Prognostic nomogram for patients undergoing resection for adenocarcinoma of the pancreas

Value of radiomics features extracted from baseline computed tomography images in predicting overall survival in patients with nonsurgical pancreatic ductal adenocarcinoma: incorporation of a radiomics score to a multiparametric nomogram to predict 1-year overall survival

PURPOSE

This study aimed to determine the value of radiomics features derived from baseline computed tomography (CT) scans and volumetric measurements to predict overall survival (OS) in patients with nonsurgical pancreatic ductal adenocarcinoma (PDAC) treated with a chemotherapy combination regimen of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX).

METHODS

In this retrospective single-institution study, 131 patients with nonsurgical PDAC who received FOLFIRINOX neoadjuvant chemotherapy between December 2012 and November 2021 were included. Pretreatment contrast-enhanced CT images were obtained for all patients before inclusion. The primary tumor was contoured by an expert radiologist with 25 years of experience. A total of 845 radiomics features, including first-, second-, and higher-order features, were extracted from the total tumor volume. A feature reduction pipeline was used to reduce the dimensionality of the data. The selected features were used to generate a radiomics score based on the Least Absolute Shrinkage and Selection Operator coefficients. A high-dimensional Cox model was generated on the basis of the radiomics score and other quantitative and semantic imaging findings.

RESULTS

From the 845 radiomics features extracted, 45 were significantly different between the tertiles. The following equation was used to generate a radiomics score: radiomics score = SmallAreaEmphasis (-66.87801 + LargeDependenceEmphasis) - 0.2345916. The radiomics score was significantly different among the 3 groups of the radiomics features (P = .034). The overall difference in survival was significant among the 3 groups (P = .02). The nomogram showed good calibration and showed significant differences among the patients when they were classified as tertiles (P < .00).

CONCLUSION

Radiomics approaches have the potential to predict OS in nonsurgical patients with PDAC, and the inclusion of semantic imaging findings and pathologic data could further enhance prognostication in patients with PDAC.

Expert Judgment Supporting a Bayesian Network to Model the Survival of Pancreatic Cancer Patients

Purpose: Pancreatic cancer is known for its poor prognosis. The most effective treatment combines surgery with peri-operative chemotherapy. Current prognostic tools are designed to predict patient outcomes and inform treatment decisions based on collected data. Bayesian networks (BNs) can integrate objective data with subjective clinical insights, such as expert opinions, or they can be independently based on either element. This pilot study is one of the first efforts to incorporate expert opinions into a prognostic model using a Bayesian framework. Methods: A clinical hybrid BN was selected to model the long-term overall survival of pancreatic cancer patients. The SHELF expert judgment method was employed to enhance the BN's effectiveness. This approach involved a two-phase protocol: an initial single-center pilot phase followed by a definitive international phase. Results: Experts generally agreed on the distribution shape among the 12 clinically relevant predictive variables identified for the BN. However, discrepancies were noted in the tumor size, age, and ASA score nodes. With regard to expert concordance for each node, tumor size, and ASA score exhibited absolute concordance, indicating a strong consensus among experts. Ca19.9 values and resectability status showed high concordance, reflecting a solid agreement among the experts. The remaining nodes showed acceptable concordance. Conclusions: This project introduces a novel clinical hybrid Bayesian network (BN) that incorporates expert elicitation and clinical variables present at diagnosis to model the survival of pancreatic cancer patients. This model aims to provide research-based evidence for more reliable prognosis predictions and improved decision-making, addressing the limitations of existing survival prediction models. A validation process will be essential to evaluate the model's performance and clinical applicability.

Construction of prognostic nomogram based on the SEER database for esophageal cancer patients

Currently, the incidence of esophageal cancer continues to rise around the world. Because of its good early prognosis, it is of great significance to establish an effective model for predicting the survival of EC patients. The purpose of this study was to predict survival after diagnosis in Esophageal Cancer (EC) patients by constructing a valid clinical nomogram. In this study, 5037 EC patient samples diagnosed from 2010 to 2015 were screened by accessing the SEER database, and 8 independent prognostic factors were screened by various methods, and Cox multivariate regression was included to construct a prognostic model and nomogram for esophageal cancer. to estimate esophageal cancer recurrence and overall survival. Calibration of the nomogram predicted probabilities of 1-year, 3-year and 5-year survival probability, which were closely related to actual survival. In conclusion, this study validated that the column-line graphical model can be considered an individualized quantitative tool for predicting the prognosis of patients with EC in order to assist clinicians in making therapeutic decisions.

Nomogram for predicting survival of patients with diffuse large B-cell lymphoma

The international prognostic index (IPI) system has been widely used to predict prognosis in diffuse large B-cell lymphoma (DLBCL). However, this system categorizes DLBCL patients into four risk groups, and cannot optimize individualized prognosis. In addition, other clinicopathological factors, such as molecular aberrations, are not incorporated into the system. To partly overcome these weak points, we developed nomograms to predict individual patient survival. We also incorporated MYD88L265P and CD79BY196 mutations into the nomograms since these mutations are associated with a worse prognosis and their signaling pathways have been highlighted as a therapeutic target. We analyzed 302 DLBCL cases for which multivariate analysis by Cox proportional hazard regression was performed. Nomograms for progression-free survival (PFS) and overall survival (OS) were constructed and assessed by a concordance index (C-index). The nomograms were also evaluated using an open external dataset (n = 187). The MYD88L265P and/or CD79BY196 (MYD88/CD79B) mutation was detected in 62/302 patients. The nomograms incorporating IPI factors exhibited a C-index of 0.738 for PFS and a C-index of 0.765 for OS. The nomograms incorporating IPI factors and the MYD88/CD79B mutation showed a C-index of 0.745 for PFS and a C-index of 0.769 for OS. The nomograms we created were evaluated using an external dataset and were well validated. The present nomograms incorporating IPI factors and the MYD88/CD79B mutation have sufficient discrimination ability, and may effectively predict prognosis in DLBCL patients. The prognostic models we have presented here may help clinicians personalize prognostic assessments and clinical decisions.

Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality

Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.

Development and validation of a nomogram for Siewert II esophagogastric junction adenocarcinoma: a retrospective analysis

Background

Due to the complex histological type and anatomical structures, there has been considerable debate on the classification of adenocarcinoma of the esophagogastric junction (AEG), especially Siewert II AEG. Furthermore, neither the American Joint Committee on Cancer (AJCC) 7th tumor-node-metastasis (TNM) [esophageal adenocarcinoma (E) or gastric cancer (G)] nor the AJCC 8th TNM (E or G) accurately predicted the prognosis of patients with Siewert II AEG.

Objective

This study aimed to investigate the factors influencing the survival and prognosis of patients with Siewert II AEG and establish a new and better prognostic predictive model.

Design

A retrospective study.

Methods

Patients with Siewert II AEG, retrieved from the Surveillance, Epidemiology, and End Results (SEER) databases, were assigned to the training set. Patients retrieved from a single tertiary medical center were assigned to the external validation set. Significant variables were selected using univariate and multivariate Cox regression analyses to construct the nomogram. Nomogram models were assessed using the concordance index (C-index), a calibration plot, decision curve analysis (DCA), and external validation.

Results

Age, tumor grade, and size, as well as the T, N, and M stages, were included in the nomograms. For the SEER training set, the C-index of the nomogram was 0.683 (0.665-0.701). The C-index of the nomogram for the external validation set was 0.690 (0.653-0.727). The calibration curve showed good agreement between the nomogram estimations and actual observations in both the training and external validation sets. The DCA showed that the nomogram was clinically useful.

