Prognostic nomogram for nonresectable pancreatic cancer treated with gemcitabine-based chemotherapy

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BACKGROUND

The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.

METHODS

We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan-Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.

RESULTS

Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675-0.690), 0.689 (95% CI: 0.677-0.701), and 0.823 (95% CI: 0.786-0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.

CONCLUSION

The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Impact of Coronavirus Disease 2019 on the Diagnosis and Treatment of Pancreatic Cancer: An Observational Cohort Study

Early pancreatic cancer diagnosis is crucial for timely intervention and better outcomes. The coronavirus disease 2019 (COVID-19) pandemic has disrupted routine health care globally. The COVID-19 pandemic has disrupted routine health care globally. This study aimed to evaluate the impact of COVID-19 on the diagnosis and treatment of pancreatic cancer. This retrospective study compared pancreatic cancer patients from 2 tertiary hospitals in pre and COVID-19 periods. Pre-COVID-19 period spanned from January 1, 2018, to January 19, 2020, while the COVID-19 period extended from January 20, 2020, to December 31, 2021. Results: A total of 542 patients were included. In the pre-COVID-19 period, 280 new cases of pancreatic cancer were enrolled, compared to 262 during COVID-19. The annual incidence rates were 136.63 and 134.50 patients, respectively. The median age was significantly lower during COVID-19 (71.5 years) compared to pre-COVID-19 (77 years) (P < .001). In subgroup analyses for stage 3 and 4, the proportion of stage 4 pancreatic cancer increased during COVID-19 (χ2 = 5.53, P = .019), and the COVID-19 group had younger diagnoses, better performance status, more surgery, higher use of FOLFIRINOX chemotherapy, fewer hospital referrals, and better median overall survival compared to the pre-COVID-19 group. This study revealed changes in the characteristics and treatment of patients diagnosed with pancreatic cancer during the COVID-19 pandemic. Although further large-scale research is necessary, the findings of this study can function as foundational data for formulating policies for the management of patients with pancreatic cancer during future pandemics of other infectious diseases.

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research

Background

This study aimed to develop a prognostic model for patients with advanced ductal adenocarcinoma aged ≥50 years.

Methods

Patient information was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to screen the model variables. Cases from Nanchang Central Hospital were collected for external validation. The new nomogram and the American Joint Committee on Cancer (AJCC) criteria were evaluated using integrated discrimination improvement (IDI) and net reclassification index (NRI) indicators. Survival curves presented the prognosis of the new classification system and AJCC criteria.

Results

In total, 17,621 eligible patients were included. Lasso Cox regression selected 4 variables including age, chemotherapy, radiotherapy and AJCC stage. The C-index of the training cohort was 0.721. The C-index value of the validation cohort was 0.729. The AUCs for the training cohorts at 1, 2, and 3 years were 0.749, 0.729, and 0.715, respectively. The calibration curves showed that the predicted and actual probabilities at 1, 2, and 3 years matched. External validation confirmed the model's outstanding predictive power. Decision curve analysis indicated that the clinical benefit of the nomogram was higher than that of the AJCC staging system. The model evaluation indices preceded the AJCC staging with NRI (1-year: 0.88, 2-year: 0.94, 3-year: 0.72) and IDI (1-year: 0.24, 2-year: 0.23, 3-year: 0.22). The Kaplan-Meier curves implied that the new classification system was more capable of distinguishing between patients at different risks.

Conclusions

This study established a prognostic nomogram and risk classification system for advanced pancreatic cancer in patients aged ≥50 years to provide a practical tool for the clinical management of patients with pancreatic ductal adenocarcinoma.

Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma

Objective

To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).

Methods

We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively.

Results

The random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828-0.956) and 0.824 (95% CI: 0.677-0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group.

Conclusion

The radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities.

Prognostic Nomogram for Lymph-Node Metastasis in Oral Squamous Cell Carcinoma (OSCC) Using Immunohistochemical Marker D2-40

Introduction

Nomograms are proven in "individualized risk prediction" in sarcomas and breast and prostate cancers. Incorporating immunohistochemical markers and histopathological parameters can enhance accuracy of these graphical representations of statistical predictive models concerning metastasis. D2-40, a monoclonal antibody to podoplanin (regulator of motility expressed in malignant epithelial cells), dually predicts metastatic potential of tumour by estimating the motile tumour phenotype and by detecting lymphatic vessels/density, both essential to metastasis in OSCC. Thus, we propose a model that incorporates D2-40 immunostaining of individual tumour cells (ITC) too with other variables (seen in H+E staining) as a predictive nomogram.

Methods

Sixty cases of OSCC were selected with equal number of cases (n=30) of pN0 and pN+ status. Bryne's grading of invasive front of tumour (ITF) was done on H+E-stained slides followed by D2-40 immunostaining for ITCs at ITF and lymphatic vessels. Multivariate regression analysis was used to generate the nomogram of LNM where the predictive contribution of each covariate, namely depth of invasion, D2-40-stained ITCs, gender, histological grade, and worst pattern of invasion (WPOI), was plotted on a scale of 1-100 points.

Results

The nomogram showed that the strongest variable in OSCC was the WPOI in H+E-stained section followed by D2-40-positive ITCs and gender.

Discussion

Our predictive nomogram for LNM in OSCC surprisingly showed that a tumour with lower score of WPOI (islands vs ITC) showed numerous D2-40-positive ITCs, drastically increasing the probability of metastasis. The concept of "individualized risk prediction" can be used to predict lymph node metastasis using a variety of histopathological criteria that can be visualized in routine and immunohistochemical staining in OSCC with the aid of a nomogram.

Development of a Clinical-Biological Model to Assess Tumor Progression in Metastatic Pancreatic Cancer: Post Hoc Analysis of the PRODIGE4/ACCORD11 Trial

Background: The follow-up of pancreatic cancer (PC) is based on computed tomography (CT) assessment; however, there is no consensus on the use of clinical and biological criteria in tumor progression. We aimed to establish a clinical−biological model to highlight the progression of metastatic PC during first-line treatment. Methods: The patients treated with first-line chemotherapy in the phase 2/3 PRODIGE4/ACCORD11 clinical trial were evaluated retrospectively. Clinical and biological markers were evaluated at the time of CT scans and during treatment to determine tumor progression. Results: In total, 196 patients were analyzed, with 355 available tumor assessments. The clinical and biological factors associated with tumor progression in multivariate analysis included gemcitabine, global health status ≤ 33 (OR = 3.38, 95%CI [1.15; 9.91], p = 0.028), quality of life score between 34 and 66 (OR = 2.65, 95%CI [1.06; 6.59], p = 0.037), carcinoembryonic antigen (CEA) ≥ 3 times the standard value without any increase in the CEA level from inclusion (OR = 2.22, 95%CI [1.01; 4.89], p = 0.048) and with an increase in the CEA level from inclusion (OR = 6.56, 95%CI [2.73; 15.78], p < 0.001), and an increase in the carbohydrate antigen 19-9 level from inclusion (OR = 2.59, 95%CI [1.25; 5.36], p = 0.016). Conclusions: The self-assessment of patients’ general health status alongside tumor markers is an interesting approach to the diagnosis of the tumor progression of metastatic pancreatic cancer patients during first-line treatment.

Multimodal survival prediction in advanced pancreatic cancer using machine learning

Background

Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care.

Methods

In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups.

Results

Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59).

Conclusions

The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

•

We developed a machine-learning-based prediction model that outperforms the AJCC staging system and mGPS.

•

Applying our model to an external validation cohort demonstrates generalizability.

•

Explainable machine learning enables to understand the decision making of our model and identifies relevant parameters.

