Nonselective β-adrenergic blockade impacts pancreatic cancer tumor biology, decreases perineural invasion and improves patient survival

ABSTRACT Background: Pancreatic adenocarcinoma remains one of the worst digestive cancers. Surgical resection is the main target when treating a patient with curative intent. Aim: To assess angiolymphatic invasion as a prognostic factor in resected pN0 pancreatic cancer. Methods: Thirty-eight patients were submitted to pancreatoduodenectomy due to head pancreatic cancer. Tumor size, margins, lymph nodes, pTNM staging, angiolymphatic and perineural invasion were described in the pathologists' reports. Results: Most patients were female. Overall median survival was 13 months. Gemcitabine was the regimen of choice for chemotherapy in selected patients; however, it did not improve overall survival. pR0 resection had better survival compared with pR1. Within the pN0 group, survival was significantly better in patients without angiolymphatic invasion. Conclusion: Angiolymphatic invasion in N0 pancreatoduodenectomy can be demonstrated by the Hematoxylin-Eosin stain and may predict a poor prognosis factor for those patients.

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Vascular invasion in pancreatic cancer: Tumor biology or tumor topography?

Surgery ◽

10.1016/j.surg.2012.05.012 ◽

2012 ◽

Vol 152 (3) ◽

pp. S143-S151 ◽

Cited By ~ 26

Author(s):

Alexander Rehders ◽

Nikolas H. Stoecklein ◽

Ayse Güray ◽

Rouwen Riediger ◽

Andrea Alexander ◽

...

Keyword(s):

Pancreatic Cancer ◽

Vascular Invasion ◽

Tumor Biology ◽

Cancer Tumor ◽

Tumor Topography

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Expression of Immune Checkpoint Regulators IDO, VISTA, LAG3, and TIM3 in Resected Pancreatic Ductal Adenocarcinoma

Cancers ◽

10.3390/cancers13112689 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2689

Author(s):

Felix Popp ◽

Ingracia Capino ◽

Joana Bartels ◽

Alexander Damanakis ◽

Jiahui Li ◽

...

Keyword(s):

Pancreatic Cancer ◽

Immune Checkpoint ◽

Patient Survival ◽

Checkpoint Inhibitors ◽

Tumor Infiltrating Lymphocytes ◽

Ductal Adenocarcinoma ◽

Lymph Node Status ◽

Clinicopathologic Parameters ◽

Survival Differences ◽

Infiltrating Lymphocytes

Pancreatic cancer features elaborate mechanisms of immune evasion. The potential of new immune molecules was explored to restore the antitumor immune response. If these immune molecules are associated with poor survival, specific drugs could take effect. Here, we analyze the expression of VISTA, LAG3, IDO, and TIM3 on tumor-infiltrating lymphocytes (TILs) and its impact on patient survival. We analyzed 153 pancreatic cancer patients from the prospectively managed database of the multicentered PANCALYZE study. Immunohistochemistry on a tissue microarray assessed VISTA, LAG3, IDO, and TIM3 expression of TILs from the patients undergoing primary resection. Complementarily, we analyzed publicly available transcriptomic data (n = 903). Successful completion of chemotherapy, and lymph node status were independent predictors of survival in the multivariate analysis of the clinicopathologic parameters. Fifteen tumors were exclusively VISTA-positive, thirteen tumors expressed VISTA together with TIM3, and ten tumors expressed VISTA together with IDO. Patients featuring tumors with high numbers of IDO-positive TILs had better patient survival (p = 0.037). VISTA, LAG3, and TIM3 expression did not correlate with survival. The analysis of publicly available data did not show survival differences. Tumors rarely co-express more than two immune molecules at the same time, and VISTA is most frequently co-expressed. Although IDO generally inhibits T-cell proliferation, a high expression of IDO was associated with improved survival. We expect immune checkpoint inhibitors against VISTA, LAG3, and TIM3 to be inefficient in a clinical application.

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Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102652 ◽

2021 ◽

Vol 68 ◽

pp. 102652

Author(s):

Vahid Asadpour ◽

Rex A. Parker ◽

Patrick R. Mayock ◽

Samuel E. Sampson ◽

Wansu Chen ◽

...

Keyword(s):

Neural Network ◽

Pancreatic Cancer ◽

Convolutional Neural Network ◽

Ct Images ◽

Cancer Tumor

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Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-06-2016-0021 ◽

2017 ◽

Vol 10 (2) ◽

pp. 166-182 ◽

Cited By ~ 2

Author(s):

Shabia Shabir Khan ◽

S.M.K. Quadri

Keyword(s):

Pancreatic Cancer ◽

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Patient Survival ◽

Data Partitioning ◽

Structure Identification ◽

Inference System ◽

Content Type ◽

Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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