Conclusion

The new predictive model showed significant accuracy in predicting the prognosis of Siewert II AEG.

Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features

OBJECTIVES

To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer.

METHODS

We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity.

RESULTS

Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort.

CONCLUSIONS

The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.

An Integrated Multi-Omics Analysis Identifying Immune Subtypes of Pancreatic Cancer

Limited studies have explored novel pancreatic cancer (PC) subtypes or prognostic biomarkers based on the altered activity of relevant signaling pathway gene sets. Here, we employed non-negative matrix factorization (NMF) to identify three immune subtypes of PC based on C7 immunologic signature gene set activity in PC and normal samples. Cluster 1, the immune-inflamed subtype, showed a higher response rate to immune checkpoint blockade (ICB) and had the lowest tumor immune dysfunction and exclusion (TIDE) scores. Cluster 2, the immune-excluded subtype, exhibited strong associations with stromal activation, characterized by elevated expression levels of transforming growth factor (TGF)-β, cell adhesion, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) related genes. Cluster 3, the immune-desert subtype, displayed limited immune activity. For prognostic prediction, we developed an immune-related prognostic risk model (IRPM) based on four immune-related prognostic genes in pancreatic cancer, RHOF, CEP250, TSC1, and KIF20B. The IRPM demonstrated excellent prognostic efficacy and successful validation in an external cohort. Notably, the key gene in the prognostic model, RHOF, exerted significant influence on the proliferation, migration, and invasion of pancreatic cancer cells through in vitro experiments. Furthermore, we conducted a comprehensive analysis of somatic mutational landscapes and immune landscapes in PC patients with different IRPM risk scores. Our findings accurately stratified patients based on their immune microenvironment and predicted immunotherapy responses, offering valuable insights for clinicians in developing more targeted clinical strategies.

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors.

METHODS

The PubMed database was searched for papers published in the English language no earlier than January 2018.

RESULTS

We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective.

CONCLUSIONS

It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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.

Accuracy of models to prognosticate survival after surgery for pancreatic cancer in the era of neoadjuvant therapy

BACKGROUND

Outcomes for pancreatic adenocarcinoma (PDAC) remain difficult to prognosticate. Multiple models attempt to predict survival following the resection of PDAC, but their utility in the neoadjuvant population is unknown. We aimed to assess their accuracy among patients that received neoadjuvant chemotherapy (NAC).

METHODS

We performed a multi-institutional retrospective analysis of patients who received NAC and underwent resection of PDAC. Two prognostic systems were evaluated: the Memorial Sloan Kettering Cancer Center Pancreatic Adenocarcinoma Nomogram (MSKCCPAN) and the American Joint Committee on Cancer (AJCC) staging system. Discrimination between predicted and actual disease-specific survival was assessed using the Uno C-statistic and Kaplan-Meier method. Calibration of the MSKCCPAN was assessed using the Brier score.

RESULTS

A total of 448 patients were included. There were 232 (51.8%) females, and the mean age was 64.1 years (±9.5). Most had AJCC Stage I or II disease (77.7%). For the MSKCCPAN, the Uno C-statistic at 12-, 24-, and 36-month time points was 0.62, 0.63, and 0.62, respectively. The AJCC system demonstrated similarly mediocre discrimination. The Brier score for the MSKCCPAN was 0.15 at 12 months, 0.26 at 24 months, and 0.30 at 36 months, demonstrating modest calibration.

CONCLUSIONS

Current survival prediction models and staging systems for patients with PDAC undergoing resection after NAC have limited accuracy.

Preoperative C-Reactive Protein-to-Albumin Ratio and Its Ability to Predict Outcomes of Pancreatic Cancer Resection: A Systematic Review

OBJECTIVES

To evaluate the ability of the c-reactive protein-to-albumin ratio (CAR) in predicting outcomes in patients undergoing pancreatic cancer resection.

METHODS

A systematic search of electronic information sources and bibliographic reference lists was conducted. Survival outcomes and perioperative morbidity were the evaluated outcome parameters.

RESULTS

Eight studies reporting a total of 1056 patients undergoing pancreatic cancer resection were identified. The median cut-off value for CAR was 0.05 (range 0.0003-0.54). Using multivariate analysis, all studies demonstrated that a higher CAR value was an independent and significant predictor of poor overall survival in patients undergoing pancreatic cancer resection. The estimated hazard ratio (HR) ranged from 1.4 to 3.6. Although there was a positive correlation between the reported cut-off values for CAR and HRs for overall survival, it was weak and non-significant (r = 0.36, n = 6, p = 0.480). There was significant between-study heterogeneity.

CONCLUSIONS

Preoperative CAR value seems to be an important prognostic score in predicting survival outcomes in patients undergoing pancreatic cancer resection. However, the current evidence does not allow the determination of an optimal cut-off value for CAR, considering the heterogeneous reporting of cut-off values by the available studies and the lack of knowledge of their sensitivity and specificity. Future research is required.

Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study

OBJECTIVE

To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning.

BACKGROUND

Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer.

METHODS

This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness.

RESULTS

Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk.

CONCLUSIONS

Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Small RNA sequencing analysis of peptide-affinity isolated plasma extracellular vesicles distinguishes pancreatic cancer patients from non-affected individuals

Pancreatic ductal adenocarcinoma (PDAC) has a high fatality rate, mainly due to its asymptomatic nature until late-stage disease and therefore delayed diagnosis that leads to a lack of timely treatment intervention. Consequently, there is a significant need for better methods to screen populations that are at high risk of developing PDAC. Such advances would result in earlier diagnosis, more treatment options, and ultimately better outcomes for patients. Several recent studies have applied the concept of liquid biopsy, which is the sampling of a biofluid (such as blood plasma) for the presence of disease biomarkers, to develop screening approaches for PDAC; several of these studies have focused on analysis of extracellular vesicles (EVs) and their cargoes. While these studies have identified many potential biomarkers for PDAC that are present within EVs, their application to clinical practice is hindered by the lack of a robust, reproducible method for EV isolation and analysis that is amenable to a clinical setting. Our previous research has shown that the Vn96 synthetic peptide is indeed a robust and reproducible method for EV isolation that has the potential to be used in a clinical setting. We have therefore chosen to investigate the utility of the Vn96 synthetic peptide for this isolation of EVs from human plasma and the subsequent detection of small RNA biomarkers of PDAC by Next-generation sequencing (NGS) analysis. We find that analysis of small RNA from Vn96-isolated EVs permits the discrimination of PDAC patients from non-affected individuals. Moreover, analyses of all small RNA species, miRNAs, and lncRNA fragments are most effective at segregating PDAC patients from non-affected individuals. Several of the identified small RNA biomarkers have been previously associated with and/or characterized in PDAC, indicating the validity of our findings, whereas other identified small RNA biomarkers may have novel roles in PDAC or cancer in general. Overall, our results provide a basis for a clinically-amendable detection and/or screening strategy for PDAC using a liquid biopsy approach that relies on Vn96-mediated isolation of EVs from plasma.

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

BACKGROUND

When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes toward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma.

METHODS

Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neoadjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neoadjuvant therapy.

RESULTS

The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P < .001). The regret of the commission was diametrically opposite to the regret of omission (P ≤ .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P ≤ .001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy.

CONCLUSION

Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.

Prediction nomogram development and validation for postoperative radiotherapy in the management of oral squamous cell carcinoma

BACKGROUND

Predictive nomograms are useful tools to guide clinicians in estimating disease course. Oral squamous cell carcinoma (OSCC) patients would benefit from an interactive prediction calculator that defines their levels of survival-risk specific to their tumors to guide the use of postoperative radiotherapy (PORT).

