Pancreas Solid Tumors

Pancreatic neuroendocrine tumor with solitary splenic metastasis and synchronous renal cell carcinoma: A rare case report

Synchronous pancreatic neuroendocrine tumors and renal cell cancer are extremely rare. Von-Hipple-Landau syndrome is a major association. A 43-year-old male patient with left upper quadrant pain and significant weight loss was diagnosed with a synchronous pancreatic tail neuroendocrine tumor with solitary splenic metastasis and a clear-cell renal cell carcinoma of the left kidney. Sonography and a computed tomography scan of the abdomen showed a complex exophytic left renal mass and a necrotic lesion limited to the spleen. Although not apparent on preoperative imaging, distal pancreatic mass was also discovered intraoperatively. Subsequently, left radical nephrectomy, splenectomy, and distal pancreatectomy were performed, and the synchronous primaries and splenic metastasis were confirmed histopathologically. This case is unique in that it demonstrates multiple extremely rare events occurring simultaneously, namely pancreatic and kidney primaries, as well as solitary splenic metastasis.

KRT81 and HNF1A expression in pancreatic ductal adenocarcinoma: investigation of predictive and prognostic value of immunohistochemistry-based subtyping

Even after decades of research, pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease and responses to conventional treatments remain mostly poor. Subclassification of PDAC into distinct biological subtypes has been proposed by various groups to further improve patient outcome and reduce unnecessary side effects. Recently, an immunohistochemistry (IHC)-based subtyping method using cytokeratin-81 (KRT81) and hepatocyte nuclear factor 1A (HNF1A) could recapitulate some of the previously established molecular subtyping methods, while providing significant prognostic and, to a limited degree, also predictive information. We refined the KRT81/HNF1A subtyping method to classify PDAC into three distinct biological subtypes. The prognostic value of the IHC-based method was investigated in two primary resected cohorts, which include 269 and 286 patients, respectively. In the second cohort, we also assessed the predictive effect for response to erlotinib + gemcitabine. In both PDAC cohorts, the new HNF1A-positive subtype was associated with the best survival, the KRT81-positive subtype with the worst, and the double-negative with an intermediate survival (p < 0.001 and p < 0.001, respectively) in univariate and multivariate analyses. In the second cohort (CONKO-005), the IHC-based subtype was additionally found to have a potential predictive value for the erlotinib-based treatment effect. The revised IHC-based subtyping using KRT81 and HNF1A has prognostic significance for PDAC patients and may be of value in predicting treatment response to specific therapeutic agents.

Electrospun nanofibrous mats loaded with gemcitabine and cisplatin suppress bladder tumor growth by improving the tumor immune microenvironment

The perplexing issues related to positive surgical margins and the considerable negative consequences associated with systemic chemotherapy have posed ongoing challenges for clinicians, especially when it comes to addressing bladder cancer treatment. The current investigation describes the production of nanocomposites loaded with gemcitabine (GEM) and cisplatin (CDDP) through the utilization of electrospinning technology. In vitro and in vivo studies have provided evidence of the strong effectiveness in suppressing tumor advancement while simultaneously reducing the accumulation of chemotherapy drugs within liver and kidney tissues. Mechanically, the GEM and CDDP-loaded electrospun nanocomposites could effectively eliminate myeloid-derived suppressor cells (MDSCs) in tumor tissues, and recruit CD8+ T cells and NKp46+ NK cells to kill tumor cells, which can also effectively inhibit tumor microvascular formation. Our investigation into the impact of localized administration of chemotherapy through GEM and CDDP-loaded electrospun nanocomposites on the tumor microenvironment will offer novel insights for tackling tumors.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most common (90%) type of solid pancreatic neoplasm. Due to its late presentation and poor survival rate, early diagnosis and timely treatment is of utmost importance for better clinical outcomes. Endoscopic ultrasound provides high-resolution images of the pancreas and has excellent sensitivity in the diagnosis of even small (<2 cm) pancreatic lesions. Apart from imaging, it also has an advantage of tissue acquisition (EUS fine-needle aspiration, FNA; or fine-needle biopsy, FNB) for definitive diagnoses. EUS-guided tissue acquisition plays a crucial role in genomic and molecular studies, which in today's era of personalized medicine, are likely to become important components of PDAC management. With the use of better needle designs and technical advancements, EUS has now become an indispensable tool in the management of PDAC. Lastly, artificial intelligence for the detection of pancreatic lesions and newer automated needles for tissue acquisition will obviate observer dependency in the near future, resulting in the wider dissemination and adoption of this technology for improved outcomes in patients with PDAC.

Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms

PURPOSE

This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs).

METHODS

This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m²). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs.

RESULTS

The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients.

CONCLUSION

The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.

