Pancreatic cancer surveillance and its ongoing challenges: is it time to refine our eligibility criteria?

Progress in the application of dual-energy CT in pancreatic diseases

Pancreatic diseases are difficult to diagnose due to their insidious onset and complex pathophysiological developmental characteristics. In recent years, dual-energy computed tomography (DECT) imaging technology has rapidly advanced. DECT can quantitatively extract and analyze medical imaging features and establish a correlation between these features and clinical results. This feature enables the adoption of more modern and accurate clinical diagnosis and treatment strategies for patients with pancreatic diseases so as to achieve the goal of non-invasive, low-cost, and personalized treatment. The purpose of this review is to elaborate on the application of DECT for the diagnosis, biological characterization, and prediction of the survival of patients with pancreatic diseases (including pancreatitis, pancreatic cancer, pancreatic cystic tumor, pancreatic neuroendocrine tumor, and pancreatic injury) and to summarize its current limitations and future research prospects.

Familial pancreatic cancer: a case study and review of the psychosocial effects of diagnoses on families

BACKGROUND

Familial pancreatic cancer touches families through a genetic susceptibility to developing this neoplasia. Genetic susceptibility is assessed via family history, genetic testing, or both. Individuals with two or more first-degree relatives or three or more relatives of any degree diagnosed with pancreatic cancer are considered at elevated risk. Following a diagnosis of familial pancreatic cancer, patients and families face uncertainty and anxiety about the future. Psychosocial effects of a pancreatic cancer diagnosis on families include fear, concerns about personal health, and how lifestyle may impact the risk of developing pancreatic cancer.

CASE PRESENTATION

A 66-year-old male was diagnosed with pancreatic ductal adenocarcinoma stage IIB, T3, N1, M0. A genetic referral was made due to a history of multiple cases of pancreatic cancer within the patient's family. Genetic testing revealed the patient had a pathogenic variant in the ATM gene that is associated with an increased risk for pancreatic cancer development. The patient's one adult child was offered testing due to the autosomal dominant pattern of inheritance for this variant. The adult child was found to have the same pathogenic variant. She expressed fear for her future and her child's future health and longevity. Discussing a case study allows us to capture the multi-faceted relationship between the disease, the affected individuals, and their families. Examining the psychosocial stresses and concerns when there is a pancreatic cancer diagnosis in the family is essential to provide holistic care to patients and families.

CONCLUSIONS

The psychosocial effects of FPC may be overwhelming for patients and families. Healthcare providers can offer education, support, and referrals to appropriate services to help families cope through stages of evaluation, diagnosis, and treatment of FPC.

CircZFR promotes pancreatic cancer progression through a novel circRNA-miRNA-mRNA pathway and stabilizing epithelial-mesenchymal transition protein

Pancreatic cancer (PC) ranks third in incidence and seventh in mortality among cancers worldwide. CircZFR has been implicated in various human cancers. Yet, how they affect PC progression is understudied. Herein, we demonstrated that circZFR was upregulated in PC tissues and cells, a feature that was correlated with the poor performance of patients with PC. Functional analyses elucidated that circZFR facilitated cell proliferation and enhanced tumorigenicity of PC. Moreover, we found that circZFR facilitated cell metastasis by differentially regulating the levels of proteins related to epithelial-mesenchymal transition (EMT). Mechanistic investigations revealed that circZFR sponged miR-375, thereby upregulating the downstream target gene, GREMLIN2 (GREM2). Additionally, circZFR knockdown resulted in attenuation of the JNK pathway, an effect that was reversed by GREM2 overexpression. Collectively, our findings implicate circZFR as a positive regulator of PC progression through the miR-375/GREM2/JNK axis.

The Multicenter Cancer of Pancreas Screening Study: Impact on Stage and Survival

PURPOSE

To report pancreas surveillance outcomes of high-risk individuals within the multicenter Cancer of Pancreas Screening-5 (CAPS5) study and to update outcomes of patients enrolled in prior CAPS studies.

METHODS

Individuals recommended for pancreas surveillance were prospectively enrolled into one of eight CAPS5 study centers between 2014 and 2021. The primary end point was the stage distribution of pancreatic ductal adenocarcinoma (PDAC) detected (stage I v higher-stage). Overall survival was determined using the Kaplan-Meier method.

RESULTS

Of 1,461 high-risk individuals enrolled into CAPS5, 48.5% had a pathogenic variant in a PDAC-susceptibility gene. Ten patients were diagnosed with PDAC, one of whom was diagnosed with metastatic PDAC 4 years after dropping out of surveillance. Of the remaining nine, seven (77.8%) had a stage I PDAC (by surgical pathology) detected during surveillance; one had stage II, and one had stage III disease. Seven of these nine patients with PDAC were alive after a median follow-up of 2.6 years. Eight additional patients underwent surgical resection for worrisome lesions; three had high-grade and five had low-grade dysplasia in their resected specimens. In the entire CAPS cohort (CAPS1-5 studies, 1,731 patients), 26 PDAC cases have been diagnosed, 19 within surveillance, 57.9% of whom had stage I and 5.2% had stage IV disease. By contrast, six of the seven PDACs (85.7%) detected outside surveillance were stage IV. Five-year survival to date of the patients with a screen-detected PDAC is 73.3%, and median overall survival is 9.8 years, compared with 1.5 years for patients diagnosed with PDAC outside surveillance (hazard ratio [95% CI]; 0.13 [0.03 to 0.50], P = .003).

CONCLUSION

Most pancreatic cancers diagnosed within the CAPS high-risk cohort in the recent years have had stage I disease with long-term survival.

One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography

Purpose

We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making.

Methods

This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance.

Results

A total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists.

Conclusions

The AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective.

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma

PURPOSE

Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.

METHODS

From August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction.

RESULTS

DLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85-0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91-0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012).

CONCLUSIONS

The DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.