Development and Validation of a Nomogram to Predict Survival in Pancreatic Head Ductal Adenocarcinoma After Pancreaticoduodenectomy

Development and validation of a network calculator model for safety and efficacy after pancreaticoduodenectomy in the elderly patients with pancreatic head cancer

BACKGROUND

Benefiting from increased life expectancy and improved perioperative management, more elderly patients with pancreatic head cancer (PHC) underwent pancreaticoduodenectomy (PD). However, individualized predictive models for the safety and efficacy of PD is still lacking. this study aimed to developed three safety- and efficacy-related risk calculators for elderly (> = 65 years) PHC patients.

METHODS

This study was designed with two research cohorts, namely, the training cohort and the validation cohort, and comprises four general steps: (1) Risk factors were analyzed for the incidence of postoperative complications, cancer-specific survival (CSS), and overall survival (OS) in the training cohort (N = 271) using logistic and Cox-regression analysis. (2) Nomograms were then plotted based on the above results. (3) The accuracy of the developed nomogram models was then verified with the validation cohort (N = 134) data using consistency index (C-index) and calibration curves. (4) We then evaluated the efficacy of these nomograms using decision curve analysis (DCA) in both the training and validation cohorts, and ultimately constructed three online calculators based on these nomograms.

RESULTS

We identified ASA, diabetes, smoking, and lymph node invasion as predisposing risk factors for postoperative complications, and the predictive factors that affected both OS and CSS were ASA, diabetes, BMI, CA19-9 level, and tumor diameter. By integrating the above risk factors, we constructed three nomograms on postoperative complication, CSS, and OS. The C-index for complication, CSS, and OS were 0.824, 0.784, and 0.801 in the training cohort and 0.746, 0.718, and 0.708 in the validation cohort. Moreover, the validation curves and DCA demonstrated good calibration and robust compliance in both training and validation cohorts. We then developed three web calculators (https://caiming.shinyapps.io/CMCD/, https://caiming.shinyapps.io/CMCSS/, and https://caiming.shinyapps.io/CMOS/) to facilitate the use of the nomograms.

CONCLUSIONS

The calculators demonstrated promising performance as an tool for predicting the safety and efficacy of PD in elderly PHC patients.

A new staging system for postoperative prognostication in pancreatic ductal adenocarcinoma

The current TNM staging system for pancreatic ductal adenocarcinoma (PDAC) has revised the definitions of T and N categories as well as stage groups. However, studies validating these modifications have yielded inconsistent results. The existing TNM staging system in prognostic prediction remains unsatisfactory. The prognosis of PDAC is closely associated with pathological and biological factors. Herein, we propose a new staging system incorporating distant metastasis, postoperative serum levels of CA19-9 and CEA, tumor size, lymph node metastasis, lymphovascular involvement, and perineural invasion to enhance the accuracy of prognosis assessment. The proposed staging system exhibited a strong correlation with both overall survival and recurrence-free survival, effectively stratifying survival into five distinct tiers. Additionally, it had favorable discrimination and calibration. Thus, the proposed staging system demonstrates superior prognostic performance compared to the TNM staging system, and can serve as a valuable complementary tool to address the limitations of TNM staging in prognostication.

A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high- or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https://rockeric.shinyapps.io/DynNomapp/ may contribute to treatment optimization in clinical practice.

Development and validation of a nomogram to predict liver metastasis for pancreatic ductal adenocarcinoma after radical resection

OBJECTIVES

This study aimed to develop and externally validate a nomogram for predicting liver metastasis after radical resection in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 247 PDAC patients who underwent radical resection were retrospectively reviewed from January 2015 to March 2022 at Ningbo Medical Centre Lihuili Hospital Eastern Section, and used as a training cohort to develop the nomogram. 83 PDAC patients from the Ningbo Medical Centre Lihuili Hospital Xingning Section were enrolled as the validation cohort. The postoperative liver metastasis was recorded during the follow-up, and the liver metastasis-free survival was defined as the time from operation to the date of liver metastasis diagnosis or death. The nomogram was established based on independent prognostic factors selected by LASSO and multivariate Cox regression model. The performance was assessed using the concordance index (C-index) and calibration curves. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to determine the clinical utility of the nomogram model.