Synchronous or metachronous presentation of pancreatic neuroendocrine tumor versus secondary lesion to pancreas in patients affected by renal cell carcinoma. Systematic review

The simultaneous or metachronous occurrence of pancreatic neuroendocrine tumor (panNET) and renal cell carcinoma (RCC) may represent a rare coincidence or a manifestation of von Hippel-Lindau disease (VHL). These two malignancies share both radiological and cytopathological features, making the differential diagnosis very challenging. In this review, we collected all cases of concurrent diagnosis of localized panNET and RCC, with or without VHL, as reported in the literature to date. We aimed to provide an insight into the differential diagnosis between panNET and RCC pancreatic metastasis with a focus on the optimal therapeutic algorithm depending on the diagnosis. We performed literature research in PubMed library databases for articles about coexisting panNET and RCC published from 2001 to 2018. We selected nine articles with a total of 13 patients, including one treated at our institution. Patients' median age was 49 years and eight out of 13 patients were women. VHL was diagnosed in nine cases. Most patients underwent radical nephrectomy for RCC (9/13) and a clear cell renal carcinoma variant was identified in six cases. The diagnosis of panNET was synchronous with RCC detection in nine cases and metachronous in four cases. The diameter of the pancreatic lesion was >2 cm in six cases. In two cases the panNET was misdiagnosed as metastatic RCC by radiological tests. Somatostatin receptor scanning was performed only in our patient (Octreoscan) showing intense uptake in the pancreatic mass. Endoscopic ultrasound fine needle aspiration of the pancreatic lesion was performed in four patients: in two cases the panNET was confused with metastatic RCC by cytological analysis. Most patients underwent pancreatic surgery (10/13) without histological confirmation. Clear cell panNET was recognized in six cases, while mixed neuroendocrine non-neuroendocrine neoplasm was diagnosed in one patient. Immunohistochemistry (IHC) staining showed positivity to typical neuroendocrine markers (chromogranin A and synaptophysin) in all reported tested cases (8/8). Three patients underwent systemic treatment: two patients received sunitinib and one patient interleukin-2 (IL-2). Other neoplasms were observed in seven patients, of whom six were affected by VHL syndrome. When neoplastic lesions are recognized in both the kidney and pancreas, panNET and RCC pancreatic metastasis are often misdiagnosed due to similar radiological and cytopathological features. An accurate differential diagnosis is crucial and IHC plays a central role in distinguishing the two entities. The therapeutic algorithm may change depending on the diagnosis: while pancreatic RCC metastases benefit from resection, in panNETs and VHL the indication for surgery must be carefully evaluated.

Utility of CT to Differentiate Pancreatic Parenchymal Metastasis from Pancreatic Ductal Adenocarcinoma

Simple Summary

The purpose of this retrospective study was to report the computed tomography (CT) features of pancreatic parenchymal metastasis (PPM) and identify CT features that may help discriminate between PPM and PDAC. At multivariable analysis, well-defined margins (OR, 6.64; 95% CI: 1.47–29.93; p = 0.014), maximal enhancement during arterial phase (OR, 6.15; 95% CI: 1.13–33.51; p = 0.036), no vessel involvement (OR, 7.19; 95% CI: 1.51–34.14) and no Wirsung duct dilatation (OR, 10.63; 95% CI: 2.27–49.91) were independently associated with PPM. A nomogram based on CT features identified at multivariable analysis yielded an AUC of 0.92 (95% CI: 0.85–0.98) for the diagnosis of PPM vs. PDAC.

Abstract

Purpose: To report the computed tomography (CT) features of pancreatic parenchymal metastasis (PPM) and identify CT features that may help discriminate between PPM and pancreatic ductal adenocarcinoma (PDAC). Materials and methods: Thirty-four patients (24 men, 12 women; mean age, 63.3 ± 10.2 [SD] years) with CT and histopathologically proven PPM were analyzed by two independent readers and compared to 34 patients with PDAC. Diagnosis performances of each variable for the diagnosis of PPM against PDAC were calculated. Univariable and multivariable analyses were performed. A nomogram was developed to diagnose PPM against PDAC. Results: PPM mostly presented as single (34/34; 100%), enhancing (34/34; 100%), solid (27/34; 79%) pancreatic lesion without visible associated lymph nodes (24/34; 71%) and no Wirsung duct enlargement (29/34; 85%). At multivariable analysis, well-defined margins (OR, 6.64; 95% CI: 1.47–29.93; p = 0.014), maximal enhancement during arterial phase (OR, 6.15; 95% CI: 1.13–33.51; p = 0.036), no vessel involvement (OR, 7.19; 95% CI: 1.512–34.14) and no Wirsung duct dilatation (OR, 10.63; 95% CI: 2.27–49.91) were independently associated with PPM. The nomogram yielded an AUC of 0.92 (95% CI: 0.85–0.98) for the diagnosis of PPM vs. PDAC. Conclusion: CT findings may help discriminate between PPM and PDAC.