•

Combining clinical, imaging and genetic data holds potential for personalized prognostication in advanced PDAC.

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.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Background

Pancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.

Methods

Patients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016-2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).

Results

Age, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715-0.735) and 0.711 (95%CI: 0.695-0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778-0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.

Conclusions

A web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

Development of a prognostic nomogram for metastatic pancreatic ductal adenocarcinoma integrating marital status

Previous studies have shown that marital status can affect the overall survival (OS) of cancer patients yet its role in metastatic pancreatic ductal adenocarcinoma (mPDAC) remains unclear. This study aimed to explore the impact of marital status on the OS of mPDAC patients and to construct a prognostic nomogram to predict OS outcomes. Data from patients diagnosed with mPDAC were obtained from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. The patients were randomized into primary and validation cohorts. Kaplan-Meier survival analysis was performed to compare differences in survival depending on marital status. Univariate and multivariate analyses were conducted to identify independent prognostic factors and a nomogram was established based using Cox regression analyses. Validation of the prognostic nomogram was evaluated with a calibration curve and concordance index (C-index). Our data showed significant differences in the OS of mPDAC patients with different marital status by Kaplan-Meier analysis (P < 0.05). Univariate and multivariate analyses confirmed that marital status was an independent OS-related factor in mPDAC patients. Based on the multivariate models of the primary cohort, a nomogram was developed that combined marital status, age, grade, tumor size, surgery of primary site, surgery of lymph node and metastatic. The nomogram showed that marital status had a moderate influence on predicting the OS of mPDAC patients. Moreover, the internally and externally validated C-indexes were 0.633 and 0.619, respectively. A calibration curve confirmed favorable consistency between the observed and predicted outcomes. Marital status was identified as an independent prognostic factor for OS of mPDAC patients and is a reliable and valid parameter to predict the survival of patients with mPDAC. This prognostic model has value and may be integrated as a tool to inform decision-making in the clinic.

Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

PURPOSE

To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA).

RESULTS

The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68-0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69-0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful.

CONCLUSIONS

The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

Real-world prognostic factors for survival among treated patients with metastatic pancreatic ductal adenocarcinoma

Background

Many real‐world studies of patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) are restricted to single centers, limiting the generalizability of their insights. This study aimed to identify important population‐based predictors for survival in patients diagnosed with mPDAC in a broader setting.

Methods

Data between 1 January 2017 and 31 December 2019 were extracted from the Flatiron Health EHR database. Treatment‐specific predictive models were generated for patients treated with first‐line gemcitabine+nab­paclitaxel (GNP), FOLFIRINOX, gemcitabine monotherapy (gem‐mono), and second‐line liposomal irinotecan‐based regimens. The holdout method was used for cross‐validation. Age at diagnosis, sex, BMI, smoking status, and ECOG performance score were included in all models with additional demographic, clinical characteristics, and hematological function assessed for inclusion.

Results

Of the 3625 patients, 43% received GNP, 26% received FOLFIRINOX, 7% received gem‐mono, and 23% received other regimens; 40% (n = 1448) advanced to the second line. Among all first‐line patients, the following were included in the final model: prior surgery, white blood cell (WBC) counts, serum albumin (SA), liver function tests (LFTs), serum bilirubin, serum carbohydrate antigen 19–9, and ascites. Models for patients receiving specific therapies differed from the overall model, GNP (ascites removed), FOLFIRINOX (stage at initial diagnosis added), and gem‐mono (LFTs omitted). Alkaline phosphatase (ALP), SA, and WBC counts were important predictors of survival among patients treated with second‐line liposomal irinotecan. Across all regimens, the strongest predictors of survival were ECOG score, SA, and ALP.

Conclusions

In this real‐world study of patients with mPDAC, important population prognostic factors of survival were identified in a large cohort of patients receiving systemic treatment.

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer

Simple Summary

Finding prognostic biomarkers and associated models with high accuracy in patients with pancreatic cancer remains a challenge. The aim of this study was to analyze whether the combination of quantitative imaging biomarkers based on geometric and radiomics analysis of whole liver tumor burden and established clinical parameters improves the prediction of survival in patients with metastatic pancreatic cancer. In this retrospective study a total of 75 patients with pancreatic cancer and liver metastases were analyzed. Segmentations of whole liver tumor burden from baseline contrast-enhanced CT images were used to derive different quantitative imaging biomarkers. For comparison, we chose two clinical prognostic models from the literature. We found that a combined clinical and imaging-based model has a significantly higher predictive performance to discriminate survival than the underlying clinical models alone (p < 0.003).

Abstract

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p < 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone.

Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer

BACKGROUND

Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC.

METHODS

A total of 323 patients with advanced and metastatic PC were identified from the Chinese People's Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively.

RESULTS

In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19-9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19-9, baseline CEA, change in CA 19-9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62-0.71) and 0.74 (95% CI, 0.69-0.77), respectively.

CONCLUSION

These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

Background

The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.

Methods

The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation.

Results

A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively.

Conclusions

In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08097-w.

Simple prognostic markers for optimal treatment of patients with unresectable pancreatic cancer

Most patients with pancreatic cancer are ineligible for curative resection at diagnosis, resulting in poor prognosis. This study aimed to evaluate the prognostic factors in patients with unresectable pancreatic cancer.We retrospectively collected clinical data from 196 patients with unresectable pancreatic cancer who received palliative chemotherapy (N = 153) or palliative care alone (N = 43) from January 2011 to December 2013. Patients' background data and overall survival were analyzed using the Cox proportional hazard regression model.In patients receiving palliative chemotherapy (gemcitabine-based regimen, 88.2%) and palliative care alone, the median (range) ages were 68 (43-91) and 78 (53-90) years, and metastatic diseases were present in 80% (N = 123) and 86% (N = 37), respectively. Multivariate analysis in the palliative chemotherapy patients showed that liver metastasis (hazard ratio [HR] 2.25, 95% confidence interval [CI] 1.58-3.20, P < .001), neutrophil-to-lymphocyte ratio (>4.5 vs ≤4.5; HR 3.45, 95% CI 2.22-5.36, P < .001), and cancer antigen 19-9 (CA19-9) (≥900 vs <900 U/mL; HR 1.45, 95% CI 1.02-2.05, P = .036) were independent prognostic factors. In those receiving palliative care alone, lung (HR 3.27, 95% Cl 1.46-7.35, p = 0.004) and peritoneum (HR 2.50, 95% CI 1.20-5.18, P = .014) metastases and the C-reactive protein-to-albumin ratio (≥1.3 vs <1.3; HR 3.33, 95% Cl 1.51-7.35, P = .003) were independent prognostic factors. Furthermore, patients with multiple factors had worse prognosis in both groups. Median survival time of palliative chemotherapy patients with risk factors 0, 1, 2, and 3 were 13.1 (95% CI 8.0-16.9), 9.4 (95% CI 7.9-10.1), 6.6 (95% CI 4.9-7.8), and 2.5 (95% CI 1.7-4.0) months, respectively. Similarly, median survival time was 5.7 (95% CI 1.3 -8.0), 2.1 (95% CI 1.5-3.9), and 1.3 (95% CI 0.6-1.7) months, respectively, for palliative care alone patients with risk factor 0, 1, and 2 to 3.Prognostic markers for pancreatic cancer were neutrophil-to-lymphocyte ratio, liver metastasis, and CA19-9 in patients undergoing palliative chemotherapy and C-reactive protein-to-albumin ratio and lung/peritoneum metastases in patients undergoing palliative care alone. These simple markers should be considered when explaining the prognosis and therapeutic options to patients.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data

BACKGROUND

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data.

MATERIALS AND METHODS

Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration.

RESULTS

Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively.