METHODS

Patients with OSCC surgically treated with curative intent at four Head and Neck Cancer Centres were recruited retrospectively for development and validation of nomograms. Predictor variables include PORT, age, T and N classification, surgical margins, perineural invasion, and lymphovascular invasion. Outcomes were disease-free, disease-specific, and overall survivals over 5 years.

RESULTS

1296 patients with OSCC were in training cohort for nomogram analysis. Algorithms were developed to show relative benefit of PORT in survivals for higher-risk patients. External validation on 1212 patients found the nomogram to be robust with favorable discrimination and calibration.

CONCLUSION

The proposed calculator can assist clinicians and patients in the decision-making process for PORT.

Analysis of Risk Factors for Distant Metastasis of Pancreatic Ductal Adenocarcinoma without Regional Lymph Node Metastasis and a Nomogram Prediction Model for Survival

Background

Negative regional lymph nodes do not indicate a lack of distant metastasis. A considerable number of patients with negative regional lymph node pancreatic cancer will skip the step of regional lymph node metastasis and directly develop distant metastasis.

Methods

We retrospectively analyzed the clinicopathological characteristics of patients with negative regional lymph node pancreatic cancer and distant metastasis in the Surveillance, Epidemiology, and End Results database from 2010 to 2015. Multivariate logistic analysis and Cox analysis were used to determine the independent risk factors that promoted distant metastasis and the 1-, 2-, and 3-year cancer-specific survival in this subgroup.

Results

Sex, age, pathological grade, surgery, radiotherapy, race, tumor location, and tumor size were significantly correlated with distant metastasis (P < 0.05). Among these factors, pathological grade II and above, tumor site other than the pancreatic head, and tumor size >40 mm were independent risk factors for distant metastasis; age ≥60 years, tumor size ≤21 mm, surgery, and radiation were protective factors against distant metastasis. Age, pathological grade, surgery, chemotherapy, and metastasis site were identified as predictors of survival. Among them, age ≥40 years, pathological grade II and above, and multiple distant metastasis were considered independent risk factors for cancer-specific survival. Surgery and chemotherapy were considered protective factors for cancer-specific survival. The prediction performance of the nomogram was significantly better than that of the traditional American Joint Committee on Cancer tumor, node, metastasis staging system. We also established an online dynamic nomogram calculator, which can predict the survival rate of patients at different follow-up time points.

Conclusion

Pathological grade, tumor location, and tumor size were independent risk factors for distant metastasis in pancreatic ductal adenocarcinoma with negative regional lymph nodes. Older age, smaller tumor size, surgery, and radiotherapy were protective factors against distant metastasis. A new nomogram that was constructed could effectively predict cancer-specific survival in pancreatic ductal adenocarcinoma with negative regional lymph nodes and distant metastasis. Furthermore, an online dynamic nomogram calculator was established.

A population-based study of synchronous distant metastases and prognosis in patients with PDAC at initial diagnosis

Objective

Cancer of the pancreas is a life-threatening condition and has a high distant metastasis (DM) rate of over 50% at diagnosis. Therefore, this study aimed to determine whether patterns of distant metastases correlated with prognosis in pancreatic ductal adenocarcinoma (PDAC) with metastatic spread, and build a novel nomogram capable of predicting the 6, 12, 18-month survival rate with high accuracy.

Methods

We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of PDAC with DM. Kaplan-Meier analysis, log-rank tests and Cox-regression proportional hazards model were used to assess the impact of site and number of DM on the cancer-specific survival (CSS) and over survival (OS). A total of 2709 patients with DM were randomly assigned to the training group and validation group in a 7:3 ratio. A nomogram was constructed by the dependent risk factors which were determined by multivariate Cox-regression analysis. An assessment of the discrimination and ability of the prediction model was made by measuring AUC, C-index, calibration curve and decision curve analysis (DCA). In addition, we collected 98 patients with distant metastases at the time of initial diagnosis from Ningbo University Affiliated LiHuili Hospital to verify the efficacy of the prediction model.

Results

There was a highest incidence of liver metastases from pancreatic cancer (2387,74.36%), followed by lung (625,19.47%), bone (190,5.92%), and brain (8,0.25%). The prognosis of liver metastases differed from that of lung metastases, and the presence of multiple organ metastases was associated with poorer prognosis. According to univariate and multivariate Cox-regression analyses, seven factors (i.e., diagnosis age, tumor location, grade of tumor differentiation, T-stage, receipt of surgery, receipt of chemotherapy status, presence of multiple organ metastases) were included in our nomogram model. In internal and external validation, the ROC curves, C-index, calibration curves and DCA were calculated, which confirmed that this nomogram can precisely predict prognosis of PDAC with DM.

Conclusion

Metastatic PDAC patients with liver metastases tended to have a worse prognosis than those with lung metastases. The number of DM had significant effect on the overall survival rate of metastatic PDAC. This study had a high prediction accuracy, which was helpful clinicians to analyze the prognosis of PDAC with DM and implement individualized diagnosis and treatment.

Surgical Decision-Making in Pancreatic Ductal Adenocarcinoma: Modeling Prognosis Following Pancreatectomy in the Era of Induction and Neoadjuvant Chemotherapy

OBJECTIVE

To develop a predictive model of oncologic outcomes for patients with pancreatic ductal adenocarcinoma (PDAC) undergoing resection after neoadjuvant or induction chemotherapy use.

BACKGROUND

Early recurrence following surgical resection for PDAC is common. The use of neoadjuvant chemotherapy prior to resection may increase the likelihood of long-term systemic disease control. Accurately characterizing an individual's likely oncologic outcome in the perioperative setting remains challenging.

METHODS

Data from patients with PDAC who received chemotherapy prior to pancreatectomy at a single high-volume institution between 2007 and 2018 were captured in a prospectively collected database. Core clinicopathologic data were reviewed for accuracy and survival data were abstracted from the electronic medical record and national databases. Cox-proportional regressions were used to model outcomes and develop an interactive prognostic tool for clinical decision-making.

RESULTS

A total of 581 patients were included with a median overall survival (OS) and recurrence-free survival (RFS) of 29.5 (26.5-32.5) and 16.6 (15.8-17.5) months, respectively. Multivariable analysis demonstrates OS and RFS were associated with type of chemotherapeutic used andthe number of chemotherapy cycles received preoperatively. Additional factors contributing to survival models included: tumor grade, histopathologic response to therapy, nodal status, and administration of adjuvant chemotherapy. The models were validated using an iterative bootstrap method and with randomized cohort splitting. The models were well calibrated with concordance indices of 0.68 and 0.65 for the final OS and RFS models, respectively.

CONCLUSION

We developed an intuitive and dynamic decision-making tool that can be useful in estimating OS, RFS, and location-specific disease recurrence rates. This prognostic tool may add value to patient care in discussing the benefits associated with surgical resection for PDAC.

Hepatic artery lymph node relevance in periampullary tumors: A retrospective analysis of survival outcomes

Background

The Periampullary area comprehends a heterogeneous and complex structure with different histological tissues. Surgical standards include the peripancreatic regional lymphadenectomy, and during pancreatoduodenectomy (PD) the hepatic artery lymph node HALN(8a) is dissected. We aimed to describe the prognostic significance of the HALN(8a) lymph node metastasis in terms of disease-free survival (DFS) and overall survival (OS) in a specific cohort of patients in limited economic and social conditions.

Methods

A retrospective study was conducted based on a prospective database from the HPB department of patients who underwent pancreaticoduodenectomy (PD) due to periampullary tumors during 2014-2021. Overall survival (OS) and disease-free survival (DFS) were estimated to be associated with positive HALN(8a) using Kaplan-Meier analysis. Log Rank test and Cox proportional hazards regression analysis was used.

