Systematic review of the clinical significance of lymph node micrometastases of pancreatic adenocarcinoma following surgical resection

Phase 2 study of preoperative chemotherapy with nab-paclitaxel and gemcitabine followed by chemoradiation for borderline resectable or node-positive pancreatic ductal adenocarcinoma

BACKGROUND

Neoadjuvant treatment with nab-paclitaxel and gemcitabine for potentially operable pancreatic adenocarcinoma has not been well studied in a prospective interventional trial and could down-stage tumors to achieve negative surgical margins.

METHODS

A single-arm, open-label phase 2 trial (NCT02427841) enrolled patients with pancreatic adenocarcinoma deemed to be borderline resectable or clinically node-positive from March 17, 2016 to October 5, 2019. Patients received preoperative gemcitabine 1000 mg/m2 and nab-paclitaxel 125 mg/m2 on Days 1, 8, 15, every 28 days for two cycles followed by chemoradiation with 50.4 Gy intensity-modulated radiation over 28 fractions with concurrent fluoropyrimidine chemotherapy. After definitive resection, patients received four additional cycles of gemcitabine and nab-paclitaxel. The primary endpoint was R0 resection rate. Other endpoints included treatment completion rate, resection rate, radiographic response rate, survival, and adverse events.

RESULTS

Nineteen patients were enrolled, with the majority having head of pancreas primary tumors, both arterial and venous vasculature involvement, and clinically positive nodes on imaging. Among them, 11 (58%) underwent definitive resection and eight of 19 (42%) achieved R0 resection. Disease progression and functional decline were primary reasons for deferring surgical resection after neoadjuvant treatment. Pathologic near-complete response was observed in two of 11 (18%) resection specimens. Among the 19 patients, the 12-month progression-free survival was 58%, and 12-month overall survival was 79%. Common adverse events were alopecia, nausea, vomiting, fatigue, myalgia, peripheral neuropathy, rash, and neutropenia.

CONCLUSION

Gemcitabine and nab-paclitaxel followed by long-course chemoradiation represents a feasible neoadjuvant treatment strategy for borderline resectable or node-positive pancreatic cancer.

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Purpose

This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.

Materials and Methods

We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).

Results

Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.

Conclusions

The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

Blood small extracellular vesicles derived miRNAs to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis

BACKGROUND

The differential diagnosis of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) is clinically challenging due to a lack of minimally invasive diagnosis methods. MicroRNAs (miRNAs) derived from small extracellular vesicles (EVs) in the blood have been reported as a promising diagnosis biomarker for various types of cancer. However, blood small EV miRNA signatures and their diagnostic value to differentiate between PDAC and CP remain to be determined.

METHODS

In this study, 107 patients with PDAC or CP were recruited, and 90 patients were finally enrolled for a training cohort (n = 48) and test cohort (n = 42). Small RNA sequencing was used to assess the expression of blood small EV miRNAs in these patients.

RESULTS

The linear model from the differentially expressed blood small EV miR-95-3p divided by miR-26b-5p showed an average sensitivity of 84.1% and an average specificity of 96.6% to identify PDAC from CP in the training cohort and the test cohort, respectively. When the model was combined with serum carbohydrate antigen 19-9 (CA19-9), the average sensitivity increased to 96.5%, and the average specificity remained at 96.4% of both cohorts, which demonstrated the best performance of all the published biomarkers for distinguishing between PDAC and CP. The causal analysis performed using the Bayesian network demonstrated that miR-95-3p was associated with a "consequence" of "cancer" and miR-26b-5p as a "cause" of "pancreatitis." A subgroup analysis revealed that blood small EV miR-335-5p/miR-340-5p could predict metastases in both cohorts and was associated with an overall survival (p = 0.020).

CONCLUSIONS

This study indicated that blood small EV miR-95-3p/miR-26b-5p and its combination with serum levels of CA19-9 could separate PDAC from CP, and miR-335-5p/miR-340-5p was identified to associate with PDAC metastasis and poor prognosis. These results suggested the potentiality of blood small EV miRNAs as differential diagnosis and metastases biomarkers of PDAC.

A radiomics model that predicts lymph node status in pancreatic cancer to guide clinical decision making: A retrospective study

Purpose: To construct a radiomics-based model for predicting lymph node (LN) metastasis status in pancreatic ductal adenocarcinoma (PDAC) before therapy and to evaluate its prognostic clinical value. Materials and Methods: We retrospectively collected preoperative CT scans of 130 PDAC patients who underwent original tumor resection and LN dissection in the entire cohort between January 2014 and December 2017. Radiomics features were systematically extracted and analyzed from CT scans of 89 patients in the primary cohort. To construct a radiomics signature, the least absolute shrinkage and selection operator methods were employed with LN metastasis status as classification labels. Pathological analysis of LN status which were assessed by experienced pathologists was used as the evaluation label. We subjected the clinical nomogram to multivariable logistic regression analysis and conducted performance evaluation based on its discrimination, calibration, and clinical value. The model was tested and validated in 41 patients with PDAC in a separate validation cohort. Results: Four radiomics features closely associated with LN metastasis were selected in the primary and validation cohorts (P < 0.01). Following the integration of CT-reported results and radiomics signatures into the radiomics nomogram, we reported better performance in the primary (area under the curve, 0.80) and validation (area under the curve, 0.78) cohorts. Conclusion: The noninvasive tool constructed from the portal venous phase CT based on radiomics showed better performance for LN metastasis prediction than traditional approaches in pancreatic cancer. It may assist surgeons in crafting detailed procedures before treatment, this subsequently improves tumor staging and resection of patients.

