A decision support tool for the detection of pancreatic cancer in general practice: A modified Delphi consensus

Association between unstable diabetes mellitus and risk of pancreatic cancer

BACKGROUND

Deterioration of glycaemic control in people with long-standing diabetes mellitus (diabetes) may be a possible indicator of pancreatic cancer. However, the magnitude of the association between diabetes deterioration and pancreatic cancer has received little attention.

METHODS

We conducted a matched cohort study, nested within a population-based cohort of Australian women with diabetes. Women with unstable diabetes, defined as a change in medication after a 2-year period of stable medication use, were matched by birth year to those with stable diabetes, in a 1:4 ratio. We used flexible parametric survival models to estimate hazard ratios (HRs) and 95% confidence intervals (CI).

RESULTS

We included 134,954 and 539,789 women in the unstable and stable diabetes cohorts, respectively (mean age 68 years). In total, 1,315 pancreatic cancers were diagnosed. Deterioration of stable diabetes was associated with a 2.5-fold increased risk of pancreatic cancer (HR 2.55; 95% CI 2.29-2.85). The risk was particularly high within the first year after diabetes deteriorated. HRs at 3 months, 6 months and 1 year were: 5.76 (95% CI 4.72-7.04); 4.56 (95% CI 3.81-5.46); and 3.33 (95% CI 2.86-3.89), respectively. The risk was no longer significantly different after 7 years.

CONCLUSIONS

Deterioration in glycaemic control in people with previously stable diabetes may be an indicator of pancreatic cancer, suggesting investigations of the pancreas may be appropriate. The weaker longer-term (3-7 years) association between diabetes deterioration and pancreatic cancer may indicate that poor glycaemic control can be a risk factor for pancreatic cancer.

The association between diabetes mellitus of different durations and risk of pancreatic cancer: an Australian national data-linkage study in women

AIMS

The bidirectional association between diabetes mellitus (DM) and pancreatic cancer (PC) is established; however, the strength of association between duration of DM and risk of PC needs further investigation.

METHODS

We conducted a case-control study nested within a population-based cohort of Australian women established using record linkage. Women diagnosed with PC from July 2007 to December 2013, were matched to five controls based on age and state of residence. DM was defined according to prescription of anti-diabetic medication from administrative prescription data. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI), adjusted for area-level socioeconomic status, rurality of residence, weighted comorbidity score, and predicted probability of obesity.

RESULTS

The analyses included 7,267 cases and 35,978 controls. The mean age at the time of DM diagnosis was 71 years whereas the mean age at the time of diagnosis of PC was 76 years. A history of DM of any duration was associated with a 2-fold increase in risk of PC (OR=2.12; 95%CI:1.96-2.29) compared to having no history of DM. The risk decreased with increasing duration of DM. The highest risk was in those who had recent-onset DM (OR=8.08; 95%CI:6.88-9.50 for <12 months of DM), but the risk remained elevated with ≥5 years of DM (OR=1.40; 95%CI:1.27-1.55).

CONCLUSION

The markedly increased risk of PC in those with recent-onset DM emphasises the need for further research to distinguish patients for whom new-onset DM is a manifestation of PC from those with type-2 DM. The elevated risk associated with long-standing DM suggests that preventing DM may contribute to a reduction in the incidence of PC.

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.