Clinicopathological Features, Prognostic Factors and Survival in Patients With Pancreatic Cancer Bone Metastasis

Pre-Operative Markers of Post-Operative Complications in Pancreatic Cancer Patients: A Single-Center Study

PURPOSE

Pancreatic cancer remains a deadly disease with a low survival rate specifically if diagnosed at later stages. Surgery is one of the mainstay treatments for early stage disease but is accompanied by significant mortality and morbidity even in high volume centers. In the present study we wanted to determine the influence of pre-operative weight loss, BMI and liver function tests on postoperative outcomes.

METHODS

Data for patients who underwent pancreatic surgery at the American University of Beirut Medical Center between 1998 and 2017 were analyzed. The data included patient demographics, pathologic diagnosis, stage, nodal involvement, tumor grade, surgical margin, type of adjuvant therapy, liver function tests and post-operative complications. The correlation and p-value were determined by the chi-square test with p-value < 0.05 considered statistically significant.

RESULTS

The analysis included 236 patients. 63% were males and mean age was 60 years. A total of 96 (41%) patients had significant weight loss prior to surgery and 173 (31%) had a BMI > 30 kg/m2 which were considered obese. Obesity and significant weight loss were associated with increased risk of post-operative complications (P = 0.013 and P = 0.004, respectively). Transaminases and cholestatic liver enzymes were not associated with post-operative complications.

CONCLUSIONS

Assessment and management of nutritional status particularly weight loss and BMI prior to pancreatic cancer surgery may influence postoperative outcomes in this patient population.

Clinicopathological features, prognostic factors, and prognostic survival prediction in patients with extrahepatic bile duct cancer liver metastasis

PURPOSE

Extrahepatic bile duct cancer (EBDC) is a compound malignant tumor mainly consisting of extrahepatic cholangiocarcinoma and gallbladder carcinoma. Most EBDC patients are diagnosed at an advanced stage characterized by distant metastases, and the liver is one of the common sites of metastasis. Hence, the purpose of this study is to investigate the clinicopathological features, identify prognostic risk factors, and assess the long-term prognosis of extrahepatic bile duct cancer liver metastasis (EBDCLM).

METHODS

We identified 1922 eligible EBDCLM patients from the SEER database.Cox regression models were used to predict independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS),and Kaplan-Meier survival curves were drawn. A nomogram was constructed based on the results of multivariate Cox analysis, and the predictive effect of the nomogram was evaluated.

RESULTS

Age, surgery, chemotherapy, brain metastasis, and lung metastasis were common independent prognostic factors for OS and CSS, and radiotherapy and bone metastasis were independent prognostic factors for CSS. The Kaplan-Meier survival curves showed a significant increase in survival for patients aged less than or equal to 70 years, undergoing surgery and chemotherapy, and without lung metastases. The results showed that the nomogram constructed by us had good predictability and ha d strong clinical application value.

CONCLUSION

Our study identified age, surgery, chemotherapy, brain metastasis, and lung metastasis as independent prognostic factors for EBDCLM patients. The nomogram can accurately predict the survival probability, which is helpful for clinicians to assess the prognosis of patients with advanced EBDC and provide personalized clinical decisions.

Meaningful nomograms based on systemic immune inflammation index predicted survival in metastatic pancreatic cancer patients receiving chemotherapy

OBJECTIVE

The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy.

METHODS

This study is retrospective and consecutively included 143 patients from January 2013 to June 2021. The receiver operating characteristic (ROC) curve with the area under the curve (AUC) is utilized to determine the optimal cut-off value. The Kaplan-Meier survival analysis, univariate and multivariable Cox regression analysis are exploited to identify the correlation of inflammatory biomarkers and clinicopathological features with survival. R software are run to construct nomograms based on independent risk factors to visualize survival. Nomogram model is examined using calibration curve and decision curve analysis (DCA).

RESULTS

The best cut-off values of 966.71, 0.257, and 2.54 for the systemic immunological inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were obtained by ROC analysis. Cox proportional-hazards model revealed that baseline SII, history of drinking and metastasis sites were independent prognostic indices for survival. We established prognostic nomograms for primary endpoints of this study. The nomograms' predictive potential and clinical efficacy have been evaluated by calibration curves and DCA.

CONCLUSION

We constructed nomograms based on independent prognostic factors, these models have promising applications in clinical practice to assist clinicians in personalizing the management of patients.