CONCLUSIONS

A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

Development and Validation of Multicenter Predictive Nomograms for Locally Advanced Pancreatic Cancer After Chemoradiotherapy

OBJECTIVE

Due to common practice of hypofractionated radiotherapy in pancreatic cancer and heterogeneous chemotherapy regimens in previous studies, modified nomograms are required. Therefore, we aim to develop and validate prognostic nomograms for locally advanced pancreatic cancer (LAPC) after stereotactic body radiation therapy (SBRT) and chemotherapy.

METHODS

The development cohort comprised 925 patients with LAPC receiving SBRT and gemcitabine-based chemotherapy in our center, while 297 patients from another two centers formed the validation cohort. Nomograms were created from COX models and internally validated by bootstrap. Model discriminations were evaluated by calibration plots and concordance index (C-index). A decision curve analysis (DCA) was performed to evaluate clinical benefits of nomograms. Additionally, recursive partitioning analysis (RPA) was used for stratifications of survival probability based on the total score of each patient calculated by nomograms.

RESULTS

Weight loss, tumor diameter, radiation dose, CA19-9 kinetics after treatment and surgical resection were included in the nomogram for overall survival (OS), while the five factors plus performance status formed the nomogram for progression free survival (PFS). The corrected C-indexes for estimated 1-year and 2-year OS of the development cohort were 0.88 (95% CI: 0.85-0.91) and 0.86 (95% CI: 0.83-0.90). For those of the validation cohort, it was 0.88 (95% CI: 0.82-0.94) and 0.83 (95% CI: 0.74-0.91). Additionally, the corrected C-index for predicted 1-year PFS in the development and validation cohort was 0.83 (95% CI: 0.81-0.86) and 0.82 (95% CI: 0.78-0.87), respectively. The calibration plots showed good agreement of 1- and 2-year OS and 1-year PFS between the estimations and actual observations. Potential clinical benefits were demonstrated with DCA. Additionally, for 1- and 2-year OS and 1-year PFS, patients were stratified into four groups with different survival probability by RPA.

CONCLUSION

The validated nomograms provided useful predictions of OS and PFS for LAPC with chemoradiotherapy.

Prospective Non-Randomized Study of Intraoperative Assessment of Surgical Resection Margin of Colo-Rectal Liver Metastases

Introduction: More than 50% of patients with colorectal cancer (CRC) develop liver metastases during the natural course of disease. Surgical resection is currently the most potentially curative method in the treatment of colorectal liver metastases (CRLM). The goal of surgery is to achieve a negative resection margin (RM) of at least 1 mm, which provides the best prognosis for patients. The RM can be assessed by the pathologist of the resected liver specimen (RLS) and by the surgeon intraoperatively. The aim of this research paper is to determine the degree of agreement on intraoperative assessment of the RM by the surgeon and histopathological RM assessment by the pathologist.

Material and methods: This prospective non-randomized double-blind study was approved by the Ethics Committee of the Oncology Institute of Vojvodina and registered on ClinicalTrials.gov #NCT04634526. The study was conducted at the Oncology Institute of Vojvodina, Sremska Kamenica, Serbia. An experienced hepatobiliary surgeon assessed RM for every specimen intra-operatively, immediately after CRLM resection. Resected CRLM lesions were analyzed by two experienced pathologists. These data were compared with pathological RM assessment as a “gold standard”. RM of 1 mm or more was rated as negative RM (RM-). Disease-free survival (DFS) and recurrence rate was calculated by RM status defined by surgeon and by pathologist.

Results: From 01 January 2015 to 31 August 2019, 98 patients were enrolled in the study. There were 219 RLS with 245 CRLM. The surgeon registered positive RM (RM+) of <1mm in 41 (18.7%) RLS. Taking the result of the histopathological assessment (HPA) as the “gold standard”, it was determined that RM was true positive in 32 (14.6%) cases. False positive RM was found in 9 (4.1%) cases. False negative RM was found in 20 (9.1%) cases. True negative RM was found in 158 (72.2%) cases. Sensitivity of surgical assessment (SA) of RM+ was 61.5% (32/52). Specificity of SA of RM+ was 94.6% (158/167). The positive predictive value (PPV) was 78.0% (32/41), while the negative predictive value (NPV) was 88.8% (158/178). The overall accuracy of the RM+ SA was 86.8% (190/219). There was no statistically significant difference in the assessment of RM+ per RLS by surgeon and pathologists (p=0.061), but it was significant when analyses per patients was performed (p=0.017). Recurrence rate for RM+ patients was 48.1% (13/27, p=0.05) for SA and 35.0% (14/40, p=0.17) for HPA. Three year DFS for RM- and RM+ was 66.5% and 27.9% (p=0.04), respectively, by SA, and 64.8% and 42.1% (p=0.106), respectively, by HPA.

Conclusion: Intraoperative assessment of RM- by surgeon of RLS is clinically meaningful. There is not a statistically significant difference in the assessment of RM+ by surgeon and pathologists per RLS, but it was statically significant on a per patient basis. RM determined by surgeon has better prognostic impact on recurrence rate and 1- and 3-year DFS than standard histopathological assessment.

A Simple-to-Use Nomogram for Predicting Survival in Children with Acute Myeloid Leukemia

Background

The research analyzed a group of patients to develop a statistical nomogram and a web-based survival rate predictor for the comprehensive estimate of the overall survival (OS) of children with acute myeloid leukemia.

Methods

Between 1999 to 2015, we used the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database to evaluate and randomly divide 440 children diagnosed with AML into the population of training (n = 309) and validation (n = 131). The analysis of Lasso Cox was used to identify separate predictive variables. We have used essential forecasting considerations to construct a nomogram and a web-based calculator focused on Cox regression analysis. Nomogram validation was tested through discrimination and calibration.

Results

Compared to the multivariate training cohort models, a nomogram integrating gender, age of diagnose, WBC at diagnosis, bone marrow leukemic blast percentage, and chromosomal abnormalities [t(8; 21), inv(16)] were designed for the prediction of OS. We also developed a predictive survival nomogram and a web-based calculator. C-indexes validated internally and checked externally were 0.747 and 0.716. The calibration curves have shown that the nomogram might accurately forecast 3-year and 5-year OS.

Conclusions

A nomogram effectively predicts survival in children with AML. This prognostic model can be used in clinical practice.

Antireflux metal stent for biliary obstruction: Any benefits?

Endoscopic retrograde cholangiopancreatography with stent placement has been utilized as standard palliative management of distal malignant biliary obstruction (MBO). Compared to plastic stents, metal stents can provide longer-term relief of symptoms. When a large-bore metal stent is placed across the ampulla, patients are predisposed to the risk of cholangitis or stent dysfunction due to reflux of duodenal contents. To mitigate the risk of adverse events associated with the duodenobiliary reflux, efforts have been directed to development of antireflux metal stents (ARMSs). The antireflux property has been introduced through adding of an antireflux valve to the duodenal stent end. Evidence from clinical studies indicates that ARMSs may not only reduce the risk of ascending cholangitis during follow-up but also prolong stent patency time. However, the results of clinical studies testing ARMSs are inconsistent owing to heterogeneous designs of antireflux valves and stent bodies. Metal stents are increasingly indicated for benign biliary strictures and MBO in the setting of neoadjuvant chemotherapy, and therefore, research is warranted to evaluate ARMSs for those indications. Given that endoscopic ultrasound (EUS)-guided transmural biliary drainage has gained popularity, the optimal timing of placing an ARMS in relation to EUS-guided and percutaneous drainage should be investigated. Development and evaluation of ARMSs require an integrative approach utilizing phantom and animal models, measurements of stent mechanical properties, and in vivo functional study after stent placement. In this review article, we summarize updated evidence on ARMSs for MBO and discuss issues that should be addressed in future studies.