Dataset for the reporting of carcinoma of the exocrine pancreas: recommendations from the International Collaboration on Cancer Reporting (ICCR)

BACKGROUND AND OBJECTIVES

Current guidelines for the pathology reporting on pancreatic cancer differ in certain aspects, resulting in divergent reporting practice and a lack of comparability of data. Here we report on a new international dataset for the pathology reporting of resection specimens with cancer of the exocrine pancreas (ductal adenocarcinoma and acinar cell carcinoma). The dataset was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter-)national pathology and cancer organisations.

METHODS AND RESULTS

According to the ICCR's rigorous process for dataset development, an international expert panel consisting of pancreatic pathologists, a pancreatic surgeon and an oncologist produced a set of core and non-core data items based on a critical review and discussion of current evidence. Commentary was provided for each data item to explain the rationale for selecting it as a core or non-core element, its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the document was finalised and ratified, and the dataset, which includes a synoptic reporting guide, was published on the ICCR website.

CONCLUSIONS

This first international dataset for cancer of the exocrine pancreas is intended to promote high quality, standardised pathology reporting. Its widespread adoption will improve consistency of reporting, facilitate multidisciplinary communication and enhance comparability of data, all of which will help to improve the management of pancreatic cancer patients.

Modeling Texture in Deep 3D CNN for Survival Analysis

Radiomics has shown remarkable potential for predicting the survival outcome for various types of cancers such as pancreatic ductal adenocarcinoma (PDAC). However, to date, there has been limited research using convolutional neural networks (CNN) with radiomic methods for this task, due to their requirement for large training sets. To overcome this issue, we propose a new type of radiomic descriptor modeling the distribution of learned features with a Gaussian mixture model (GMM). These parametric features (GMM-CNN) are computed from gross tumor volumes of PDAC patients defined semi-automatically in pre-operative computed tomography (CT) scans. We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RF) to predict the survival outcome of patients with PDAC. Our experiments assess the advantage of GMM-CNN compared to employing the same 3D CNN model directly, standard radiomic (i.e., histogram, texture and shape), conditional entropy (CENT) based on 3DCNN, and clinical features (i.e., serum carbohydrate antigen 19-9 and chemotherapy neoadjuvant). Using the RF model (100 samples for training; 59 samples for validation), GMM-CNN features provided the highest area under the ROC curve (AUC) of 72.0% (p =  6.4×10^-5) compared to 64.0% (p = 0.01) for the 3D CNN model output, 66.8% (p = 0.01) for standard radiomic features, 64.2% (p = 0.003) for CENT, and 57.6% (p = 0.3) for clinical variables. Our results suggest that the proposed GMM-CNN features used with a RF classifier can significantly improve the capacity to prognosticate PDAC patients prior to surgery via routinely-acquired imaging data.

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks

BACKGROUND

In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches.

METHODS

The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma.

RESULTS

The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5-10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%).

CONCLUSIONS

The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

Intraoperative Detection of Micrometastases in Whole Excised Lymph Nodes Using Fluorescent Paired-Agent Imaging Principles: Identification of a Suitable Staining and Rinsing Protocol

PURPOSE

Correctly identifying nodal status is recognized as a critical prognostic factor in many cancer types and is essential to guide adjuvant treatment. Currently, surgical removal of lymph nodes followed by pathological examination is commonly performed as a standard-of-care to detect node metastases. However, conventional pathology protocols are time-consuming, yet less than 1 % of lymph node volumes are examined, resulting in a 30-60 % rate of missed micrometastases (0.2-2 mm in size).

PROCEDURES

This study presents a method to fluorescently stain excised lymph nodes using paired-agent molecular imaging principles, which entail co-administration of a molecular-targeted imaging agent with a suitable control (untargeted) agent, whereby any nonspecific retention of the targeted agent is accounted for by the signal from the control agent. Specifically, it was demonstrated that by dual-needle continuous infusion of either an antibody-based imaging agent pair (epidermal growth factor receptor (EGFR) targeted agent: IRDye-800CW labeled Cetuximab; control agent: IRDye-700DX-IgG) or an Affibody-based pair (EGFR targeted Affibody® agent: ABY-029; control agent IRDYe-700DX carboxylate) at 0.3 ml/min.

RESULTS

The results demonstrated the possibility to achieve >99 % sensitivity and > 95 % specificity for detection of a single micrometastasis (~0.2 mm diameter) in a whole lymph node within 22 min of tissue processing time.

