Contrast agent dispersion visualized by CE-EUS may be a prediction tool for FOLFIRINOX chemotherapy effectiveness in patients with pancreatic adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) still has a dismal 5-year overall survival of 13 %. Chemotherapy is increasingly used as treatment in both (neo-) adjuvant and palliative conditions. However, the overall survival benefits of chemotherapy must be weighed against significant side effects leading to a reduction in quality of life. CE-EUS and elastography could provide additional information about the vascularization and elasticity of the pancreatic tumor. The aim of this study was to investigate if contrast-enhanced endoscopic ultrasound and/or elastography could be suitable to predict the effectiveness of FOLFIRINOX. Methods: Single center, prospective proof-of-concept study in which intravenous contrast agent was administered and strain ratio was calculated in patients undergoing EUS in their regular diagnostic work-up. Directly after contrast administration, a video of 120 s was recorded and afterwards tracked and fitted by a Modified Local Density Random Walk (mLDRW) model. Results: We included 17 patients. Based on cross-sectional imaging based RECIST criteria, chemotherapy treatment was effective in 11 patients and not effective in 6 patients. The contrast dispersion parameter (κ1) differed significantly between both groups in favor of the responders: 2.994 (IQR 1.670–5.170) vs 1.203 (IQR 0.953–1.756), p = 0.005. The elastography strain ratio was higher in the effectively treated group (20.9 vs 13.6, p = 0.138). Conclusion: This proof-of-concept study showed that the dispersion parameter of the first wave of contrast was 2.5 times higher in patients in whom FOLFIRINOX was effective, suggesting that this parameter could possibly be a reliable prediction tool.</p

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team’s (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT’s prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.</p

Ferumoxtran-10-enhanced MRI for pre-operative metastatic lymph node detection in pancreatic, duodenal, or periampullary adenocarcinoma

Objectives: To assess 3-Tesla (3-T) ultra-small superparamagnetic iron oxide (USPIO)-enhanced MRI in detecting lymph node (LN) metastases for resectable adenocarcinomas of the pancreas, duodenum, or periampullary region in a node-to-node validation against histopathology. Methods: Twenty-seven consecutive patients with a resectable pancreatic, duodenal, or periampullary adenocarcinoma were enrolled in this prospective single expert centre study. Ferumoxtran-10-enhanced 3-T MRI was performed pre-surgery. LNs found on MRI were scored for suspicion of metastasis by two expert radiologists using a dedicated scoring system. Node-to-node matching from in vivo MRI to histopathology was performed using a post-operative ex vivo 7-T MRI of the resection specimen. Sensitivity and specificity were calculated using crosstabs. Results: Eighteen out of 27 patients (median age 65 years, 11 men) were included in the final analysis (pre-surgery withdrawal n = 4, not resected because of unexpected metastases peroperatively n = 2, and excluded because of inadequate contrast-agent uptake n = 3). On MRI 453 LNs with a median size of 4.0 mm were detected, of which 58 (13%) were classified as suspicious. At histopathology 385 LNs with a median size of 5.0 mm were found, of which 45 (12%) were metastatic. For 55 LNs node-to-node matching was possible. Analysis of these 55 matched LNs, resulted in a sensitivity and specificity of 83% (95% CI: 36–100%) and 92% (95% CI: 80–98%), respectively. Conclusion: USPIO-enhanced MRI is a promising technique to preoperatively detect and localise LN metastases in patients with pancreatic, duodenal, or periampullary adenocarcinoma. Clinical relevance statement: Detection of (distant) LN metastases with USPIO-enhanced MRI could be used to determine a personalised treatment strategy that could involve neoadjuvant or palliative chemotherapy, guided resection of distant LNs, or targeted radiotherapy. Registration: The study was registered on clinicaltrials.gov NCT04311047. https://clinicaltrials.gov/ct2/show/NCT04311047?term=lymph+node&cond=Pancreatic+Cancer&cntry=NL&draw=2&rank=1. Key Points: LN metastases of pancreatic, duodenal, or periampullary adenocarcinoma cannot be reliably detected with current imaging. This technique detected LN metastases with a sensitivity and specificity of 83% and 92%, respectively. MRI with ferumoxtran-10 is a promising technique to improve preoperative staging in these cancers

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team’s (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT’s prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals

Validation of In Vivo Nodal Assessment of Solid Malignancies with USPIO-Enhanced MRI: A Workflow Protocol