Recent Discoveries of Diagnostic, Prognostic and Predictive Biomarkers for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.

A retrospective comparative study of S-IROX and modified FOLFIRINOX for patients with advanced pancreatic cancer refractory to gemcitabine plus nab-paclitaxel

PURPOSE

The aim of this study was to evaluate the efficacy and tolerability of S-IROX and modified FOLFIRINOX (mFFX) after gemcitabine plus nab-paclitaxel for advanced pancreatic cancer (PC) in the real world setting.

METHODS

Consecutive patients receiving S-IROX or mFFX as a second-line chemotherapy for advanced PC refractory to gemcitabine plus nab-paclitaxel were retrospectively studied. Patients were treated every 2 weeks: S-1 40 mg/m² was administered orally twice daily on days 1 to 7 in S-IROX and 5-fluorouracil 2400 mg/m² was intravenously administered for 46 h without bolus infusion in mFFX, in addition to intravenous oxaliplatin 85 mg/m² and irinotecan 150 mg/m² on day 1 in both regimens.

RESULTS

Fifty-four patients with advanced PC who received S-IROX (n = 19) or mFFX (n = 35) were retrospectively studied. The disease control rate and response rate were 73.7% and 10.5% in the S-IROX group and 62.2% and 2.7% in the mFFX group, respectively. The median progression free survival (PFS) was 7.8 and 5.7 months in the S-IROX and mFFX groups (p = 0.24). The median overall survival (OS) was 14.2 and 11.5 months in the S-IROX and mFFX groups (p = 0.34). There were no significant differences in the incidences of grade 3-4 adverse effects. The subgroup analyses suggested S-IROX demonstrated favorable OS in patients with PFS ≥6 months of first-line gemcitabine plus nab-paclitaxel (p for interaction = 0.02).

CONCLUSIONS

S-IROX and mFFX were similarly tolerable and effective as a second-line chemotherapy in patients with PC refractory to gemcitabine plus nab-paclitaxel.

Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer

PURPOSE

The diagnostic value of nomogram in pancreatic cancer (PC) with liver metastasis (PCLM) is still largely unknown. We sought to develop and validate a novel nomogram for the prediction of liver metastasis in patients with PC.

METHOD

About 604 pathologically confirmed PC patients from the Sun Yat-sen University Cancer Center (SYSUCC) between July, 2001 and December, 2013 were retrospectively studied. The SYSUCC cohort was randomly assigned to as the training set and internal validation set. Using these two sets, we derived and validated a prognostic model by using concordance index and calibration curves. Another two independent cohorts between August, 2002 and December, 2013 from the Sun Yat-sen Memorial Hospital (SYSMH, n = 335) and Guangdong General Hospital (GDGH, n = 503) was used for external validation.

RESULT

Computed tomography (CT) reported liver metastasis status, carcinoembryonic antigen (CEA) level and differentiation type were identified as risk factors for PCLM in the training set. The final diagnostic model demonstrated good calibration and discrimination with a concordance index of 0.97 and had a robust internal validation. The score ability to diagnose PCLM was further externally validated in SYSMH and GDGH with a concordance index of 0.93. The model showed better calibration and discrimination than CT, CEA and differentiation in each cohort.

CONCLUSION

Based on a large multi-institution database and on the routinely observed CT-reported status, CEA level and tumor differentiation in clinical practice, we developed and validated a novel nomogram to predict PLCM.

Score for the Overall Survival Probability of Patients With Pancreatic Adenocarcinoma of the Body and Tail After Surgery: A Novel Nomogram-Based Risk Assessment

Pancreatic adenocarcinoma of the body and tail often has a dismal prognosis and lacks a specific prognostic stage. The aim of this study was to construct a nomogram for predicting survival of patients with pancreatic adenocarcinoma of the body and tail after surgery. Data of patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database and from medical records of Sun Yat-sen University Cancer Center (SYSUCC). In a multivariate analysis for overall survival (OS), the following six variables were identified as independent predictors and incorporated into the nomogram: age, tumor differentiation, tumor size, lymph node ratio (LNR), and chemotherapy. A nomogram was built based on independent risk predictors. The concordance index (C-index) for nomogram, Tumor-Node-Metastasis (TNM) 7th and 8th stage system were 0.775 [95% confidence interval (CI), 0.731–0.819], 0.617 (95%CI, 0.575–0.659), and 0.632 (95%CI, 0.588–0.676), respectively. The calibrated nomogram predicted survival rates which closely corresponded to the actual survival rates. Furthermore, the values of the area under receiver operating characteristic (ROC) curves (AUC) of the nomograms were higher than those of the TNM 7th or 8th stage system in predicting 1-, 2-, and 3-year survival of patients in training and external validation cohorts. The well-calibrated nomogram could be used to predict prognosis for patients with pancreatic adenocarcinoma of the body and tail after surgery.

Prognostic and predictive factors in pancreatic cancer

Pancreatic cancer is one of the leading causes of cancer death worldwide. Its high mortality rate has remained unchanged for years. Radiotherapy and surgery are considered standard treatments in early and locally advanced stages. Chemotherapy is the only option for metastatic patients. Two treatment regimens, i. e. the association of 5-fluorouracil- irinotecan-oxaliplatin (FOLFIRINOX) and the association of nab-paclitaxel with gemcitabine, have been shown to improve outcomes for metastatic pancreatic adenocarcinoma patients. However, there are not standardized predictive biomarkers able to identify patients who benefit most from treatments. CA19-9 is the most studied prognostic biomarker, its predictive role remains unclear. Other clinical, histological and molecular biomarkers are emerging in prognostic and predictive settings. The aim of this review is to provide an overview of prognostic and predictive markers used in clinical practice and to explore the most promising fields of research in terms of treatment selection and tailored therapy in pancreatic cancer.

Long-term Risk of Malignancy in Branch-Duct Intraductal Papillary Mucinous Neoplasms

BACKGROUND & AIMS

Long-term outcomes of patients with branch-duct intraductal papillary mucinous neoplasms (IPMNs), particularly those after 5 years of surveillance, have not been fully evaluated in large studies. We analyzed incidences of IPMN-derived carcinoma and concomitant ductal adenocarcinoma (pancreatic ductal adenocarcinoma [PDAC]) over 20 years in a large population of patients.

METHODS

We identified 1404 consecutive patients (52% women; mean age, 67.5 years) with a diagnosis of branch-duct IPMN, from 1994 through 2017, at the University of Tokyo in Japan. Using a competing risk analysis, we estimated cumulative incidence of pancreatic carcinoma, overall and by carcinoma type. We used competing risks proportional hazards models to estimate subdistribution hazard ratios (SHRs) for incidences of carcinomas. To differentiate IPMN-derived and concomitant carcinomas, we collected genomic DNA from available paired samples of IPMNs and carcinomas and detected mutations in GNAS and KRAS by polymerase chain reaction and pyrosequencing.

RESULTS

During 9231 person-years of follow-up, we identified 68 patients with pancreatic carcinomas (38 patients with IPMN-derived carcinomas and 30 patients with concomitant PDACs); the overall incidence rates were 3.3%, 6.6%, and 15.0% at 5, 10, and 15 years, respectively. Among 804 patients followed more than 5 years, overall cumulative incidence rates of pancreatic carcinoma were 3.5% at 10 years and 12.0% at 15 years from the initial diagnosis. The size of the IPMN and the diameter of the main pancreatic duct associated with incidence of IPMN-derived carcinoma (SHR 1.85; 95% confidence interval 1.38-2.48 for a 10-mm increase in the IPMN size and SHR 1.56; 95% confidence interval 1.33-1.83 for a 1-mm increase in the main pancreatic duct diameter) but not with incidence of concomitant PDAC.