The roles of lncRNAs and miRNAs in pancreatic cancer: a focus on cancer development and progression and their roles as potential biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is among the most penetrative malignancies affecting humans, with mounting incidence prevalence worldwide. This cancer is usually not diagnosed in the early stages. There is also no effective therapy against PDAC, and most patients have chemo-resistance. The combination of these factors causes PDAC to have a poor prognosis, and often patients do not live longer than six months. Because of the failure of conventional therapies, the identification of key biomarkers is crucial in the early diagnosis, treatment, and prognosis of pancreatic cancer. 65% of the human genome encodes ncRNAs. There are different types of ncRNAs that are classified based on their sequence lengths and functions. They play a vital role in replication, transcription, translation, and epigenetic regulation. They also participate in some cellular processes, such as proliferation, differentiation, metabolism, and apoptosis. The roles of ncRNAs as tumor suppressors or oncogenes in the growth of tumors in a variety of tissues, including the pancreas, have been demonstrated in several studies. This study discusses the key roles of some lncRNAs and miRNAs in the growth and advancement of pancreatic carcinoma. Because they are involved not only in the premature identification, chemo-resistance and prognostication, also their roles as potential biomarkers for better management of PDAC patients.

Understanding the Genetic Landscape of Pancreatic Ductal Adenocarcinoma to Support Personalized Medicine: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. While population-wide screening recommendations for PDAC in asymptomatic individuals are not achievable due to its relatively low incidence, pancreatic cancer surveillance programs are recommended for patients with germline causative variants in PDAC susceptibility genes or a strong family history. In this study, we sought to determine the prevalence and significance of germline alterations in major genes (ATM, BRCA1, BRCA2, CDKN2A, EPCAM, MLH1, MSH2, MSH6, PALB2, PMS2, STK11, TP53) involved in PDAC susceptibility. We performed a systematic review of PubMed publications reporting germline variants identified in these genes in PDAC patients. Overall, the retrieved articles included 1493 PDAC patients. A high proportion of these patients (n = 1225/1493, 82%) were found to harbor alterations in genes (ATM, BRCA1, BRCA2, PALB2) involved in the homologous recombination repair (HRR) pathway. Specifically, the remaining PDAC patients were reported to carry alterations in genes playing a role in other cancer pathways (CDKN2A, STK11, TP53; n = 181/1493, 12.1%) or in the mismatch repair (MMR) pathway (MLH1, MSH2, MSH6, PMS2; n = 87/1493, 5.8%). Our findings highlight the importance of germline genetic characterization in PDAC patients for better personalized targeted therapies, clinical management, and surveillance.

Long-term survival after surgical resection for bone metastasis from pancreatic cancer: A case report

INTRODUCTION

Pancreatic cancer (PC) is highly malignant and metastatic; however, bone metastases are rare. Although the effectiveness of conversion surgery for distant metastases of PC has been reported in a few cases, there are no reports on surgical resection for bone metastases. Here, we report a case of long-term survival after resection of bone metastasis from PC.

PATIENT CONCERNS

A 60-year-old woman underwent pancreaticoduodenectomy after neoadjuvant chemoradiotherapy for pancreatic head cancer. At 28 months after surgery, multiple lung metastases from PC were diagnosed, and chemotherapy was administered. After 59 months, chemotherapy was terminated because all target lesions had disappeared on imaging.

DIAGNOSIS

At 77 months after the initial surgery, bone metastasis in the left 9th rib was detected by positron emission tomography/computed tomography, which was performed due to elevated carbohydrate antigen 19-9 levels.

INTERVENTIONS

Chemotherapy was readministered as the initial treatment. Subsequently, due to the long-term well-controlled status of the recurrence site and the absence of other metastases, thoracoscopic-assisted partial resection of the left 9th rib was performed 128 months following pancreaticoduodenectomy. Pathological examination revealed adenocarcinoma metastasis from PC.

OUTCOMES

The patient is currently alive without recurrence 44 months after resection for bone metastasis and 172 months after the initial surgery.

CONCLUSION

Surgical resection may be favorable in patients with bone metastasis of PC that is well-controlled with chemotherapy.

Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis

BACKGROUND

The mechanisms of distant metastasis in pancreatic cancer (PC) have not been elucidated, and this study aimed to explore the risk factors affecting the metastasis and prognosis of metastatic patients and to develop a predictive model.

