Development and multicenter validation of a nomogram for preoperative prediction of lymph node positivity in pancreatic cancer (NeoPangram)

Neoadjuvant Treatments for Pancreatic Ductal Adenocarcinoma: Where We Are and Where We Are Going

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) represents a challenging disease for the surgeon, oncologist, and radiation oncologist in both diagnostic and therapeutic settings. Surgery is currently the gold standard treatment, but the role of neoadjuvant treatment (NAD) is constantly evolving and gaining importance in resectable PDACs. The aim of this narrative review is to report the state of the art and future perspectives of neoadjuvant therapy in patients with PDAC.

METHODS

A PubMed database search of articles published up to September 2022 was carried out.

RESULTS

Many studies showed that FOLFIRINOX or Gemcitabine-nab-paclitaxel in a neoadjuvant setting had a relevant impact on overall survival (OS) for patients with locally advanced and borderline resectable PDAC without increasing post-operative complications. To date, there have not been many published multicentre randomised trials comparing upfront surgery with NAD in resectable PDAC patients, but the results obtained are promising. NAD in resectable PDAC showed long-term effective benefits in terms of median OS (5-year OS rate 20.5% in NAD group vs. 6.5% in upfront surgery). NAD could play a role in the treatment of micro-metastatic disease and lymph nodal involvement. In this scenario, given the low sensitivity and specificity for lymph-node metastases of radiological investigations, CA 19-9 could be an additional tool in the decision-making process.

CONCLUSIONS

The future challenge could be to identify only selected patients who will really benefit from upfront surgery despite a combination of NAD and surgery.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

Background

The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation.

Purpose

To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC.

Material and Methods

In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values.

Results

Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively).

Conclusion

Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.

Establishment of the diagnostic and prognostic nomograms for pancreatic cancer with bone metastasis

Bone metastasis (BM) is rare in patients with pancreatic cancer (PC), but often neglected at the initial diagnosis and treatment. Bone metastasis is associated with a worse prognosis. This study was aimed to perform a large data analysis to determine the predictors and prognostic factors of BM in PC patients and to develop two nomograms to quantify the risks of BM and the prognosis of PC patients with BM. In the present study, we reviewed and collected the data of patients who were diagnosed as PC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used together to screen and validate the risk factors for BM in PC patients. The independent prognostic factors for PC patients with BM were identified by Cox regression analysis. Finally, two nomograms were established via calibration curves, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). This study included 16,474 PC patients from the SEER database, and 226 of them were diagnosed with BM. The risk factors of BM for PC patients covered age, grade, T stage, N stage, tumor size, and primary site. The independent prognostic factors for PC patients with BM included age, race, grade, surgery, and lung metastasis. The AUC of the diagnostic nomogram was 0.728 in the training set and 0.690 in the testing set. In the prognostic nomogram, the AUC values of 6/12/18 month were 0.781/0.833/0.849 in the training set and 0.738/0.781/0.772 in the testing set. The calibration curve and DCA furtherly indicated the satisfactory clinical consistency of the nomograms. These nomograms could be accurate and personalized tools to predict the incidence of BM in PC patients and the prognosis of PC patients with BM. The nomograms can help clinicians make more personalized and effective treatment choices.

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.

Role of CA 19.9 in the Management of Resectable Pancreatic Cancer: State of the Art and Future Perspectives

Background: Surgery still represents the gold standard of treatment for resectable pancreatic ductal adenocarcinoma (PDAC). Neoadjuvant treatments (NAT), currently proposed for borderline and locally advanced PDACs, are gaining momentum even in resectable tumors due to the recent interesting concept of “biological resectability”. In this scenario, CA 19.9 is having increasing importance in preoperative staging and in the choice of therapeutic strategies. We aimed to assess the state of the art and to highlight the future perspectives of CA 19.9 use in the management of patients with resectable pancreatic cancer. Methods: A PubMed database search of articles published up to December 2021 has been carried out. Results: Elevated pre-operative levels of CA 19.9 have been associated with reduced overall survival, nodal involvement, and margin status positivity after surgery. These abilities of CA 19.9 increase when combined with radiological or different biological criteria. Unfortunately, due to strong limitations of previously published articles, CA 19.9 alone cannot be yet considered as a key player in resectable pancreatic cancer patient management. Conclusion: The potential of CA 19.9 must be fully explored in order to standardize its role in the “biological staging” of patients with resectable pancreatic cancer.

CA19.9 Serum Level Predicts Lymph-Nodes Status in Resectable Pancreatic Ductal Adenocarcinoma: A Retrospective Single-Center Analysis

Background

The choice between upfront surgery or neoadjuvant treatments (NAT) for resectable pancreatic ductal adenocarcinoma (R-PDAC) is controversial. R-PDAC with potential nodal involvement could benefit from NT. Ca (Carbohydrate antigen) 19.9 and serum albumin levels, alone or in combination, have proven their efficacy in assessing PDAC prognosis. The objective of this study was to evaluate the role of Ca 19.9 serum levels in predicting nodal status in R-PDAC.

Methods

Preoperative Ca 19.9, as well as serum albumin levels, of 165 patients selected for upfront surgery have been retrospectively collected and correlated to pathological nodal status (N), resection margins status (R) and vascular resections (VR). We further performed ROC curve analysis to identify optimal Ca 19.9 cut-off for pN+, R+ and vascular resection prediction.

Results

Increased Ca 19.9 levels in 114 PDAC patients were significantly associated with pN+ (p <0.001). This ability, confirmed in all the series by ROC curve analysis (Ca 19.9 ≥32 U/ml), was lost in the presence of hypoalbuminemia. Furthermore, Ca 19.9 at the cut off >418 U/ml was significantly associated with R+ (87% specificity, 36% sensitivity, p 0.014). Ca 19.9, at the cut-off >78 U/ml, indicated a significant trend to predict the need for VR (sensitivity 67%, specificity 53%; p = 0.059).

Conclusions

In R-PDAC with normal serum albumin levels, Ca 19.9 predicts pN+ and R+, thus suggesting a crucial role in deciding on NAT.