Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer

A nomogram based on dual-layer detector spectral computed tomography quantitative parameters and morphological quantitative indicator for distinguishing metastatic and nonmetastatic regional lymph nodes in pancreatic ductal adenocarcinoma

Background

There is no unified scope for regional lymph node (LN) dissection in patients with pancreatic ductal adenocarcinoma (PDAC). Incomplete regional LN dissection can lead to postoperative recurrence, while blind expansion of the scope of regional LN dissection significantly increases the perioperative risk without significantly prolonging overall survival. We aimed to establish a noninvasive visualization tool based on dual-layer detector spectral computed tomography (DLCT) to predict the probability of regional LN metastasis in patients with PDAC.

Methods

A total of 163 regional LNs were reviewed and divided into a metastatic cohort (n=58 LNs) and nonmetastatic cohort (n=105 LNs). The DLCT quantitative parameters and the nodal ratio of the longest axis to the shortest axis (L/S) of the regional LNs were compared between the two cohorts. The DLCT quantitative parameters included the iodine concentration in the arterial phase (APIC), normalized iodine concentration in the arterial phase (APNIC), effective atomic number in the arterial phase (APZeff), normalized effective atomic number in the arterial phase (APNZeff), slope of the spectral attenuation curves in the arterial phase (APλHU), iodine concentration in the portal venous phase (PVPIC), normalized iodine concentration in the portal venous phase (PVPNIC), effective atomic number in the portal venous phase (PVPZeff), normalized effective atomic number in the portal venous phase (PVPNZeff), and slope of the spectral attenuation curves in the portal venous phase (PVPλHU). Logistic regression analysis based on area under the curve (AUC) was used to analyze the diagnostic performance of significant DLCT quantitative parameters, L/S, and the models combining significant DLCT quantitative parameters and L/S. A nomogram based on the models with highest diagnostic performance was developed as a predictor. The goodness of fit and clinical applicability of the nomogram were assessed through calibration curve and decision curve analysis (DCA).

Results

The combined model of APNIC + L/S (APNIC + L/S) had the highest diagnostic performance among all models, yielding an AUC, sensitivity, and specificity of 0.878 [95% confidence interval (CI): 0.825-0.931], 0.707, and 0.886, respectively. The calibration curve indicated that the APNIC-L/S nomogram had good agreement between the predicted probability and the actual probability. Meanwhile, the decision curve indicated that the APNIC-L/S nomogram could produce a greater net benefit than could the all- or-no-intervention strategy, with threshold probabilities ranging from 0.0 to 0.75.

Conclusions

As a valid and visual noninvasive prediction tool, the APNIC-L/S nomogram demonstrated favorable predictive efficacy for identifying metastatic LNs in patients with PDAC.

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma

PURPOSE

To predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT).

METHODS

Totally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade.

RESULTS

Necrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13-7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98-17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07-1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%).

CONCLUSION

Variables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

Clinical diagnosis and management of pancreatic cancer: Markers, molecular mechanisms, and treatment options

Pancreatic cancer (PC) is the third-leading cause of cancer deaths. The overall 5-year survival rate of PC is 9%, and this rate for metastatic PC is below 3%. However, the PC-induced death cases will increase about 2-fold by 2060. Many factors such as genetic and environmental factors and metabolic diseases can drive PC development and progression. The most common type of PC in the clinic is pancreatic ductal adenocarcinoma, comprising approximately 90% of PC cases. Multiple pathogenic processes including but not limited to inflammation, fibrosis, angiogenesis, epithelial-mesenchymal transition, and proliferation of cancer stem cells are involved in the initiation and progression of PC. Early diagnosis is essential for curable therapy, for which a combined panel of serum markers is very helpful. Although some mono or combined therapies have been approved by the United States Food and Drug Administration for PC treatment, current therapies have not shown promising outcomes. Fortunately, the development of novel immunotherapies, such as oncolytic viruses-mediated treatments and chimeric antigen receptor-T cells, combined with therapies such as neoadjuvant therapy plus surgery, and advanced delivery systems of immunotherapy will improve therapeutic outcomes and combat drug resistance in PC patients. Herein, the pathogenesis, molecular signaling pathways, diagnostic markers, prognosis, and potential treatments in completed, ongoing, and recruiting clinical trials for PC were reviewed.

Comment on “Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer”

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies because of its high invasiveness and metastatic potential. Computed tomography (CT) is often used as a preliminary diagnostic tool for pancreatic cancer, and it is increasingly used to predict treatment response and disease stage. Recently, a study published in World Journal of Gastroenterology reported that quantitative analysis of preoperative enhanced CT data can be used to predict postoperative overall survival in patients with PDAC. A tumor relative enhancement ratio of ≤ 0.7 indicates a higher tumor stage and poor prognosis.