Results

111 patients were included, 55,4% female. The most frequent pathology was ductal adenocarcinoma (60.3%). The positive rate of the HALN(8a) node was 21.62%. The Median OS time was 25.5 months, and the median DFS time was 13,8 months. Positive HLAN(8a) node, the cutoff of lymph node ratio resection (LNRR), and vascular invasion showed a strong association with OS. (CoxRegression p = 0.03 HR 0.5, p 0.003 HR = 1.8, p = 0.02 HR 0.4 CI 95%). In terms of DFS, lymph node ratio cutoff, tumoral size, and vascular invasion showed a statistically significant association with the outcome (p = 0.008, HR = 1.5; p = 0.04 HR = 2.1; p = 0.02 HR = 0.4 CI 95%).

Conclusion

In this series of PD, OS was reduced in patients with HALN(8a) compromise in patients with pancreatic cancer, however without statistical significance in DFS. In multivariate analysis, lymph node status remains an independent predictor of OS and DFS. Further studies are needed.

Fifty years of pancreas cancer care

Resulting from 50 years of innovation, operations for pancreatic neoplasms can now be performed safely, albeit with significant but manageable morbidity. Molecular diagnosis has allowed for the identification of multiple distinct histopathologies with variable natural histories. Observation is now a strategy for selected indolent cysts and some neuroendocrine neoplasms. For ductal pancreatic adenocarcinoma, a long-term cure remains elusive and will require more than surgical resection for meaningful progress.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

Development of Nomograms for Predicting Prognosis of Pancreatic Cancer after Pancreatectomy: A Multicenter Study

Surgical resection is the only curative treatment for pancreatic ductal adenocarcinoma (PDAC). Currently, the TNM classification system is considered the standard for predicting prognosis after surgery. However, the prognostic accuracy of the system remains limited. This study aimed to develop new predictive nomograms for resected PDAC. The clinicopathological data of patients who underwent surgery for PDAC between 2006 and 2015 at five major institutions were retrospectively reviewed; 885 patients were included in the analysis. Cox regression analysis was performed to investigate prognostic factors for recurrence and survival, and statistically significant factors were used for creating nomograms. The nomogram for predicting recurrence-free survival included nine factors: sarcopenic obesity, elevated carbohydrate antigen 19-9, platelet-to-lymphocyte ratio, preoperatively-identified arterial abutment, estimated blood loss (EBL), tumor differentiation, size, lymph node ratio, and tumor necrosis. The nomogram for predicting overall survival included 10 variables: age, underlying liver disease, chronic kidney disease, preoperatively found portal vein invasion, portal vein resection, EBL, tumor differentiation, size, lymph node metastasis, and tumor necrosis. The time-dependent area under the receiver operating characteristic curve for both nomograms exceeded 0.70. Nomograms were developed for predicting survival after resection of PDAC, and the platforms showed fair predictive performance. These new comprehensive nomograms provide information on disease status and are useful for determining further treatment for PDAC patients.

Predictive Model of Early Death of Resectable Pancreatic Ductal Adenocarcinoma After Curative Resection: A SEER-Based Study

OBJECTIVE

This study aims to determine the factors that predict early death and establish a predictive model for early death by analyzing clinical characteristics of patients with resectable pancreatic ductal adenocarcinoma (R-PDAC) who die early after radical surgery.

MATERIALS AND METHODS

This was a retrospective study of patients who underwent radical surgical resection for R-PDAC in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with overall survival ≤ 12 months were assigned as early death group and above 1 year as the late death group. Univariate and multivariate logistic regression was conducted to identify factors significantly associated with early death. An early death predictive model was constructed based on the identified independent risk factors.

RESULTS

A total of 9695 patients were analyzed, and the total incidence of early death was 30.72%. Multivariable analysis showed that factors significantly associated with early death included age at diagnosis, race, marital status, tumor location, tumor size, tumor grade, number of positive lymph nodes, number of examined lymph nodes, positive lymph node ratio, chemotherapy, and radiotherapy. The predictive model showed good discrimination with a C-index of 0.722 (95% confidence interval: 0.711-0.733) and convincing calibration.

CONCLUSIONS

We developed a predictive model that may be easily applied to patients with R-PDAC after radical resection to predict the chance of death within 1 year. For patients with high risk of early death, neoadjuvant therapy should be considered. Even after radical resection, more aggressive adjuvant chemotherapy (with or without combined radiotherapy) must be used to minimize the chance of early death.

Prognosis Based Definition of Resectability in Pancreatic Cancer: A Road Map to New Guidelines

OBJECTIVE

To identify objective preoperative prognostic factors that are able to predict long-term survival of patients affected by PDAC.

SUMMARY OF BACKGROUND DATA

In the modern era of improved systemic chemotherapy for PDAC, tumor biology, and response to chemotherapy are essential in defining prognosis and an improved approach is needed for classifying resectability beyond purely anatomic features.

METHODS

We queried the National Cancer Database regarding patients diagnosed with PDAC from 2010 to 2016. Cox proportional hazard models were used to select preoperative baseline factors significantly associated with survival; final models for overall survival (OS) were internally validated and formed the basis of the nomogram.

RESULTS

A total of 7849 patients with PDAC were included with a median follow-up of 19 months. On multivariable analysis, factors significantly associated with OS included carbohydrate antigen 19-9, neoadjuvant treatment, tumor size, age, facility type, Charlson/Deyo score, primary site, and sex; T4 stage was not independently associated with OS. The cumulative score was used to classify patients into 3 groups: good, intermediate, and poor prognosis, respectively. The strength of our model was validated by a highly significant randomization test, Log-rank test, and simple hazard ratio; the concordance index was 0.59.

CONCLUSION

This new PDAC nomogram, based solely on preoperative variables, could be a useful tool to patients and counseling physicians in selecting therapy. This model suggests a new concept of resectability that is meant to reflect the biology of the tumor, thus partially overcoming existing definitions, that are mainly based on tumor anatomic features.

Selecting surgical candidates with locally advanced pancreatic cancer: a review for modern pancreatology

Pancreatic cancer (PC) is likely to become the second leading cause of malignancy-associated mortality within the next 10 years and surgery remains the best hope for cure. The introduction of effective neoadjuvant treatment (NAT) has increased the resection rate of PC in the era of contemporary pancreatology. This review summarizes the surgical selection criteria for locally advanced PC (LAPC), by focusing on the commonly used predictors for resectability and better overall survival outcome. Based on the currently available evidence, the role of change in carbohydrate antigen 19-9 (CA 19-9) and patient's tumor response to NAT are critical in surgical candidacy selection. Although, consensus on surgical candidacy selection for LAPC still needs to be made, several data have shown that surgery provides the most optimistic chance of cure for PC. Surgery is, therefore, recommended whenever the benefits of pancreatectomy outweigh surgical complications, and the chance of local or distant metastases in the postoperative setting is low. This review also provided our insight for and experience in selecting surgical candidates by focusing on optimizing the overall survival of LAPC patients. Nevertheless, a collaborative approach to formulating standardized criteria for surgical candidate selection and treatment guidelines for LAPC is a common goal that pancreatologists worldwide should focus on.