METHOD

Clinical data from patients meeting criteria from 1990 to 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and two machine learning methods, random forest and support vector machine, combined with logistic regression, were used to explore risk factors influencing distant metastasis and to create nomograms. The performance of the model was validated using calibration curves and ROC curves based on the Shaanxi Provincial People's Hospital cohort. LASSO regression and Cox regression models were used to explore the independent risk factors affecting the prognosis of patients with distant PC metastases.

RESULTS

We found that independent risk factors affecting PC distant metastasis were: age, radiotherapy, chemotherapy, T and N; the independent risk factors for patient prognosis were: age, grade, bone metastasis, brain metastasis, lung metastasis, radiotherapy and chemotherapy.

CONCLUSION

Together, our study provides a method for risk factors and prognostic assessment for patients with distant PC metastases. The nomogram we developed can be used as a convenient individualized tool to facilitate aid in clinical decision making.

Nomogram for Predicting Distant Metastasis of Pancreatic Ductal Adenocarcinoma: A SEER-Based Population Study

(1) Background: The aim of this study was to identify risk factors for distant metastasis of pancreatic ductal adenocarcinoma (PDAC) and develop a valid predictive model to guide clinical practice; (2) Methods: We screened 14328 PDAC patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Lasso regression analysis combined with logistic regression analysis were used to determine the independent risk factors for PDAC with distant metastasis. A nomogram predicting the risk of distant metastasis in PDAC was constructed. A receiver operating characteristic (ROC) curve and consistency-index (C-index) were used to determine the accuracy and discriminate ability of the nomogram. A calibration curve was used to assess the agreement between the predicted probability of the model and the actual probability. Additionally, decision curve analysis (DCA) and clinical influence curve were employed to assess the clinical utility of the nomogram; (3) Results: Multivariate logistic regression analysis revealed that risk factors for distant metastasis of PDAC included age, primary site, histological grade, and lymph node status. A nomogram was successfully constructed, with an area under the curve (AUC) of 0.871 for ROC and a C-index of 0.871 (95% CI: 0.860-0.882). The calibration curve showed that the predicted probability of the model was in high agreement with the actual predicted probability. The DCA and clinical influence curve showed that the model had great potential clinical utility; (4) Conclusions: The risk model established in this study has a good predictive performance and a promising potential application, which can provide personalized clinical decisions for future clinical work.

Risk factors and predictive nomograms for early death of patients with pancreatic cancer liver metastasis: A large cohort study based on the SEER database and Chinese population

Background

The liver is the most common organ for distant metastasis of pancreatic cancer, and patients with pancreatic cancer liver metastases (PCLM) often die in a short period of time. As such, the establishment of an effective nomogram to predict the probability of early death (survival time ≤3 months) in PCLM patients is of considerable significance.

Methods

Patients diagnosed with PCLM in the Surveillance, Epidemiology, and End Result (SEER) database between 2010 and 2015 were included for model construction and internal validation. A data set was obtained from the Chinese population for external validation. Risk factors that contributed to all-cause and cancer-specific early death were determined by means of univariable and multivariable logistic regression. The accuracy of the nomogram was verified by means of receiver operating characteristic (ROC) curves, and the true consistency of the model was assessed by calibration curves. The clinical applicability of the model was evaluated by means of decision curve analysis (DCA).

Results

A total of 12,955 patients were included in the present study, of whom 7,219 (55.7%) experienced early death and 6,973 (53.8%) patients died of PCLM. Through multivariable logistic regression analysis, 11 risk factors associated with all-cause early death and 12 risk factors associated with cancer-specific early death were identified. The area under the curves (AUCs) for all-cause and cancer-specific early death were 0.806 (95% CI: 0.785- 0.827) and 0.808 (95% CI: 0.787- 0.829), respectively. Internal validation showed that the C-indexes of all-cause and cancer-specific early death after bootstrapping (5,000 re-samplings) were 0.805 (95% CI: 0.784-0.826) and 0.807 (95% CI: 0.786-0.828), respectively. As revealed by the calibration curves, the constructed nomograms exhibited good consistency. The decision curve analysis (DCA) indicated the nomograms had significant clinical applicability.

Conclusion

In the present study, reliable nomograms were developed for predicting the early death probability in patients with PCLM. Such tools can help clinicians identify high-risk patients and develop individualized treatment plans as early as possible.