RESULTS

From the training cohort of 247 patients, a total of 132 patients developed liver metastasis during the follow-up, the 1-, 2- and 3- year liver metastasis-free survival were 52.4%, 43.5% and 40% respectively. The LASSO and multivariate Cox regression analysis indicated that postoperative CA125 (hazard ratio [HR] = 1.007, p <0.001), tumor differentiation (HR = 1.640, p = 0.010), tumor size (HR = 1.520, p = 0.029), lymph node ratio (HR = 1.897, p = 0.002) and portal/superior mesenteric/splenic vein invasion degree (PV/SMV/SV) (HR = 2.829, p <0.001) were the independent factors of liver metastasis. A nomogram with independent factors was developed and the C-index was 0.760 (95% confidence interval [CI], 0.720-0.799) and 0.739 (95% CI, 0.669-0.810) in the training and validation cohorts, respectively. The areas under curve (AUC) of the nomogram at 1-, 2- and 3-year were 0.815, 0.803 and 0.773 in the training cohort, and 0.765, 0.879 and 0.908 in the validation cohort, respectively, higher than those in TNM stage. Decision curve analysis (DCA) analysis revealed that the nomogram model provided superior net benefit in clinical utility. Liver metastasis-free survival curves showed a significant discriminatory ability for liver metastasis risk based on the nomogram (p <0.001).

CONCLUSIONS

The nomogram showed high accuracy in predicting liver metastasis for PDAC after radical resection, and may serve as a clinical support tool to guide personalized and prescient intervention.

Development and validation of a nomogram for predicting post-operative abdominal infection in patients undergoing pancreaticoduodenectomy

AIM

The aim of this retrospective study is to develop and validate a predictive nomogram for predicting the risk of post-operative abdominal infection (PAI) in patients undergoing pancreaticoduodenectomy (PD).

METHODS

A total of 360 patients who underwent PD were enrolled into this research and randomly divided into the development and validation group. The clinical data of patients were statistically compared and the nomogram was constructed based on the results of multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally and crossly validated by the development and validation cohort. The discriminatory ability of the nomogram was estimated by AUC (Area Under the receiver operating characteristic Curve), calibration curve and decision curve analysis.

RESULTS

After PD, post-operative abdominal infection occurred in 33.89% (n = 122) of patients. The nomogram showed that preoperative biliary drainage and C-reactive protein (CRP), direct bilirubin (DB), alkaline phosphatase (AKP) levels on the 3rd postoperative day (POD3) were independent prognostic factors for abdominal infection after PD. The internal and cross validation of Receiver Operating Characteristic (ROC) curve was statistically significant (AUC = 0.723 and 0.786, respectively). The calibration curves showed good agreement between nomogram predictions and actual observations. The decision curves showed that the nomogram was of great clinical value.

CONCLUSION

A nomogram based on perioperative risk factors such as preoperative biliary drainage, CRP, DB and AKP could simply and accurately predict the risk degree of PAI in patients undergoing PD.

Prognostic signature based on m6A-related lncRNAs to predict overall survival in pancreatic ductal adenocarcinoma

A growing body of evidence indicates that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play crucial roles in the progression of PDAC and the treatment response of patients with pancreatic ductal adenocarcinoma (PDAC). In this study, we identified m6A-related lncRNAs to reveal their association with PDAC in prognosis and tumor immune environment. A prognostic signature based on 9 m6A-related lncRNAs was established, and the high-risk patients exhibited a significantly worse prognosis than low-risk patients. The predictive capacity was confirmed by receiver operating characteristic (ROC) curve analysis and an independent validation cohort. Correlation analyses revealed that m6A-related lncRNA signature was significantly associated with the number of somatic mutations, immunocyte infiltration, immune function, immune checkpoints, tumor microenvironment (TME) score, and sensitivity to chemotherapeutic drugs. Consequently, we constructed a highly accurate nomogram for improving clinical applicability of signature and exhibited superior predictive accuracy than both the signature and tumor stage. In conclusion, our proposed m6A-related lncRNA signature is a potential indicator predictive of prognosis and immunotherapeutic responses in PDAC patients.