Downregulation of Notch3 links TIMP3 inhibition to suppress aggressive phenotypes of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), one of the most deadly digestive cancers, has a poor 5-year survival rate and is resistant to chemotherapeutic agents, such as gemcitabine. Notch3 plays an important role in cancer progression, and its expression facilitates chemoresistance in cancers. This study examined the clinical significance of Notch3 and explored the mechanisms through which it may affect disease progression in PDAC. We found Notch3 to be upregulated in PDAC patients in whom it correlated with lymph node stage and poor survival. In vitro and in vivo, functional assays indicated that silencing Notch3 could suppress the growth, migration, invasion of PDAC cells and sensitize PDAC cells to gemcitabine. QPCR array, which was performed to elucidate the Notch3-regulated pathway, revealed that inhibition of Notch3 decreased the transcription and secretion of TIMP3 in PDAC cells. Overexpression of TIMP3 reversed the impaired growth, migration, invasion, and chemosensitivity induced by Notch3 silencing. We also found a positive correlation between Notch3 mRNA expression and TIMP3 expression in patients with PDAC. We concluded that blocking Notch3/TIMP3 pathway could considered a potentially new therapeutic strategy for treating PDAC.

Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation

OBJECTIVES

In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model.

METHODS

Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses.

RESULTS

The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40-5.87, p < 0.001)/5.28 (95% CI 2.35-11.86, p < 0.001), respectively, along with age 1.02 (1.01-1.04, p = 0.01)/1.02 (1.00-1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543-0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496-0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524-0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities.

CONCLUSION

A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings.

KEY POINTS

• At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging. • Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters. • Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.

Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy

PURPOSE

Aim of this study is to assess the ability of contrast-enhanced CT image-based radiomic analysis to predict local response (LR) in a retrospective cohort of patients affected by pancreatic cancer and treated with stereotactic body radiation therapy (SBRT). Secondary aim is to evaluate progression free survival (PFS) and overall survival (OS) at long-term follow-up.

METHODS

Contrast-enhanced-CT images of 37 patients who underwent SBRT were analyzed. Two clinical variables (BED, CTV volume), 27 radiomic features were included. LR was used as the outcome variable to build the predictive model. The Kaplan-Meier method was used to evaluate PFS and OS.

RESULTS

Three variables were statistically correlated with the LR in the univariate analysis: Intensity Histogram (StdValue feature), Gray Level Cooccurrence Matrix (GLCM25_Correlation feature) and Neighbor Intensity Difference (NID25_Busyness feature). Multivariate model showed GLCM25_Correlation (P = 0.007) and NID25_Busyness (P = 0.03) as 2 independent predictive variables for LR. The odds ratio values of GLCM25_Correlation and NID25_Busyness were 0.07 (95%CI 0.01-0.49) and 8.10 (95%CI 1.20-54.40), respectively. The area under the curve for the multivariate logistic regressive model was 0.851 (95%CI 0.724-0.978). At a median follow-up of 30 months, median PFS was 7 months (95%CI 6-NA); median OS was 11 months (95%CI 10-22 months).

CONCLUSIONS

This analysis identified a radiomic signature that correlates with LR. To confirm these results, prospective studies could identify patient sub-groups with different rates of radiation dose-response to define a more personalized SBRT approach.

Optimal value of CA19-9 determined by KRAS-mutated circulating tumor DNA contributes to the prediction of prognosis in pancreatic cancer patients

Despite the acceptance of carbohydrate antigen 19-9 (CA19-9) as a valuable predictor for the prognosis of pancreatic ductal adenocarcinoma (PDAC), its cutoff value remains controversial. Our previous study showed a significant correlation between CA19-9 levels and the presence of KRAS-mutated ctDNA in the blood of patients with PDAC. Based on this correlation, we investigated the optimal cutoff value of CA19-9 before surgery. Continuous CA19-9 values and KRAS-mutated ctDNAs were monitored in 22 patients with unresectable PDAC who underwent chemotherapy between 2015 and 2017. Receiver operating characteristic curve analysis identified 949.7 U/mL of CA19-9 as the cutoff value corresponding to the presence of KRAS-mutated ctDNA. The median value of CA19-9 was 221.1 U/mL. Subsequently, these values were verified for their prognostic values of recurrence-free survival (RFS) and overall survival (OS) in 60 patients who underwent surgery between 2005 and 2013. Multivariate analysis revealed that 949.7 U/mL of CA19-9 was an independent risk factor for OS and RFS in these patients (P = 0.001 and P = 0.010, respectively), along with lymph node metastasis (P = 0.008 and P = 0.017), unlike the median CA19-9 level (P = 0.150 and P = 0.210). The optimal CA19-9 level contributes to the prediction of prognosis in patients with PDAC before surgery.

Resected pancreatic adenocarcinoma: An Asian institution's experience

BACKGROUND

Pancreatic adenocarcinoma (PDAC) is highly lethal. Surgery offers the only chance of cure, but 5-year overall survival (OS) after surgical resection and adjuvant therapy remains dismal. Adjuvant trials were mostly conducted in the West enrolling fit patients. Applicability to a general population, especially Asia has not been described adequately.

AIM

We aimed to evaluate the clinical outcomes, prognostic factors of survival, pattern, and timing of recurrence after curative resection in an Asian institution.

METHODS AND RESULTS

The clinicopathologic and survival outcomes of 165 PDAC patients who underwent curative resection between 1998 and 2013 were reviewed retrospectively. Median age at surgery was 62.0 years. 55.2% were male, and 73.3% had tumors involving the head of pancreas. The median OS of the entire cohort was 19.7 months. Median OS of patients who received adjuvant chemotherapy was 23.8 months. Negative predictors of survival include lymph node ratio (LNR) of >0.3 (HR = 3.36, P = .001), tumor site involving the body or tail of pancreas (HR = 1.59, P = .046), presence of perineural invasion (PNI) (HR = 2.36, P = .018) and poorly differentiated/undifferentiated tumor grade (HR = 1.86, P = .058). The median time to recurrence was 8.87 months, with 66.1% and 81.2% of patients developing recurrence at 12 months and 24 months respectively. The most common site of recurrence was the liver.

CONCLUSION

The survival of Asian patients with resected PDAC who received adjuvant chemotherapy is comparable to reported randomized trials. Clinical characteristics seem similar to Western patients. Hence, geographical locations may not be a necessary stratification factor in RCTs. Conversely, lymph node ratio and status of PNI ought to be incorporated.

Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach

Simple Summary

If pancreatic adenocarcinoma is assessed to be technically resectable, curative surgery is still suggested as the primary treatment option; however, the recurrence rate can be very high even in this selected population. The aim of our retrospective study was to develop a preoperative model to accurately stratify upfront resectable patients according to the risk of early distant disease relapse after surgery (<12 months from index procedure). Through a machine learning-based approach, we identified one biochemical marker (serum level of CA19.9), one radiological finding (necrosis) and one radiomic feature (SurfAreaToVolumeRatio), all significantly associated with the early resurge of distant recurrence. A model composed of these three variables only allowed identification of those patients at high risk for early distant disease relapse (50% chance of developing metastases within 12 months after surgery), who would benefit from neoadjuvant chemotherapy instead of upfront surgery.

Abstract

Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (n = 94) and validation cohort (n = 53). Multivariable Cox regression analysis was first applied on the training cohort; the resulting prognostic index was then tested in the validation cohort. Clinical (serum level of CA19.9), radiological (necrosis), and radiomic (SurfAreaToVolumeRatio) features were significantly associated with the early resurge of distant recurrence. The model combining these three variables performed well in the training cohort (p = 0.0015, HR = 3.58, 95%CI = 1.98–6.71) and was then confirmed in the validation cohort (p = 0.0178, HR = 5.06, 95%CI = 1.75–14.58). The comparison of survival curves between low and high-risk patients showed a p-value <0.0001. Our model may help to better define resectability status, thus providing an actual aid for pancreatic adenocarcinoma patients’ management (upfront surgery vs. neoadjuvant chemotherapy). Independent validations are warranted.

