BACKGROUND:This study developed and validated a nomogram to predict patient prognosis for pancreatic ductal adenocarcinoma (PDAC) of the head of the pancreas following pancreaticoduodenectomy. MATERIAL AND METHODS:Retrospective data were obtained fro...

The aim of this study was to establish and validate an individualized nomogram for predicting disease-specific survival (DSS) in patients with non-metastat

Purpose: Pancreatic cancer (PC) has poor prognosis despite systemic treatment. Dehydrogenase/reductase member 9 (DHRS9) has been reported to be involved in many events of tumorigenesis, but its prognostic impact in PC remains undetermined. Thus, this study aimed to explore the association between DHRS9 expression and the prognosis of PC and investigate the possible mechanism by which DHRS9 is involved in PC progression. Patients and Methods: The study used data: from Gene Expression Omnibus (GEO) and our institution to compare the DHRS9 expression between PC and adjacent normal tissues; from The Cancer Genome Atlas (TCGA) and our institution to assess the clinicopathological characteristics and prognosis of PC patients in high and low DHRS9 expression groups; and from TCGA to predict the potential mechanism of DHRS9 in PC. Western blot assay was used to identify DHRS9 expression in specimens collected from five patients who underwent surgery in our institute. Furthermore, immunohistochemistry (IHC) was then used to identify DHRS9 expression in the specimens of 109 patients who underwent surgery at our institute. Kaplan–Meier and Cox regression analyses were used to assess the prognostic significance of DHRS9 expression among PC patients. Results: All the IHC, Western blot, and GEO datasets indicated that compared to normal tissues, DHRS9 was significantly overexpressed in PC tissues. IHC results demonstrated that the strong intensity of DHRS9 expression was significantly correlated with vascular infiltration (P < 0.05). Further, high DHRS9 expression was identified as a prognostic risk factor for overall survival. Functional analysis of DHRS9 co-expressed genes indicated that DHRS9 was involved in mitogen-activated protein kinases/extracellular signal-regulated kinase (MAPK/ERK) signaling pathway. Conclusion: DHRS9 is upregulated in PC tissue, and high DHRS9 expression is correlated with poor prognosis in PC. DHRS9 may affect the oncological process of PC through MAPK/ERK pathway.

Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further improved accuracy. In this study, we aimed to construct a combined prognostic model using deep learning-extracted features from digital pathology images of pancreatic ductal adenocarcinoma (PDAC) alongside clinical predictive indicators and to explore its prognostic value. A retrospective analysis was conducted on 142 postoperative pathologically confirmed PDAC cases. These cases were divided into training (n = 114) and testing sets (n = 28) at an 8:2 ratio. Tumor whole-slide imaging features were extracted and screened to construct a pathological risk model based on a pre-trained deep learning model. Clinical and pathological data from the training set were used to select independent predictive factors for PDAC and establish a clinical risk model using LASSO, univariate, and multivariate Cox regression analyses. Based on the pathological and clinical risk models, a combined model was developed. The Harrell concordance index (C-index) was computed to assess the predictive performance of each model for PDAC survival prognosis. For the training and testing sets, the C-index values for the clinical risk model were 0.76 and 0.75, respectively; for the pathological risk model, they were 0.82 and 0.73, respectively; and for the combined model, they were 0.86 and 0.77, respectively. The combined model exhibited appropriate calibration at 1-, 3-, and 5-year time points, as well as a superior area under the curve of the receiver operating characteristic curve and clinical net benefit compared to the single models. Integrating the pathological and clinical risk models may provide a higher predictive value for survival prognosis.

Background: This study aimed to investigate the risk factors for 30-day mortality in patients with malignant biliary obstruction (MBO) after endoscopic retrograde cholangiopancreatography (ERCP) with endobiliary metal stent placement. Furthermore, we aimed to construct and visualize a prediction model based on LASSO-logistic regression. Methods: Data were collected from 245 patients who underwent their first ERCP with endobiliary metal stent placement for unresectable MBO between June 1, 2013, and August 31, 2021. Univariable and multivariable logistic regression analyses were conducted to identify the risk factors for 30-day mortality. We subsequently developed a logistic regression model that incorporated multiple parameters identified by LASSO regression. The model was visualized and the nomogram was plotted. Risk stratification was performed based on nomogram-derived scores. Results: The 30-day mortality rate was 10.7% (23/245 patients). Distant metastasis, total bilirubin, post-ERCP complications, and successful drainage were independent risk factors of 30-day mortality. The variables screened by LASSO regression, including distant metastasis, total bilirubin, post-ERCP complications, and successful drainage, were incorporated into the logistic model. The results were visualized through a nomogram based on the model. To assess the model’s performance, discrimination was evaluated using the area-under-the-curve values obtained from receiver operating characteristic analyses with 10-fold cross-validation in the training group and validated in the testing group. The calibration curve showed the good predictive ability of the model. Decision curve analysis is used to evaluate the clinical application of nomogram. Finally, we performed risk stratification based on the risk calculated using the nomogram. Patients were assigned to the low-, moderate-, and high-risk groups based on their probability scores. The Kaplan–Meier survival curves for the different nomogram-based groups were significantly different (p < 0.001). Conclusions: We developed a nomogram using the LASSO-logistic regression model to forecast the 30-day mortality rate in patients who had undergone ERCP with endobiliary metal stent placement due to MBO. This nomogram can assist in identifying individuals at high-risk of 30-day mortality following ERCP.

Early recurrence is a major challenge for patients with pancreatic ductal adenocarcinoma (PDAC) after radical resection. Early identification of high-risk