CONCLUSIONS

In a large long-term study of patients with branch-duct IPMNs, we found the 5-year incidence rate of pancreatic malignancy to be 3.3%, reaching 15.0% at 15 years after IPMN diagnosis. We observed heterogeneous risk factor profiles between IPMN-derived and concomitant carcinomas.

External validation of the Besançon nomogram in Asian patients with advanced pancreatic cancer receiving second-line chemotherapy: A multi-institute experience in Taiwan

BACKGROUND

Determining survival outcome in advanced pancreatic ductal adenocarcinoma (aPDAC) patients receiving second-line (L2) chemotherapy is important for clinical decision-making. The Besançon group from France recently proposed a prognostic nomogram to predict overall survival (OS) for aPDAC patients receiving L2 chemotherapy. The present study aimed to externally validate the performance of the Besançon nomogram in predicting OS in an Asian cohort.

METHODS

We retrospectively enrolled 349 patients who received L2 chemotherapy for aPDAC between 2010 and 2016 at four institutes in Taiwan. The performance of the Besançon model in this cohort was evaluated with C-index and calibration plots.

RESULTS

The median OS time in our patient cohort was 4.5 months (95% confidence interval [CI], 3.0-5.0). Using the Besançon nomogram-predicted risk groups, the median OS times in the low, intermediate, and high-risk groups were 6.7 (95% CI, 5.3-8.2), 3.2 (95% CI, 2.4-3.9), and 1.7 months (95% CI, 0.6-2.7), respectively. The C-index of the predicted six- and 12-month survival probabilities for the Besançon nomogram were 0.766 (95% CI, 0.715-0.816) and 0.698 (95% CI, 0.641-0.754), respectively. The calibration plot showed that the observed six-month survival probability was close to the diagonal line, while that for 12-month survival deviated below the diagonal line compared to the survival probability predicted by the Besançon nomogram.

CONCLUSIONS

Although the Besançon nomogram tended to over-estimate the 12-month survival probability, our study demonstrated that the nomogram is a reliable and readily applicable model to estimate survival outcomes of aPDAC patients receiving L2 chemotherapy.

A Systematic Inflammation-based Model in Advanced Pancreatic Ductal Adenocarcinoma

Emerging evidence revealed the critical role of systematic inflammation in pancreatic ductal adenocarcinoma (PDAC). In the present study, we reviewed the records of 279 patients with advanced PDAC. Among them, 147 cases were used as the training cohort and another 132 as the validation cohort. In the training cohort, distant metastasis, carbohydrate antigen 19-9 (CA19-9), Glasgow prognostic score (GPS), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) were independent prognostic factors in Cox regression. A nomogram based on these factors was generated to predict median survival time and survival probabilities at 6, 12, and 18 months. The nomogram showed a better discriminatory ability than the American Joint Committee on Cancer (AJCC) TNM staging (C-index: 0.727 vs. 0.610). In the validation cohort, a nomogram composed of the same variables also showed a high discriminatory ability (C-index: 0.784). In the low-risk group with a nomogram total point (NTP) value of more than 175, patients receiving combination therapy showed better prognosis than those receiving monotherapy (P=0.015). In conclusion, the nomogram based on inflammatory biomarkers can serve as useful prognostic tool for advanced PDAC. In addition, patients with high NTP can greater benefit from combination chemotherapy than monotherapy.

Major Complications Independently Increase Long-Term Mortality After Pancreatoduodenectomy for Cancer

BACKGROUND

Postoperative major morbidity has been associated with worse survival gastrointestinal tumors. This association remains controversial in pancreatic cancer (PC). We analyzed whether major complications after surgical resection affect long-term survival.

METHODS

Records of all PC patients resected from 2007 to 2015 were reviewed. Major morbidity was defined as any grade-3 or higher 30-day complications, per the Clavien-Dindo Classification. Patients who died within 90 days after surgery were excluded from survival analysis.

RESULTS

Of 616 patients, 81.7% underwent pancreatoduodenectomy (PD) and 18.3% distal pancreatectomy (DP). Major complications occurred in 19.1% after PD and 15.9% after DP. In patients who survived > 90 days, the likelihood of receiving adjuvant treatment was 43.9% if major complications had occurred, vs. 68.5% if not (p < 0.001), and those who received it started the treatment median 10 days later compared with uncomplicated patients (median 60 days (50-72) vs. 50 days (41-61), p = 0.001). By univariate analysis, in addition to the conventional pathology-related prognostic determinants and the receipt of adjuvant treatment, major complications worsened long-term survival after PD (median OS 26 months vs. 15, p = 0.008). A difference was also seen after DP, but it did not reach statistical significance, likely related to the small sample size (median OS 33 months vs. 18, p = 0.189). At multivariate analysis for PD, major postoperative complications remained independently associated with worse survival [HR 1.37, 95%CI (1.01-1.86)].

CONCLUSIONS

Major surgical complications after pancreaticoduodenectomy are associated with worse long-term survival in pancreatic cancer. This effect is independent of the receipt of adjuvant treatment.

Validation and application of a prognostic model for patients with advanced pancreatic cancer receiving palliative chemotherapy

Background

We previously developed a robust prognostic model (GS model) to predict the survival outcome of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy with gemcitabine plus S‐1 (GS). This study aimed to validate the application of the GS model in APC patients receiving chemotherapy other than the GS regimen.

Patients and methods

We retrospectively analyzed 727 APC patients who received first‐line palliative chemotherapy other than the GS regimen between 2010 and 2016 at four institutions in Taiwan. The patients were categorized into three prognostic groups based on the GS model for comparisons of survival outcome, best tumor response, and in‐group survival differences with monotherapy or combination therapy.

Results

The median survival times for the good, intermediate, and poor prognostic groups were 13.4, 8.4, and 4.6 months, respectively. The hazard ratios for the comparisons of intermediate and poor to good prognostic groups were 1.51 (95% confidence interval [CI]), 1.22‐1.88, P < .001) and 2.84 (95% CI, 2.34‐3.45, P < .001). The best tumor responses with either partial response or stable disease were 57.5%, 40.4%, and 17.2% of patients in the good, intermediate, and poor prognostic groups (P < .001), respectively. For patients in the good prognostic group, first‐line chemotherapy with monotherapy and combination therapy had similar median survival times (13.8 vs 12.9 months, P = .26), while combination therapy showed a better median survival time than monotherapy in patients in the intermediate and poor prognostic groups (8.5 vs 8.0 months, P = .038 and 5.7 vs 3.7 months, P = .001, respectively).

Conclusion

The results of our study supported the application of the GS model as a general prognostic tool for patients with pancreatic cancer receiving first‐line palliative chemotherapy with gemcitabine‐based regimens.