Minimally invasive versus open distal pancreatectomy for pancreatic ductal adenocarcinoma (DIPLOMA): study protocol for a randomized controlled trial

BACKGROUND

Recently, the first randomized trials comparing minimally invasive distal pancreatectomy (MIDP) with open distal pancreatectomy (ODP) for non-malignant and malignant disease showed a 2-day reduction in time to functional recovery after MIDP. However, for pancreatic ductal adenocarcinoma (PDAC), concerns have been raised regarding the oncologic safety (i.e., radical resection, lymph node retrieval, and survival) of MIDP, as compared to ODP. Therefore, a randomized controlled trial comparing MIDP and ODP in PDAC regarding oncological safety is warranted. We hypothesize that the microscopically radical resection (R0) rate is non-inferior for MIDP, as compared to ODP.

METHODS/DESIGN

DIPLOMA is an international randomized controlled, patient- and pathologist-blinded, non-inferiority trial performed in 38 pancreatic centers in Europe and the USA. A total of 258 patients with an indication for elective distal pancreatectomy with splenectomy because of proven or highly suspected PDAC of the pancreatic body or tail will be randomly allocated to MIDP (laparoscopic or robot-assisted) or ODP in a 1:1 ratio. The primary outcome is the microscopically radical resection margin (R0, distance tumor to pancreatic transection and posterior margin ≥ 1 mm), which is assessed using a standardized histopathology assessment protocol. The sample size is calculated with the following assumptions: 5% one-sided significance level (α), 80% power (1-β), expected R0 rate in the open group of 58%, expected R0 resection rate in the minimally invasive group of 67%, and a non-inferiority margin of 7%. Secondary outcomes include time to functional recovery, operative outcomes (e.g., blood loss, operative time, and conversion to open surgery), other histopathology findings (e.g., lymph node retrieval, perineural- and lymphovascular invasion), postoperative outcomes (e.g., clinically relevant complications, hospital stay, and administration of adjuvant treatment), time and site of disease recurrence, survival, quality of life, and costs. Follow-up will be performed at the outpatient clinic after 6, 12, 18, 24, and 36 months postoperatively.

DISCUSSION

The DIPLOMA trial is designed to investigate the non-inferiority of MIDP versus ODP regarding the microscopically radical resection rate of PDAC in an international setting.

TRIAL REGISTRATION

ISRCTN registry ISRCTN44897265 . Prospectively registered on 16 April 2018.

CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma

OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.

Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study

BACKGROUND

or Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality in the world with the overall 5-year survival rate of 6%. The survival of patients with PDAC is closely related to recurrence and therefore it is necessary to identify the risk factors for recurrence. This study uses artificial intelligence approaches and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants.

METHODS

Data came from 4846 patients enrolled in a multi-center registry system, the Korea Tumor Registry System (KOTUS). The random forest and the Cox proportional-hazards model (the Cox model) were applied and compared for the prediction of disease-free survival. Variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of disease-free survival after surgery. The C-Index was introduced as a criterion for validating the models trained.

RESULTS

Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively.

CONCLUSIONS

This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.

Refined prognostic staging for resected pancreatic cancer by modified stage grouping and addition of tumour grade

BACKGROUND

With changes in T and N categories the 8th edition of the AJCC/UICC TNM staging system for pancreatic cancer resulted in improved prognostic staging, but inconsistencies were observed with specific stage groups. Tumour grading remains disregarded in prognostic staging. We aimed to validate the current staging system and to investigate the possibility of further optimization by integration of grading.

METHODS

1946 patients undergoing upfront surgical resection for pancreatic adenocarcinoma from 10/2001 to 12/2015 were identified from a prospective institutional database. Survival analyses based on the 8th UICC TNM edition were performed and rare TNM subgroups were reallocated based on survival. The impact of tumour grade on stage-specific survival was assessed and a TNMG staging system was developed.

RESULTS

The 8th UICC staging system accurately stratified prognosis except for comparable survival in stages IB (pT2N0M0) and IIA (pT3N0M0). Regrouping of pT3N0M0 and pT1N1M0 to IB and of pT1N2M0 to II resulted in a modified staging system with higher consistency. High tumour grade (G3&G4 vs G1&G2) was associated with a significantly shorter survival in all new stage groups except for stage IV modified UICC. A TNMG-based prognostic stage grouping in which high tumour grade results in grouping with tumours of the next higher pTNM-stage resulted in improvement of prognostication in non-metastatic pancreatic cancer.

CONCLUSIONS

The 8th edition of the UICC TNM staging system leaves room for improvement. A TNMG staging system with adjustments in group-allocation of specific rarely occurring pTNM subgroups and integration of tumour grade results in improved prognostic stratification.

Intraductal Papillary Mucinous Carcinoma Versus Conventional Pancreatic Ductal Adenocarcinoma: A Comprehensive Review of Clinical-Pathological Features, Outcomes, and Molecular Insights

Intraductal papillary mucinous neoplasms (IPMN) are common and one of the main precursor lesions of pancreatic ductal adenocarcinoma (PDAC). PDAC derived from an IPMN is called intraductal papillary mucinous carcinoma (IPMC) and defines a subgroup of patients with ill-defined specificities. As compared to conventional PDAC, IPMCs have been associated to clinical particularities and favorable pathological features, as well as debated outcomes. However, IPMNs and IPMCs include distinct subtypes of precursor (gastric, pancreato-biliary, intestinal) and invasive (tubular, colloid) lesions, also associated to specific characteristics. Notably, consistent data have shown intestinal IPMNs and associated colloid carcinomas, defining the “intestinal pathway”, to be associated with less aggressive features. Genomic specificities have also been uncovered, such as mutations of the GNAS gene, and recent data provide more insights into the mechanisms involved in IPMCs carcinogenesis. This review synthetizes available data on clinical-pathological features and outcomes associated with IPMCs and their subtypes. We also describe known genomic hallmarks of these lesions and summarize the latest data about molecular processes involved in IPMNs initiation and progression to IPMCs. Finally, potential implications for clinical practice and future research strategies are discussed.

Predicting early recurrence for resected pancreatic ductal adenocarcinoma: a multicenter retrospective study in China

A precise classification of early recurrence (ER) after radical surgery of pancreatic ductal adenocarcinoma (PDAC) has not been standardized. We aim to develop an optimal cut-off based on scientific evidence to distinguish early and late recurrence (LR) for PDAC after radical surgery and develop a predictive model for ER of PDAC. The best threshold for recurrence-free survival (RFS) was assessed with a minimum P-value method, and patients were categorized into ER and LR groups. We used a logistic regression model to assess potential risk factors for ER and develop a predictive model for ER risk. The best threshold between high-risk and intermediate-high-risk groups was identified by using the receiver operating characteristic curve. Among 3,279 patients included, 1,234 (37.6%) experienced ER. The RFS of 9 months is the optimal threshold to distinguish ER and LR. Univariable and multivariable analysis identified four preoperative risk factors for ER, including larger tumor maximal diameter on computed tomography (CT), enlarged lymph nodes on CT, carbohydrate antigen (CA) 125 > 35 U/ml, and CA19-9 > 235 U/ml. The concordance index (C-index) for the predictive model in the training cohort and the validation cohort was 0.651 (95% confidence interval (CI): 0.624-0.678), and 0.636 (95% CI: 0.593-0.679), respectively, showing promising predictive ability. The high-risk group had a score above 203, and the corresponding risk of ER for this group was 56.7%. An RFS of 9 months is the best threshold to distinguish ER and LR. The model can accurately predict the risk of ER in PDAC after radical resection, and risk grouping can predict the patients who could benefit from upfront surgery.

