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BACKGROUND: Several variables have been described as prognostic factors for resected distal cholangiocarcinoma (dCC), including lymph node metastases (N status) and lymph node ratio (LNR). The present study aimed to evaluate the prognostic value of LNR on survival and to establish a novel prognostic nomogram to predict the cancer-specific recurrence-free survival (RFS) of dCC. STUDY DESIGN: Between December 2006 and September 2020, 415 consecutive patients who underwent pancreaticoduodenectomy (PD) for dCC in 10 centers were identified. Multivariate Cox analysis was used to identify all independent risk factors among several prognostic factors. A nomogram was then developed and assessed by integrating the independent prognostic factors into the model, and the concordance index (C-index) was used to evaluate its performance. RESULTS: According to Cox regression multivariate analysis, a nomogram based on independent prognostic factor for RFS was performed including LNR 15 (hazard ratio [HR] 2.442, 95% CI 1.348–4.425, p = 0.003), perineural invasion (HR 3.100, 95% CI 1.183–8.122, p = 0.025), differentiation grade (HR 2.100, 95% CI 1.172–4.143, p = 0.021), and radicality of PD (HR 2.276, 95% CI 1.223–4.234, p = 0.009). The C-index of the nomogram, tailored based on the previous significant factors, was 0.8. CONCLUSIONS: LNR15 yields a high prognostic efficiency for RFS. The nomogram based on LNR can provide an accurate prognosis assessment for patients with resected dCC. [ABSTRACT FROM AUTHOR]

Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA. This retrospective multicentric observational study included patients with dCCA from 13 international centers who underwent curative pancreaticoduodenectomy (PD). A LASSO-regularized Cox regression model was used to feature selection, examine the path of the coefficient and create a model to predict recurrence. Internal and external validation and model performance were assessed using the C-index score. Additionally, a web application was developed to enhance the clinical use of the algorithm. Among 654 patients, LNR (Lymph Node Ratio) 15, neural invasion, N stage, surgical radicality, and differentiation grade emerged as significant predictors of disease-free survival (DFS). The model showed the best discrimination capacity with a C-index value of 0.8 (CI 95 %, 0.77%–0.86 %) and highlighted LNR15 as the most influential factor. Internal and external validations showed the model's robustness and discriminative ability with an Area Under the Curve of 92.4 % (95 % CI, 88.2%–94.4 %) and 91.5 % (95 % CI, 88.4%–93.5 %), respectively. The predictive model is available at https://imim.shinyapps.io/LassoCholangioca/. This study pioneers the integration of machine learning into prognostic modeling for dCCA, yielding a robust predictive model for DFS following PD. The tool can provide information to both patients and healthcare providers, enhancing tailored treatments and follow-up. [ABSTRACT FROM AUTHOR]

Aim of database: The Danish Pancreatic Cancer Database aims to prospectively register the epidemiology, diagnostic workup, diagnosis, treatment, and outcome of patients with pancreatic cancer in Denmark at an institutional and national level. Study population: Since May 1, 2011, all patients with microscopically verified ductal adenocarcinoma of the pancreas have been registered in the database. As of June 30, 2014, the total number of patients registered was 2,217. All data are cross-referenced with the Danish Pathology Registry and the Danish Patient Registry to ensure the completeness of registrations. Main variables: The main registered variables are patient demographics, performance status, diagnostic workup, histological and/or cytological diagnosis, and clinical tumor stage. The following data on treatment are registered: type of operation, date of first adjuvant, neoadjuvant, and first palliative chemo- or chemoradiation therapy, and dates for milestones in referrals, diagnostic workup, treatment decisions, and treatment. For patients undergoing resection, data on operative evaluation of tumor stage, histological diagnosis, and duration of hospital stay are registered. Descriptive data: Death is monitored using data from the Danish Civil Registry. This registry monitors the survival status of the Danish population, and the registration is virtually complete. All data in the database are audited by all participating institutions, with respect to baseline characteristics, key indicators, and survival. The results are published annually. Conclusion: The Danish Pancreatic Cancer Database has registered data on 2,217 patients with microscopically verified ductal adenocarcinoma of the pancreas. The data have been obtained nationwide over a period of 4 years and 2 months. The completeness of registration was 82%. The observed overall 3-year survival after diagnosis was 6%. [ABSTRACT FROM AUTHOR]