Nomogram for Predicting Survival in Patients Treated with Liposomal Irinotecan Plus Fluorouracil and Leucovorin in Metastatic Pancreatic Cancer

NAPOLI-1 (NCT01494506) was a phase III study of liposomal irinotecan (nal-IRI) plus 5-fluorouracil/leucovorin (5-FU/LV) in patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) previously treated with gemcitabine-based therapy. This post hoc analysis of NAPOLI-1 aimed to develop a predictive nomogram for overall survival (OS) at 6 and 12 months. Analyses were derived from all patients in NAPOLI-1 randomized to receive nal-IRI+5-FU/LV, nal-IRI monotherapy, or 5-FU/LV combination therapy. OS was associated with baseline factors using univariate and multivariable Cox analyses. A predictive nomogram was derived and validated using a concordance index and calibration plots. The univariate analyses identified 21 independent factors that contributed to OS, with eight factors significantly associated with OS. The Karnofsky Performance Score contributed the largest number of points (100), followed by presence of liver metastasis (98) and randomization to nal-IRI+5-FU/LV (96). The other baseline factors showing effects were albumin (g/dL), neutrophil/lymphocyte ratio, carbohydrate antigen 19-9 (U/mL), disease stage at diagnosis, and body mass index (kg/m²). The nomogram was used to predict the 6- and 12-month survival probability. The mean absolute errors between the observed and predicted probabilities for OS at 3, 6, and 9 months were 0.07, 0.08, and 0.07, respectively. This nomogram, based on NAPOLI-1, provides additional insight to aid decision-making for patients with mPDAC after previous gemcitabine-based therapy.

Consensus statement on mandatory measurements in pancreatic cancer trials (COMM-PACT) for systemic treatment of unresectable disease

Variations in the reporting of potentially confounding variables in studies investigating systemic treatments for unresectable pancreatic cancer pose challenges in drawing accurate comparisons between findings. In this Review, we establish the first international consensus on mandatory baseline and prognostic characteristics in future trials for the treatment of unresectable pancreatic cancer. We did a systematic literature search to find phase 3 trials investigating first-line systemic treatment for locally advanced or metastatic pancreatic cancer to identify baseline characteristics and prognostic variables. We created a structured overview showing the reporting frequencies of baseline characteristics and the prognostic relevance of identified variables. We used a modified Delphi panel of two rounds involving an international panel of 23 leading medical oncologists in the field of pancreatic cancer to develop a consensus on the various variables identified. In total, 39 randomised controlled trials that had data on 15 863 patients were included, of which 32 baseline characteristics and 26 prognostic characteristics were identified. After two consensus rounds, 23 baseline characteristics and 12 prognostic characteristics were designated as mandatory for future pancreatic cancer trials. The COnsensus statement on Mandatory Measurements in unresectable PAncreatic Cancer Trials (COMM-PACT) identifies a mandatory set of baseline and prognostic characteristics to allow adequate comparison of outcomes between pancreatic cancer studies.

Prognostic factors for survival with nab-paclitaxel plus gemcitabine in metastatic pancreatic cancer in real-life practice: the ANICE-PaC study

Background

Treatment with nab-paclitaxel plus gemcitabine increases survival in patients with metastatic pancreatic cancer. However, the assessment of treatment efficacy and safety in non-selected patients in a real-life setting may provide useful information to support decision-making processes in routine practice.

Methods

Retrospective, multicenter study including patients with metastatic pancreatic cancer, who started first-line treatment with nab-paclitaxel plus gemcitabine between December 2013 and June 2015 according to routine clinical practice. In addition to describing the treatment pattern, overall survival (OS) and progression-free survival (PFS) were assessed for the total sample and the exploratory subgroups based on the treatment and patients’ clinical characteristics.

Results

All 210 eligible patients had a median age of 65.0 years (range 37–81). Metastatic pancreatic adenocarcinoma was recurrent in 46 (21.9%) patients and de novo in 164 (78.1%); 38 (18%) patients had a biliary stent. At baseline, 33 (18.1%) patients had an ECOG performance status ≥2. Patients received a median of four cycles of treatment (range 1–21), with a median duration of 3.5 months; 137 (65.2%) patients had a dose reduction of nab-paclitaxel and/or gemcitabine during treatment, and 33 (17.2%) discontinued treatment due to toxicity. Relative dose intensity (RDI) for nab-paclitaxel, gemcitabine, and the combined treatment was 66.7%. Median OS was 7.2 months (95% CI 6.0–8.5), and median PFS was 5.0 months (95% CI 4.3–5.9); 50 patients achieved either a partial or complete response (ORR 24.6%). OS was influenced by baseline ECOG PS, NLR and CA 19.9, but not by age ≥ 70 years and/or the presence of hepatobiliary stent or RDI < 85%. All included variables, computed as dichotomous, showed a significant contribution to the Cox regression model to build a nomogram for predicting survival in these patients: baseline ECOG 0–1 vs. 2–3 (p = 0.030), baseline NLR > 3 vs. ≤ 3 (p = 0.043), and baseline CA 19.9 > 37 U/mL vs. ≤37 U/mL (p = 0.004).

Conclusions

Nab-Paclitaxel plus gemcitabine remain effective in a real-life setting, despite the high burden of dose reductions and poorer performance of these patients. A nomogram to predict survival using baseline ECOG performance status, NLR and CA 19.9 is proposed.

Nomogram to Predict Cancer-Specific Survival in Patients with Pancreatic Acinar Cell Carcinoma: A Competing Risk Analysis

Background: The objective of this study was to evaluate the probability of cancer-specific death of patients with acinar cell carcinoma (ACC) and build nomograms to predict overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with ACC between 2004 and 2014 were retrospectively collected. Cancer-specific mortality and competing risk mortality were evaluated. Nomograms for estimating 1-, 2- and 3-year OS and CSS were established based on Cox regression model and Fine and Grey's model. The precision of the 1-, 2- and 3-year survival of the nomograms was evaluated and compared using the area under receiver operating characteristic (ROC) curve (AUC). Results: The study cohort included 227 patients with ACC. The established nomograms were well calibrated, and had good discriminative ability, with a concordance index (C-index) of 0.742 for OS prediction and 0.766 for CSS prediction. The nomograms displayed better discrimination power than 7th or 8th edition Tumor-Node-Metastasis (TNM) stage systems in training set and validation set for predicting both OS and CSS. The AUC values of the nomogram predicting 1-, 2-, and 3-year OS rates were 0.784, 0.797 and 0.805, respectively, which were higher than those of 7th or 8th edition TNM stage systems. Regard to the prediction of CSS rates, the AUC values of the nomogram were also higher than those of 7th or 8th edition TNM stage systems. Conclusion: We evaluated the 1-, 2- and 3-year OS and CSS in patients with ACC for the first time. Our nomograms showed relatively good performance and could be considered as convenient individualized predictive tools for prognosis.

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

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 from 4,383 patients with PDAC of the head of the pancreas who underwent pancreaticoduodenectomy between 2004-2013 from 11 Registries Research Data of the Surveillance, Epidemiology,and End Results (SEER) database. Cox proportional hazards model was used to identify independent risk factors. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curve. The results were externally validated by comparison with data from 1,743 patients from 7 other Registries Research Data. RESULTS Of the 4,383 patients in the training dataset, median disease-specific survival (DSS) was 17.0 months (range, 1.0-131 months), and postoperative 1-year, 3-year, and 5-year DSS rates were 70.3%, 26.1%, and 16.8%, respectively. Multivariate analysis showed that patient sex, age, tumor grade, regional lymph nodes examined, positive regional lymph nodes, tumor size, extent of local invasion, and tumor metastases were independent risk factors for DSS. The C-index of the internal validation dataset for prediction of DSS was 0.64 (95% CI, 0.63-0.65), which was superior to the American Joint Committee on Cancer (AJCC) staging, 0.57 (95% CI, 0.56-0.58) (P<0.001). The 5-year DSS rates and median DSS time for patients in the low-risk group were significantly greater compared with high-risk group (P<0.001). CONCLUSIONS A validated prognostic disease-specific nomogram for patient survival in PDAC of the head of the pancreas following pancreaticoduodenectomy was developed.