Does a Laparoscopic Approach to Distal Pancreatectomy for Cancer Contribute to Optimal Adjuvant Chemotherapy Utilization?

BACKGROUND

Although laparoscopic distal pancreatectomy (LDP) versus open approaches (ODP) for pancreatic adenocarcinoma (PDAC) is associated with reduced morbidity, its impact on optimal adjuvant chemotherapy (AC) utilization remains unclear. Furthermore, it is uncertain whether oncologic resection quality markers are equivalent between approaches.

METHODS

The National Cancer Database (NCDB) was queried between 2010 and 2016 for PDAC patients undergoing DP. Effect of LDP vs ODP and institutional case volumes on margin status, hospital stay, 30-day and 90-day mortality, administration of and delay to AC, and 30-day unplanned readmission were analyzed using binary and linear logistic regression. Cox multivariable regression was used to correct for confounders.

RESULTS

The search yielded 3411 patients; 996 (29.2%) had LDP and 2415 (70.8%) had ODP. ODP had higher odds of readmission [odds ratio (OR) 1.681, p = 0.01] and longer hospital stay [β 1.745, p = 0.004]. No difference was found for 30-day mortality [OR 1.689, p = 0.303], 90-day mortality [OR 1.936, p = 0.207], and overall survival [HR 1.231, p = 0.057]. The highest-volume centers had improved odds of AC [OR 1.275, p = 0.027] regardless of approach. LDP conferred lower margin positivity [OR 0.581, p = 0.005], increased AC use [3rd quartile: OR 1.844, p = 0.026; 4th quartile; OR 2.144, p = 0.045], and fewer AC delays [4th quartile: OR 0.786, p = 0.045] in higher-volume centers.

CONCLUSIONS

In selected patients, LDP offers an oncologically safe alternative to ODP for PDAC independent of institutional volume. However, additional oncologic benefit due to optimal AC utilization and lower positive margin rates in higher volume centers suggests that LDP by experienced teams can achieve best possible cancer outcomes.

Prognostic Nomogram for patients undergoing radical Pancreaticoduodenectomy for adenocarcinoma of the pancreatic head

Background

Radical pancreaticoduodenectomy is the most common treatment strategy for patients diagnosed with adenocarcinoma of the pancreatic head. Few studies have reported the clinical characteristics and treatment efficacies of patients undergoing radical pancreaticoduodenectomy for adenocarcinoma of the pancreatic head.

Methods

A total of 177 pancreatic head cancer patients who underwent radical pancreaticoduodenectomy and were pathologically confirmed as having pancreatic ductal adenocarcinoma were screened in the West China Hospital of Sichuan University. The multivariate analysis results were implemented to construct a nomogram. The concordance index (c-index), the area under the curve (AUC) and calibration were utilized to evaluate the predictive performance of the nomogram.

Results

The prognostic nutritional index (PNI), the lymph node ratio (LNR) and the American Joint Committee on Cancer (AJCC) staging served as independent prognostic factors and were used to construct the nomogram. The c-indexes of the nomogram were 0.799 (confidence interval (CI), 0.741–0.858) and 0.732 (0.657–0.807) in the primary set and validation set, respectively. The AUCs of the nomogram at 1 and 3 years were 0.832 and 0.783, which were superior to the AJCC staging values of 0.759 and 0.705, respectively.

Conclusions

The nomogram may be used to predict the prognosis of radical resection for adenocarcinoma of the pancreatic head. These findings may represent an effective model for the developing an optimal therapeutic schedule for malnourished patients who need early effective nutritional intervention and may promote the treatment efficacy of resectable adenocarcinoma of the pancreatic head.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08295-5.

Risk Factors and a New Prediction Model for Pancreatic Fistula After Pancreaticoduodenectomy

AIM

In order to find the risk factors of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) according to the latest definition and grading system of International Study Group of Pancreatic Surgery (ISGPS) (version 2016) and propose a nomogram for predicting POPF.

METHODS

We conducted a retrospective analysis of 232 successive cases of PD performed at our hospital by the same operator from August 2012 to June 2020. POPF was diagnosed in accordance with the latest definition of pancreatic fistula from the ISGPS. The risk factors of POPF were analyzed by univariate and multivariate logistic regression analysis. A nomogram model to predict the risk of POPF was constructed based on significant factors.

RESULTS

There were 18 cases of POPF, accounting for 7.8% of the total. Among them, 17 cases were classified into ISGPF grade B and 1 case was classified into ISGPF grade C. In addition, 35 cases were classified into biochemical leak. Univariate and multivariate analysis showed that hypertension, non-diabetes, no history of abdominal surgery, antecolic gastrojejunostomy and soft pancreas were independent risk factors of POPF. Based on significant factors, a nomogram is plotted to predict the risk of POPF. The C-index of this nomogram to assess prediction accuracy was 0.916 (P < 0.001) indicating good prediction performance.

CONCLUSION

Hypertension, non-diabetes, no history of abdominal surgery, antecolic gastrojejunostomy and soft pancreas were independent risk factors of POPF. Meanwhile, a nomogram for predicting POPF with good test performance and discriminatory capacity was constituted.

Nomogram and competing risk model to predict recurrence after curative surgical resection of PDAC

BACKGROUND

Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC). However, a number of patients get disease recurred in a short time post-operation. Few studies have focused on the predictors of different recurrence patterns of PDAC.

OBJECTIVE

To try to establish and verify a nomogram to predict recurrence free survival (RFS) in PDAC patients, and to distinguish the risk factors of local recurrence first and distant metastasis first via competing risk model.

METHODS

Patients who underwent radical pancreatectomy for PDAC in our center from 2010 to 2018 were reviewed retrospectively. Kaplan-Meier methods and multivariate Cox regression analyses were used to identify the clinicopathological predictors of recurrence post-operation. And then, a nomogram was constructed and validated. Competing risk regression model was used to compare the predictors between local recurrence group and distant metastasis group.

RESULTS

A total of 200 patients were included into the final analysis, and 153 patients got disease relapsed post-operation. CA19-9 level, vascular resection, tumor differentiation, lymph node ratio (LNR) and adjuvant chemotherapy were identified as independent risk factors for recurrence free survival (RFS) and incorporated into the nomogram. The C-index of the nomogram was 0.650. Competing risk model indicated that the status of lymph-node metastasis was significantly associated the patterns of first relapse.

CONCLUSIONS

Nomogram and competing risk model were constructed to quantify the risk of recurrence following surgery for PDAC. Our findings may be useful for predicting RFS and recurrence pattern in clinical work.

Impact of completeness of adjuvant gemcitabine, relapse pattern, and subsequent therapy on outcome of patients with resected pancreatic ductal adenocarcinoma - A pooled analysis of CONKO-001, CONKO-005, and CONKO-006 trials

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most fatal malignancies worldwide. It is suggested that survival in PDAC depends, among other things, on pattern of disease recurrence.

PATIENTS AND METHODS

We performed a pooled analysis of the adjuvant therapy studies CONKO-001, CONKO-005, and CONKO-006, including a total of 912 patients with regard to prognostic factors in patients with recurrent disease. Overall survival from disease recurrence (OS 2) and disease-free survival (DFS) from the day of surgery were expressed by Kaplan-Meier method and compared using log-rank testing and Cox regression.

