Models Based on Dynamic Clinicopathological Indices for Predicting Prognosis During the Perioperative Period for Patients with Colorectal Cancer

Prognostic prediction model of colorectal cancer based on preoperative serum tumor markers

BACKGROUND

Preoperative serum tumor markers not only play a role in the auxiliary diagnosis and postoperative monitoring in colorectal cancer (CRC), but also have been found to have potential prognostic value.

AIM

To analyze whether preoperative serum tumor markers, including carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), affect the prognosis of CRC.

METHODS

This was a retrospective study conducted in a single center. Patients with nonmetastatic CRC who underwent initial surgery between January 2011 and January 2020 were enrolled and divided into development site and validation site groups at a ratio of 7:3. The independent prognostic factors were screened by Cox regression analysis, and finally, a prognostic nomogram model was established. The newly developed model was tested by internal validation.

RESULTS

Eventually, 3526 postoperative patients with nonmetastatic CRC were included in the study. There were 2473 patients at the development site and 1056 patients at the validation site. Age (P < 0.01, HR = 1.042, 95%CI = 1.033-1.051), tumor node metastasis (TNM) classification (P < 0.01, HR = 1.938, 95%CI = 1.665-2.255), preoperative CEA (P = 0.001, HR = 1.393, 95%CI = 1.137-1.707) and CA19-9 (P < 0.01, HR = 1.948, 95%CI = 1.614-2.438) levels were considered independent prognostic factors for patients with nonmetastatic CRC and were used as variables in the nomogram model. The areas under the curve of the development and validation sites were 0.655 and 0.658, respectively. The calibration plot also showed the significant performance of the newly established nomogram.

CONCLUSION

We successfully constructed a nomogram model based on age, TNM stage, preoperative CEA, and CA19-9 levels to evaluate the overall survival of patients with nonmetastatic CRC.

Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study

BACKGROUND

Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction.

METHODS

The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements.

RESULTS

In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery.

CONCLUSIONS

Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Objective

The purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients.

Methods

A total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve.

Results

ROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models.

Conclusion

HALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.

Predictive Nomogram for the Prediction of Early Recurrence of Colorectal Cancer

Aim

The prognosis of colorectal cancer (CRC) individuals after curative resection is not satisfactory due to the early recurrence. We sought to identify the affecting features of early recurrence in CRC patients.

Methods

A total of 3500 CRC patients underwent curative resection were retrospectively incorporated into our study. Among them, 246 patients exhibited tumor recurrence: 121 had early recurrence (≤1 year after operation) and 125 had late recurrence (>1 year after operation). A total of 246 CRC patients with recurrence were randomly assigned into the training group (N=177) or validation group (N=69) based on the ratio of 7:3. LASSO COX regression and support vector machine (SVM) were utilized to screen for the significant clinical indexes associated with the presence of early recurrence. Recurrent nomogram was created based on the above informative parameters to predict the probability of early recurrence.

Results

Proportion of advanced TNM stage, platelet count, systemic immune-inflammation index (SII), mean corpuscular hemoglobin concentration (MCHC), CA-199, CA-125, lactate dehydrogenase, total bile acid (TBA), urea nitrogen were significantly higher in early recurrence group compared with that in late recurrence group. Results from LASSO COX regression and support vector machine (SVM) revealed that TNM stage, CA-199, CA125, SII and TBA were strong predictors for the presence of early recurrence among postoperative CRC patients in the training group. The recurrent nomogram based on the five predictors exhibited good predictive performance as calculated by C-index (0.846, 95% CI 0.789-0.902 in the training group and 0.799, 95% CI 0.697-0.902 in the validation group) for the prediction of early recurrence. Moreover, the recurrent nomogram exhibited not only encouraging calibration ability, but also great clinical utility both in the training group and validation group.

Conclusion

TNM stage, CA-199, CA125, SII and TBA were closely correlated with the presence of early recurrence of CRC patients. The recurrent nomogram held well predictive ability for the identification of CRC patients with early recurrence.