appropriate. A Wilcox test or Student’s t-test was used to assess between-group differences. Categorical variables were compared using the chi-square test and are presented as quantities (percentages).

Data from the training cohort were used for the development of the prediction model. Univariable logistic regression analysis was used preliminarily to screen candidate variables. The least absolute shrinkage and selection operator (LASSO) regression analysis was then used to remove collinear independent variables to prevent overfitting (22). Subsequently, indicators with coefficients that were not zero were included in a multivariable logistic regression to complete the final selection of variables. Variables with statistical significance (P<0.05) were included in the prediction model. The prediction probability of the model was calculated using the following formula:

A web-based prediction model was developed based on the “DynNom” and “rsconnect” packages in R software (23). The prediction probability can be automatically calculated after inputting the expression of each variable.

The performance of the model in the training and validation cohort was assessed. After data were normalized, the discriminative ability was expressed using the area under the receiver operating characteristic (ROC) curve (AUC). The Youden index was used to determine the best cut-off value, and the corresponding sensitivity, specificity, accuracy, positive prediction value (PPV), negative prediction value (NPV), positive likelihood ratio (PLR), as well as negative effect ratio (NLR), were calculated. To examine the consistency between the actual risk of LCC and the probability predicted using the new model, calibration curves were plotted and the Hosmer–Lemeshow goodness of fit test was performed. The clinical utility of the decision curve analysis (DCA) and clinical impact curve (CIC) was used to demonstrate the clinical utility of the prediction model (24).

Demographic and clinical data were extracted from medical records of 2070 patients between November 2012 and June 2022. After data were excluded based on our criteria, records of 1480 patients (LCC: n=500 and CA: n=980) were finally included in this study ( Figure 1 ). Among LCC patients, five (1.0%), 161 (32.2%), and 334 (66.8%) patients had TNM stage 0, I, and II disease, respectively. The patients were then randomly divided into the training (n=1036) and validation (n=444) cohorts at a ratio of 7:3. There was no statistical difference between the training and validation cohorts in each variable ( Table 1 ).

As shown in Table 2 , in the training cohort, there were significant differences in 16 clinical parameters between LCC and CA groups using univariable logistic regression. Subsequently, 15 potential predictors without multicollinearity were identified using LASSO regression analysis ( Table 2 , Supplementary Figure 1 ). Finally, age, sex, drinking history, WBC, lymphocyte count, RDW, albumin, CEA, and FOBT were considered independent predictors of LCC in patients according to the results of multivariate logistic regression ( Table 3 ). The probability of LCC can be calculated according to the following formula: Probability (LCC) = 1/(1 + exp (− (-1.703 + 0.024 × age -1.035 × sex (male=1, female=0) + 0.482 × drinking history (yes=1, no=0) + 0.215 × WBC - 0.694 × lymphocyte count + 0.334 × RDW - 0.131 × albumin + 0.203 × CEA + 2.357 × FOBT (positive=1, negative=0)))).

The web-based dynamic prediction model developed using the selected variables can be used through the following link: https://ly11219.shinyapps.io/dynnomapp/.The interface of this webpage is shown in Figure 2 . Figure 2A displays the input nomograph interface, in which users can adjust the expression of each item. Figure 2B shows a graphical summary of the LCC probability and 95% confidence interval (CI) predicted for the three patients according to the nomogram. The page also provides a numerical summary, shown in Figure 2C .

The AUC in the training and validation cohorts was 0.910 (0.892–0.929) and 0.894 (0.862–0.925), respectively ( Figure 3A ). There was no significant difference in the diagnostic performance of the prediction model between the two cohorts (P=0.379). The optimal cut-off value of the probatility of the prediction model was 26.41%. The result of sensitivity, specificity, PPV, NPV, PLR, and NLR used to distinguish between LCC and CA was 86.4%, 80.1%, 82.2%, 69.1%, 91.9%, 4.342, and 0.170 in the training cohort, as well as 85.1%, 79.1%, 81.1%, 67.0%, 91.4%, 4.07 and 0.188 in the validation cohort, respectively ( Table 4 ). The results of the Hosmer–Lemeshow goodness of fit test showed good consistency between the outcome predicted by the model and the actual diagnosis, which was reflected in both the training and validation cohorts ( Figures 3B, C ).

When drawing the DCA to reflect the advantages of the new model, we added a comparison between the new model and the two common CRC screening indicators, CEA and FOBT. In the training and validation cohorts, the threshold probability was between 0.05 and 1.00. The performance of the prediction model was better than that of the CEA, FOBT, and two extreme cases (treat-none and treat-all) as shown in Figures 4A, B . For example, when the risk threshold was set to 0.3 (i.e. if the LCC probability of the patient was >30%), the patient would receive further treatment. In the training cohort, the net benefit of the new prediction model was 0.25, which was higher than that of of the FOBT (0.20), CEA (0.10), treat-all (0.05), and treat-none (0.00).

CIC results also showed a similar number of high-risk LCC patients predicted by the new model and the actual number of LCC patients in both the training and validation cohorts ( Figures 4C, D ). For instance, when the risk threshold was set to 0.4, almost 400 out of every 1000 persons in the training cohort were deemed at high risk, and approximately 350 of them were diagnosed with LCC.