RESULTS

Of 912 patients treated within the previously mentioned CONKO trials, we identified 689 patients with disease recurrence and defined site of relapse. In multivariable analysis, the presence of isolated pulmonary metastasis, low tumour grading, and low postoperative level of CA 19-9 remained significant factors for improved OS 2 and DFS. Furthermore, completeness of adjuvant gemcitabine-based treatment (OS 2: P = 0.006), number of relapse sites (OS 2: P = 0.015), and type of palliative first-line treatment (OS 2: P < 0.001) significantly affected overall survival after disease recurrence in PDAC.

CONCLUSIONS

Determining tumour subgroups using prognostic factors may be helpful to stratify PDAC patients for future clinical trials. In case of disease recurrence, the site of relapse may have a prognostic impact on subsequent survival. Further investigations are needed to identify differences in tumour biology, reflecting relapse patterns and the differing survival of PDAC patients.

Achieving 'Marginal Gains' to Optimise Outcomes in Resectable Pancreatic Cancer

Simple Summary

Improving outcomes in pancreatic cancer is achievable through the accumulation of marginal gains. There exists evidence of variation and undertreatment in many areas of the care pathway. By fully realising the existing opportunities, there is the potential for immediate improvements in outcomes and quality of life.

Abstract

Improving outcomes among patients with resectable pancreatic cancer is one of the greatest challenges of modern medicine. Major improvements in survival will result from the development of novel therapies. However, optimising existing pathways, so that patients realise benefits of already proven treatments, presents a clear opportunity to improve outcomes in the short term. This narrative review will focus on treatments and interventions where there is a clear evidence base to improve outcomes in pancreatic cancer, and where there is also evidence of variation and under-treatment. Avoidance of preoperative biliary drainage, treatment of pancreatic exocrine insufficiency, prehabiliation and enhanced recovery after surgery, reducing perioperative complications, optimising opportunities for elderly patients to receive therapy, optimising adjuvant chemotherapy and regular surveillance after surgery are some of the strategies discussed. Each treatment or pathway change represents an opportunity for marginal gain. Accumulation of marginal gains can result in considerable benefit to patients. Given that these interventions already have evidence base, they can be realised quickly and economically.

Tumor innervation and clinical outcome in pancreatic cancer

Pancreatic cancer is a highly aggressive malignancy characterized by poor survival, recurrence after surgery and resistance to therapy. Nerves infiltrate the microenvironment of pancreatic cancers and contribute to tumor progression, however the clinicopathological significance of tumor innervation is unclear. In this study, the presence of nerves and their cross-sectional size were quantified by immunohistochemistry for the neuronal markers S-100, PGP9.5 and GAP-43 in a series of 99 pancreatic cancer cases versus 71 normal adjacent pancreatic tissues. A trend was observed between the presence of nerves in the tumor microenvironment of pancreatic cancer and worse overall patient survival (HR = 1.8, 95% CI 0.77-4.28, p = 0.08). The size of nerves, as measured by cross-sectional area, were significantly higher in pancreatic cancer than in the normal adjacent tissue (p = 0.002) and larger nerves were directly associated with worse patient survival (HR = 0.41, 95% CI 0.19-0.87, p = 0.04). In conclusion, this study suggests that the presence and size of nerves within the pancreatic cancer microenvironment are associated with tumor aggressiveness.

Validation of a clinical score in predicting pancreatic fistula after pancreaticoduodenectomy

BACKGROUND

Postoperative pancreatic fistula (POPF) is one of the most severe complications after cephalic pancreaticoduodenectomy, with mortality as high as 30%. Risk scores may help predict the risk of POPF. Multiple external validations substantially improve generalized clinical acceptability of a scoring system.

AIM

The aim of this study was to externally validate previously described fistula risk score in the prediction of clinically relevant POPF.

METHODS

All patients who underwent pancreaticoduodenectomy for any indication during a 5-year period were prospectively analyzed. A total of 132 patients were analyzed.

RESULTS

Of the 132 patients, 44 (33.3%) developed pancreatic fistula, including 12.9% biochemical leaks, 7.6% grade B fistula, and 12.9% grade C fistula. Cut-off point of 4.5 was determined to best separate patients who developed clinically relevant POPF with area under curve of 78% (p = .00003). Sensitivity and specificity for the prediction of clinically relevant POPF with the cut-off value of 4.5 was 70.4 and 74.3%, respectively. Positive predictive value with cut-off value 4.5 was 57.8%, and negative predictive value was 83.4%.

CONCLUSION

Fistula risk score identified low risk patients with false negative rate of 16.6%. Further external validation studies on large cohorts of patients and with wide case-mix may enable additional refinements of the score model.

Prognostic Nomogram for Resected Pancreatic Adenocarcinoma: A TRIPOD-Compliant Retrospective Long-Term Survival Analysis

BACKGROUND

Prognostic prediction had been widely used in various cancer entities, from early screening to end-stage patient caring. Currently, there is hardly any well-validated nomogram which exists for long-term survival prediction in pancreatic adenocarcinoma (PC) patients in a post-surgery setting. Our objectives are to identify possible prognostic factors in PC patients following radical resection and to develop a prognostic nomogram based on independent survival predictors.

METHODS

From 2009 to 2014, a total of 432 PC patients who underwent curative intended surgeries with complete follow-up data were included in this current retrospective long-term survival analysis. Clinicopathological data were extracted from medical records, and all missing values (percentage 0.9-8.3%) were imputed five times with the "PMM" method. Cox proportional hazards models were utilized. A nomogram was formulated based on results from the multivariate regression model so as to predict OS at 1-, 2- and 3-year as well as median OS. Validations, including discrimination and calibration, were carried out with 1000 bootstrap resamples. External validation was conducted in order to verify the accuracy of our nomogram at 1 and 2 years by utilizing the clinicopathological data of 122 PC patients who underwent curative intended surgeries in 2015 in our centre.

RESULTS

Age, abdominal pain, back pain, tumour location, preoperative neutrophil-lymphocyte ratio, preoperative CA19-9, tumour differentiation, microscopic nerve invasion, microscopic vascular invasion, T stage, lymph node ratio, M stage and adjuvant chemotherapy were all assembled into nomogram. The concordance index (C-index) of internal and external validation was 0.702 and 0.688, respectively. The C-index of the TNM staging system was 0.572 (P < 0.001 vs. nomogram).

CONCLUSION

Our prognostic nomogram based on clinicopathological parameters shows good performance in long-term survival prediction in PC patients following radical surgery and could play a role in further clinical utilization.

Prognostic Model and Nomogram for Estimating Survival of Small Breast Cancer: A SEER-based Analysis

BACKGROUND

Different clinicopathologic characteristics could contribute to inconsistent prognoses of small breast neoplasms (T1a/T1b). This study was done to conduct a retrospective analysis and establish a clinical prediction model to predict individual survival outcomes of patients with small carcinomas of the breast.

MATERIALS AND METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, eligible patients with small breast carcinomas were analyzed. Univariate analysis and multivariate analysis were performed to clarify the indicators of overall survival. Pooling risk factors enabled nomograms to be constructed and further predicted 3-year, 5-year, and 10-year survival of patients with small breast cancer. The model was internally validated for discrimination and calibration.

RESULTS

A total of 17,543 patients with small breast neoplasms diagnosed between 2013 and 2016 were enrolled. Histologic grade, lymph node stage, estrogen receptor or progesterone receptor status, and molecular subtypes of breast cancer were regarded as the risk factors of prognosis in a Cox proportional hazards model (P < .05). A nomogram was constructed to give predictive accuracy toward individual survival rate of patients with small breast neoplasms.

CONCLUSIONS

This prognostic model provided a robust and effective method to predict the prognosis of patients with small breast cancer.