[Palliative therapy concepts for pancreatic carcinoma]

The majority of patients with ductal pancreatic adenocarcinoma are already in a locally advanced or metastatic stage at the time of diagnosis and require palliative therapy. Interventional and operative measures are available for the restoration of biliary outflow in bile duct obstruction and the continuity of the upper intestinal lumen in duodenal or gastric outlet obstruction. In the presence of tumor-related pain, pain therapy according to the World Health Organization (WHO) scheme or a truncus coeliacus blockade, in cachexia a nutritional therapy and in thromboembolic events an anticoagulant therapy are used. An individualized palliative chemotherapy regimen should be selected for each patient, taking into account the patient's general condition and the side effects profile of the chemotherapeutic agents. Radiochemotherapy and local ablative therapies should currently only be used within the framework of studies. A palliative resection is not recommended according to current knowledge.

A nomogram for predicting survival of patients with locally advanced pancreatic cancer treated with chemoradiotherapy

BACKGROUND

We developed a nomogram for predicting survival of patients with locally advanced pancreatic cancer (LAPC) after concurrent chemoradiotherapy (CRT) using ¹⁸F-flurodeoxyglucose-positron emission tomography (FDG-PET) parameters and CA 19-9 levels.

METHODS

Based on 426 patients with LAPC who received concurrent CRT between 2004 and 2015, we investigated significant prognostic factors for survival to build a nomogram, including the maximum standardized uptake value (SUV_max) and CA 19-9 levels. Predictive accuracy and discriminative ability were then measured.

RESULTS

Median progression-free survival and overall survival (OS) were 9.4 and 15.4 months, respectively, at a median 15-month follow-up. High-dose radiation (EQD2, ≥61 Gy), initial SUV_max <3.5 and CA 19-9 ≤400 U/mL, and surgical resection after CRT were significantly related to prolonged OS by multivariate analysis (p < 0.05). A nomogram model for OS was established and showed good calibration and acceptable discrimination (c-index 0.656). Using the nomogram, 3 different prognosis groups could be identified with a median OS of 25, 15, and 11 months (p < 0.001).

CONCLUSION

A nomogram was developed with high-dose radiation (EQD2, ≥61 Gy), initial SUV_max <3.5, CA 19-9 ≤400 U/mL, and surgical resection after CRT for patients with LAPC. This will help in clinical decision-making and in selecting patients for CRT.

Overall survival and cancer-specific survival in patients with surgically resected pancreatic head adenocarcinoma: A competing risk nomogram analysis

Background: The objective of this study was to estimate probabilities of overall survival (OS) and cancer-specific survival (CSS) in patients with pancreatic head adenocarcinoma after surgery. In addition, we attempted to build nomograms to predict prognosis of these patients.

Methods: Patients diagnosed with surgically resected pancreatic head adenocarcinoma between 2004 and 2014 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were established for estimating 1-, 2- and 3-year OS and CSS based on Cox regression model and Fine and Grey's model. The performance of the nomogram was measured by concordance index (C-index) and the area under receiver operating characteristic (ROC) curve (AUC).

Results: A total of 2374 patients were retrospectively collected from the SEER database. The discrimination of nomogram for OS prediction was superior to that of the Tumor-Node-Metastasis (TNM) 7th or 8th edition stage systems (C-index = 0.640, 95% CI, 0.618 - 0.662 vs 0.573, 95% CI, 0.554 - 0.593, P < 0.001; 0.640, 95% CI, 0.618 - 0.662 vs 0.596, 95% CI, 0.586 - 0.607, P < 0.001, respectively). The comparisons of values of AUC showed that the established nomograms displayed better discrimination power than TNM 7th or 8th stage systems for predicting both OS and CSS.

Conclusions: The nomograms which could predict 1-, 2- and 3-year OS and CSS were established in this study. Our nomograms showed a relatively good performance and could be served as an effective tool for prognostic evaluation of patients with pancreatic head adenocarcinoma after surgery.

Can physician gestalt predict survival in patients with resectable pancreatic adenocarcinoma?

PURPOSE

Clinician gestalt may hold unexplored information that can be capitalized upon to improve existing nomograms. The study objective was to evaluate physician ability to predict 2-year overall survival (OS) in resected pancreatic ductal adenocarcinoma (PDAC) patients based on pre-operative clinical characteristics and routine CT imaging.

METHODS

Ten surgeons and two radiologists were provided with a clinical vignette (including age, gender, presenting symptoms, and pre-operative CA19-9 when available) and pre-operative CT scan for 20 resected PDAC patients and asked to predict the probability of each patient reaching 2-year OS. Receiver operating characteristic curves were used to assess agreement and to compare performance with an established institutional nomogram.

RESULTS

Ten surgeons and 2 radiologists participated in this study. The area under the curve (AUC) for all physicians was 0.707 (95% CI 0.642-0.772). Attending physicians with > 5 years experience performed better than physicians with < 5 years of clinical experience since completion of post-graduate training (AUC = 0.710, 95% CI [0.536-0.884] compared to AUC = 0.662, 95% CI [0.398-0.927]). Radiologists performed better than surgeons (AUC = 0.875, 95% CI [0.765-0.985] compared to AUC = 0.656, 95% CI [0.580-0.732]). All but one physician outperformed the clinical nomogram (AUC = 0.604).

CONCLUSIONS

This pilot study demonstrated significant promise in the quantification of physician gestalt. While PDAC remains a difficult disease to prognosticate, physicians, particularly those with more clinical experience and radiologic expertise, are able to perform with higher accuracy than existing nomograms in predicting 2-year survival.

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study

It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.

Developing and validating a center-specific preoperative prediction calculator for risk of pancreaticoduodenectomy

BACKGROUND

The American College of Surgeons (ACS) Surgical Risk Calculator predicts postoperative risk based on preoperative variables. The ACS model was compared to an institution-specific risk calculator for pancreaticoduodenectomy (PD).

METHODS

Observed outcomes were compared with those predicted by the ACS and institutional models. Receiver operating characteristic (ROC) analysis evaluated the models' predictive ability. Institutional models were evaluated with retrospective and prospective internal validation.

RESULTS

Brier scores indicate equivalent aggregate predictive ability. ROC values for the institutional model (ROC: 0.675-0.881, P < 0.01) indicate superior individual event occurrence prediction (ACS ROC: 0.404-0.749, P < 0.01-0.860). Institutional models' accuracy was upheld in retrospective (ROC: 0.765-0.912) and prospective (ROC: 0.882-0.974) internal validation.

CONCLUSIONS

Identifying higher-risk patients allows for individualized care. While ACS and institutional models accurately predict average complication occurrence, the institutional models are superior at predicting individualized outcomes. Predictive metrics specific to PD center volume may more accurately predict outcomes.

Nomogram for predicting survival in patients with pancreatic cancer

Background

The purpose of this study was to develop a nomogram to predict cancer-specific survival (CSS) in pancreatic cancer (PC).

Patients and methods

We used the Surveillance, Epidemiology, and End Results (SEER) database to analyze 53,028 patients diagnosed with PC from 2004 to 2014 and randomly divided them into the training (n=26,583) cohort and validation (n=26,445) cohort. Univariate and multivariate analyses were used to select independent prognostic factors. We used significant prognostic factors for constructing a nomogram based on Cox regression analyses. Validation of the nomogram was assessed by discrimination and calibration.

Results

According to the multivariate models of training cohort, a nomogram that combined age, race, tumor location, marital status, tumor size, TNM stage, tumor grade, and surgery was constructed for predicting CSS. The internally validated and externally validated C-indexes were 0.741 and 0.734, respectively. The calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year CSS accurately.

Conclusion

A nomogram effectively predicts survival in patients with PC. This prognostic model may be considered for use in clinical practice.