CONCLUSION

The detection capabilities offer substantial improvements over existing intraoperative lymph node biopsy methods (e.g., frozen pathology has a micrometastasis sensitivity <20 %).

Clinical Implication of Node-negative Resectable Pancreatic Cancer

BACKGROUND

Lymph node metastasis is one of the strongest prognostic factors of pancreatic cancer. However, the clinical implication of pathologically node-negative pancreatic cancer (pN0-PC) has not been fully investigated.

METHODS

Patients who underwent surgical resection for radiologically resectable pancreatic cancer between 2002 and 2018 were included in this study. A clinicopathological examination focusing on pN0-PC was performed.

RESULTS

Of all 533 patients, 155 (29.1%) were diagnosed with pN0-PC and 378 (70.9%) were diagnosed with node-positive pancreatic cancer (pN1/2-PC). The 5-year survival rates of patients with pN0-PC and pN1/2-PC were 57.1% and 25.0%, respectively (p < 0.001). A multivariate analysis revealed six prognostic factors in pN0-PC: age ≥ 70 years, nonadministration of adjuvant chemotherapy, anterior serosal invasion, nerve plexus invasion, and microscopic lymphatic and venous invasions. The 5-year survival rates of patients who had pN0-PC with 0-1 risk factor, with 2-3 risk factors, and with 4-6 risk factors were 87.6%, 47.9%, and 16.4%, respectively. Survival of patients who had pN0-PC with 4-6 risk factors was comparable to that of pN1/2 patients. The diagnostic capability of metastasis-negative lymph node was unsatisfactory, with a predictive value of < 43%.

CONCLUSIONS

Although the prognosis of patients with pN0-PC was better than that of patients with pN1/2-PC, it is not satisfactory. Survival of patients who had pN0-PC with 0-1 risk factors was extremely favorable; however, survival of patients who had pN0-PC with 4-6 risk factors was similar to those with pN1/2-PC.

MiR-132 promotes the proliferation, invasion and migration of human pancreatic carcinoma by inhibition of the tumor suppressor gene PTEN

MicroRNA (miRNAs) emerges as key oncogene or tumor suppressor in a variety of cancers including pancreatic carcinoma. In this study, we detected the role of miR-132 in development and progression of pancreatic cancer and the underlying mechanism. First, the expression of miR-132 in pancreatic carcinoma and adjacent non-cancerous tissues were detected by qRT-PCR. Then, the role of miR-132 in biological function of pancreatic carcinoma cells was investigated. Our results identified that miR-132 was generally upregulated in pancreatic carcinoma, and phosphatase and tensin homolog (PTEN) was generally downregulated. miR-132 and PTEN were associated with advanced tumor size, lymph node metastasis and Tumor-Nodes-Metastases (TNM) stage of pancreatic carcinoma. Downregulation of miR-132 inhibited proliferation, migration and invasion of pancreatic carcinoma cells. In contrast, overexpression of miR-132 promoted proliferation, migration and invasion of pancreatic carcinoma cells. The luciferase reporter system demonstrated PTEN is a direct target of miR-132. Overexpression of PTEN abrogated the induction of miR-132 on proliferation, migration and invasion of pancreatic carcinoma cells. Taken together, miR-132 promotes the proliferation, invasion and migration of human pancreatic cancer by inhibition of PTEN, and could be a tumor oncogene in development and progression of pancreatic carcinoma, and might be a candidate prognostic biomarker and a promising target for new treatment of human pancreatic cancer.

Pathomorphological features of metastatic lymph nodes as predictors of postoperative prognosis in pancreatic cancer

To investigate the pathological features of metastatic lymph nodes (LN) in pancreatic ductal adenocarcinoma (PDAC) and to determine factors with prognostic implications.Metastatic LN status is a proven significant factor for predicting postoperative prognosis in pancreatic cancer patients. However, the effective prognostic criteria regarding metastatic LNs for such disease remain unknown.We retrospectively reviewed 98 patients with R0/1 resection for PDAC. All metastatic LNs were evaluated for the pathomorphological features of metastasis and analyzed in terms of postoperative outcomes. Various morphological patterns of metastasis were assessed in 440 positive LNs and then classified into 4 groups: common type, direct type (continuously invaded by the main tumor), scatter type (multiple tumor clusters among the normal LN tissues), and isolated tumor cell (ITC).The pathological stage was defined as stage IIA in 10% and IIB in 90% patients. Common-type metastasis was noted in 55% positive LNs of 75% node-positive patients; direct type in 36% LNs of 69% patients; scatter type in 5% LNs of 14% patients; and ITCs in 5% LNs of 18% patients. Significant difference was noted only in recurrence-free survival (RFS) but not in overall survival (OS) in the common-type; only in OS but not in RFS for the scatter type; and neither in RFS nor OS for both direct type and ITC. Multivariate analysis revealed that only LN ratio and curability were independent predictive factors of poor.The tumor distribution patterns in metastatic LNs are the postoperative prognostic factors in pancreatic cancer.