Background: In various cancer types, the first step towards extended metastatic disease is the presence of lymph node metastases. Imaging methods with sufficient diagnostic accuracy are required to personalize treatment. Lymph node metastases can be detected with ultrasmall superpara-magnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI), but this method needs validation. Here, a workflow is presented, which is designed to compare MRI-visible lymph nodes on a node-to-node basis with histopathology. Methods: In patients with prostate, rectal, periampullary, esophageal, and head-and-neck cancer, in vivo USPIO-enhanced MRI was performed to detect lymph nodes suspicious of harboring metastases. After lymphadenectomy, but before histopathological assessment, a 7 Tesla preclinical ex vivo MRI of the surgical specimen was performed, and in vivo MR images were radiologically matched to ex vivo MR images. Lymph nodes were annotated on the ex vivo MRI for an MR-guided pathological examination of the specimens. Results: Matching lymph nodes of ex vivo MRI to pathology was feasible in all cancer types. The annotated ex vivo MR images enabled a comparison between USPIO-enhanced in vivo MRI and histopathology, which allowed for analyses on a nodal, or at least on a nodal station, basis. Conclusions: A workflow was developed to validate in vivo USPIO-enhanced MRI with histopathology. Guiding the pathologist towards lymph nodes in the resection specimens during histopathological work-up allowed for the analysis at a nodal basis, or at least nodal station basis, of in vivo suspicious lymph nodes with corresponding histopathology, providing direct information for validation of in vivo USPIO-enhanced, MRI-detected lymph nodes

Validation of In Vivo Nodal Assessment of Solid Malignancies with USPIO-Enhanced MRI: A Workflow Protocol

Background: In various cancer types, the first step towards extended metastatic disease is the presence of lymph node metastases. Imaging methods with sufficient diagnostic accuracy are required to personalize treatment. Lymph node metastases can be detected with ultrasmall superpara-magnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI), but this method needs validation. Here, a workflow is presented, which is designed to compare MRI-visible lymph nodes on a node-to-node basis with histopathology. Methods: In patients with prostate, rectal, periampullary, esophageal, and head-and-neck cancer, in vivo USPIO-enhanced MRI was performed to detect lymph nodes suspicious of harboring metastases. After lymphadenectomy, but before histopathological assessment, a 7 Tesla preclinical ex vivo MRI of the surgical specimen was performed, and in vivo MR images were radiologically matched to ex vivo MR images. Lymph nodes were annotated on the ex vivo MRI for an MR-guided pathological examination of the specimens. Results: Matching lymph nodes of ex vivo MRI to pathology was feasible in all cancer types. The annotated ex vivo MR images enabled a comparison between USPIO-enhanced in vivo MRI and histopathology, which allowed for analyses on a nodal, or at least on a nodal station, basis. Conclusions: A workflow was developed to validate in vivo USPIO-enhanced MRI with histopathology. Guiding the pathologist towards lymph nodes in the resection specimens during histopathological work-up allowed for the analysis at a nodal basis, or at least nodal station basis, of in vivo suspicious lymph nodes with corresponding histopathology, providing direct information for validation of in vivo USPIO-enhanced, MRI-detected lymph nodes

EUS-Guided Biopsy with a Novel Puncture Biopsy Forceps Needle—Feasibility Study

Endoscopic ultrasound (EUS) with fine needle aspiration (FNA) or biopsy (FNB) to diagnose lesions in the gastrointestinal tract is common. Demand for histology sampling to identify treatment-specific targets is increasing. Various core biopsy FNB needles to obtain tissue for histology are currently available, however, with variable (37–97%) histology yields. In this multicenter study, we evaluated performance, safety, and user experience of a novel device (the puncture biopsy forceps (PBF) needle). Twenty-four procedures with the PBF needle were performed in 24 patients with a suspected pancreatic lesion (n = 10), subepithelial lesion (n = 10), lymph node (n = 3), or pararectal mass (n = 1). In 20/24 (83%) procedures, the PBF needle yielded sufficient material for interpretation (sample adequacy). In 17/24 (71%), a correct diagnosis was made with the material from the PBF needle (diagnostic accuracy). All participating endoscopists experienced a learning curve. (Per)procedural technical issues occurred in four cases (17%), but there were no adverse events. The PBF needle is a safe and potentially useful device to obtain an EUS-guided biopsy specimen. As the design of the PBF needle is different to core biopsy FNB needles, specific training will likely further improve the performance of the PBF needle. Furthermore, the design of the needle needs further improvement to make it more robust in clinical practice

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (&lt;2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors &lt;2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.</jats:p

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, however current models still fail to identify small (&amp;amp;lt;2cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors &amp;amp;lt;2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.</jats:p