Biomarkers in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is currently the third most frequent form of malignancy. The role of biomarkers in the diagnostic and therapeutic strategy of cancer is constantly expanding. Translational research is already changing paradigms in tumours encompassing from early diagnosis to precision medicine in advanced disease. Nomenclature for molecular subtypes of tumours is gradually gaining acceptance and there are growing expectations it will further go from the bench to the bedside. However, the clinical relevance of biomarkers in PDAC is still far behind the relevance of biomarkers in other solid tumours. This article is part of a wider project (GALLgo) involving over forty specialists devoted to the multidisciplinary management of PDAC which concluded in recommendations based on scientific evidence. The aim of the present article is to review the diagnostic, prognostic and predictive biomarkers, either in localised or advanced disease, which have been lately subjected to study and analysis and others currently available for PDAC in order to give strength-graded recommendations linked to quality of evidence that can be used as guidelines in routine clinical practice.

Development and Validation of a Nomogram for Predicting Survival in Patients with Advanced Pancreatic Ductal Adenocarcinoma

This study aimed to develop and validate an effective prognostic nomogram for advanced PDAC patients. We conducted a prospective multicenter cohort study involving 1,526 advanced PDAC patients from three participating hospitals in China between January 1, 2004 and December 31, 2013. Two thirds of the patients were randomly assigned to the training set (n = 1,017), and one third were assigned to the validation set (n = 509). Multivariate cox regression analysis was performed to identify significant prognostic factors for overall survival to develop the nomogram. Internal and external validation using C-index and calibration curve were conducted in the training set and validation set respectively. As results, seven independent prognostic factors were identified: age, tumor stage, tumor size, ALT (alanine aminotransferase), ALB (albumin), CA 19-9, HBV infection status, and these factors were entered into the nomogram. The proposed nomogram showed favorable discrimination and calibration both in the training set and validation set. The C-indexes of the training set and validation set were 0.720 and 0.696 respectively, which were both significantly higher than that of the staging system (C-index = 0.613, P < 0.001). In conclusion, the proposed nomogram may be served as an effective tool for prognostic evaluation of advanced PDAC.

A nomogram for predicting the risk of neck node metastasis in pathologically node-negative oral cavity carcinoma

OBJECTIVE

To generate a nomogram for predicting the risk of neck node metastasis in pathologically node-negative patients using a combination of variables comprising of protein expression, ultrastructural alterations and clinicopathological parameters.

MATERIALS AND METHODS

Surgically removed oral tumours (n = 103) were analysed for the expression of desmosomal and hemidesmosomal assembly proteins by immunohistochemistry and ultrastructural alterations by transmission electron microscopy (TEM). Protein expression, ultrastructural alterations and clinicopathological variables were used to construct nomogram from the training set in 75 patients. Clinical utility of the nomogram was validated in a discrete set of 28 patients.

RESULTS

Univariate and multivariate analyses were performed on the training set, and obtained significant variables comprising of integrin β4 expression (p = .027), number of hemidesmosomes (p = .027)/desmosomes (p = .046), tumour differentiation grade (p = .033) and tumour thickness (p = .024) were used for construction of the nomogram. The area under the curve was calculated for both training 0.821 (95% CI 0.725-0.918) and validation sets 0.880 (95% CI 0.743-1.000). The nomogram demonstrated a predictive accuracy of 73.3% and 78.6% with the sensitivity of 81.4% and 83.3% in the training and validation sets, respectively.

CONCLUSIONS

The nomogram constructed on postsurgical tumour samples will be a value addition to histopathology for the detection of neck node metastasis in pathologically node-negative patients.

Covered versus uncovered metal stents for malignant gastric outlet obstruction: Systematic review and meta-analysis

BACKGROUND AND AIM

Self-expandable metal stents (SEMS) are used for non-resectable malignant gastric outlet obstruction (GOO). Studies of covered versus uncovered SEMS have yielded inconsistent results as a result of heterogeneity in design and patient population. We carried out a meta-analysis to compare covered and uncovered gastroduodenal SEMS.

METHODS

Using MEDLINE, Embase, and the Cochrane database, we identified 1624 patients from 13 prospective and retrospective studies that evaluated covered and uncovered SEMS for malignant GOO and were published until October 2016. We pooled data on SEMS dysfunction, technical and clinical success, and adverse events using the fixed-effect or random-effects model.

RESULTS

Compared with uncovered SEMS, covered SEMS did not show any significant difference in stent dysfunction risk (risk ratio [RR], 1.02; 95% confidence interval [CI], 0.79-1.32). A subgroup analysis of five randomized trials suggested a trend toward a lower dysfunction risk in covered SEMS (RR, 0.63; 95% CI, 0.45-0.88). Covered SEMS were associated with a lower occlusion risk (RR, 0.44; 95% CI, 0.28-0.68), but with a higher migration risk (RR, 4.28; 95% CI, 2.89-6.34). Technical and clinical success rates were comparable between the groups. Overall adverse events tended to be more frequent in covered SEMS (RR, 1.75; 95% CI, 1.09-2.83).

CONCLUSIONS

Outcomes of covered and uncovered gastroduodenal SEMS were comparable, although the lower dysfunction rate of covered SEMS observed in the analysis of randomized trials needs further investigation. Antimigration mechanisms for covered SEMS and identification of patients who can achieve longer patency from uncovered SEMS would help improve the outcomes of gastroduodenal SEMS.

Neutrophil/lymphocyte ratio helps select metastatic pancreatic cancer patients benefitting from oxaliplatin

BACKGROUND

High Neutrophil/Lymphocyte ratio (NLR), as a measure of enhanced inflammatory response, has been negatively associated with prognosis in patients with localized pancreatic ductal adenocarcinoma (PDA).

OBJECTIVE

In the present study, we aimed at investigating the prognostic value of NLR in two homogeneous groups of chemotherapy-naïve metastatic PDA patients. Patients were treated with either gemcitabine (GEM) or gemcitabine/oxaliplatin (GEMOXA). We also assessed whether NLR could identify patients benefiting from the use of oxaliplatin.

METHODS

Consecutive PDA patients treated at the Medical Oncology Unit of Tor Vergata University Hospital of Rome with either GEM or GEMOXA were included (n= 103). NLR was assessed before and during chemotherapy and correlated with outcome together with common clinical and biochemical variables.

RESULTS

Among 17 analyzed variables NLR, Karhofsky Perfomance Status (KPS), d-dimer and erythrocyte sedimentation rate were found to be significantly associated with median Overall Survival (mOS) at the univariate analysis. Only NLR and KPS were independent prognosticator at multivariate analysis, with NLR displaying the highest statistical significance. NLR was also predictive of oxaliplatin activity, as only patients with NLR > 2.5 (cutoff determined upon ROC analysis) derived benefit from GEMOXA over GEM.

CONCLUSIONS

NLR is both an independent prognostic and predictive factor in metastatic PDA, since only patients with high NLR seem to benefit from the addition of oxaliplatin. NLR may help select patients for whom a particularly poor prognosis might justify more intensive, yet less tolerable, combination regimens.

CA19-9-related tumor kinetics after first-line chemotherapy of patients with advanced pancreatic cancer: a monoinstitutional experience

The absolute value of carbohydrate antigen 19-9 (CA19-9) pretreatment and its reduction after chemotherapy are established prognostic variables for patients with advanced pancreatic cancer. The present study is a retrospective monoinstitutional evaluation of the prognostic role of the CA19-9 reduction and some CA19-9-related tumor kinetics parameters, such as tumor growth rate constant (G), kinetic tumor response and log ratio. Forty-one cases met the selection criteria. After 8 weeks only G reported an inverse relationship with OS (r = -0.494) that was confirmed by regression analysis (R (2) = 0.192). G after 8 weeks of chemotherapy appears as a possible surrogate end point of overall survival.