Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.

Objectives: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs). Methods: A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed. Results: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts. Conclusion: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

Objective: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. Methods: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. Results: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. Conclusion: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Bifocal solid pseudopapillary tumour of the pancreas.

Solid pseudopapillary neoplasm of the pancreas (SPNP) is a rare entity. In this study, we present a woman in her 20's who presented for evaluation of two separate pancreatic masses. On imaging and biopsy, the tail lesion was thought to be a neuroendocrine tumour and the body lesion was thought to be a metastatic lymph node. The patient was brought to the operating room and underwent a distal pancreatectomy and splenectomy. The patient had an uneventful postoperative course and was discharged home on postoperative day 4. Pathology confirmed both masses were consistent with the diagnosis of well-differentiated SPNP with no signs of malignancy including lymphovascular or perineural invasion, or lymph node involvement.

EUS-FNA Biopsy for Pancreatic Mass.

OBJECTIVE: To assess both solid and cystic pancreatic lesions using endoscopic ultrasound (EUS), and the effect of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in patient management. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Department of Gastroenterology, Division of Internal Diseases, Sivas Cumhuriyet University Hospital, Sivas, Turkiye, from January 2018 to 2022. METHODOLOGY: Patients with pancreatic mass, who underwent EUS-FNA were inducted in the study. EUS-FNA was performed using a 22-gauge needle via both transgastric and transduodenal routes. The size of the pancreatic lesion, its location, and whether there was SMA or CA invasion were evaluated on CT and EUS scans. Biopsy results of 64 patients who received EUS-FNA due to pancreatic lesions were considered. The results were divided into malignancy or benign pathology. RESULTS: A total of 64 cases were compared. Crosstable Chi-square analysis showed a statistically significant difference between CT and EUS (p <0.001). EUS-FNA results revealed that out of the 64 patients with pancreatic mass detected in EUS, 46 had adenocarcinoma, 7 were negative for malignancy, 4 had intraductal papillary mucinous neoplasia (IPMN), 3 had neuroendocrine tumour (NET), 2 had lymphoma, and 2 had solid pseudopapillary neoplasia (SPN). In the 2-year follow-up of the seven patients who were negative for malignancy in EUS-FNA, there were no clinical, laboratory or imaging findings indicating pancreatic malignancy or distant metastasis. CONCLUSION: Tissue sampling through EUS-FNA has minimal side effects and remains useful in managing preoperative patients with resectable or suspicious pancreatic masses. KEY WORDS: Pancreatic cancer, Abdominal CT, Endoscopic ultrasound (EUS), Ultrasound-guided fine-needle aspiration (EUS-FNA).

Metabolic Tumor Volume on 18-Fluorodeoxyglucose Positron Emission Tomography as a Prognostic Marker of Survival in Patients With Locally Advanced or Metastatic Neuroendocrine Neoplasms Treated With 177Lutetium-DOTA-Octreotate Peptide Receptor Radionuclide Therapy.

OBJECTIVE: We investigated metabolic tumor volume (MTV) and total lesion glycolysis (TLG) on pre-treatment FDG-PET as prognostic markers for survival in patients with metastatic neuroendocrine neoplasms (NENs) receiving peptide receptor radionuclide therapy (PRRT). METHODS: A retrospective review of patients with metastatic NENs receiving PRRT was undertaken. Pre-treatment FDG-PET images were analyzed and variables collected included MTV and TLG (dichotomized by median into high vs low). Main Outcomes were overall survival (OS) and progression-free survival (PFS) by MTV and TLG (high vs low). RESULTS: One hundred five patients were included. Median age was 64 years (50% male). Main primary NEN sites were small bowel (43.8%) and pancreas (40.0%). Median MTV was 3.8 mL and median TLG was 19.9. Dichotomization formed identical cohorts regardless of whether MTV or TLG were used. Median OS was 72 months; OS did not differ based on MTV/TLG high versus low (47.4 months vs not reached; hazard ratio, 0.43; 95% confidence interval [CI], 0.18-1.04; P = 0.0594). Median PFS was 30.4 months; PFS differed based on MTV/TLG high versus low (21.6 months vs 45.7 months; hazard ratio, 0.35; 95% CI, 0.19-0.64; P = 0.007). CONCLUSIONS: Low MTV/TLG on pre-treatment FDG-PET was associated with longer PFS in metastatic NEN patients receiving PRRT.

Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.

This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study.

BACKGROUND: The planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast. METHODS: PANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases. RESULTS: The median JCI for the arterial and venous phases were 0.50 (range, 0.17-0.64) and 0.41 (range, 0.23-0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38-0.57) on the arterial phase, and 0.52 (range 0.36-0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators. CONCLUSIONS: Our results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial.

Importance: Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic (EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such diagnosis, but existing AI models focus solely on a single modality. Objective: To advance the clinical diagnosis of solid lesions in the pancreas through developing a multimodal AI model integrating both clinical information and EUS images. Design, Setting, and Participants: In this randomized crossover trial conducted from January 1 to June 30, 2023, from 4 centers across China, 12 endoscopists of varying levels of expertise were randomly assigned to diagnose solid lesions in the pancreas with or without AI assistance. Endoscopic ultrasonographic images and clinical information of 439 patients from 1 institution who had solid lesions in the pancreas between January 1, 2014, and December 31, 2022, were collected to train and validate the joint-AI model, while 189 patients from 3 external institutions were used to evaluate the robustness and generalizability of the model. Intervention: Conventional or AI-assisted diagnosis of solid lesions in the pancreas. Main Outcomes and Measures: In the retrospective dataset, the performance of the joint-AI model was evaluated internally and externally. In the prospective dataset, diagnostic performance of the endoscopists with or without the AI assistance was compared. Results: The retrospective dataset included 628 patients (400 men [63.7%]; mean [SD] age, 57.7 [27.4] years) who underwent EUS procedures. A total of 130 patients (81 men [62.3%]; mean [SD] age, 58.4 [11.7] years) were prospectively recruited for the crossover trial. The area under the curve of the joint-AI model ranged from 0.996 (95% CI, 0.993-0.998) in the internal test dataset to 0.955 (95% CI, 0.940-0.968), 0.924 (95% CI, 0.888-0.955), and 0.976 (95% CI, 0.942-0.995) in the 3 external test datasets, respectively. The diagnostic accuracy of novice endoscopists was significantly enhanced with AI assistance (0.69 [95% CI, 0.61-0.76] vs 0.90 [95% CI, 0.83-0.94]; P < .001), and the supplementary interpretability information alleviated the skepticism of the experienced endoscopists. Conclusions and Relevance: In this randomized crossover trial of diagnosing solid lesions in the pancreas with or without AI assistance, the joint-AI model demonstrated positive human-AI interaction, which suggested its potential to facilitate a clinical diagnosis. Nevertheless, future randomized clinical trials are warranted. Trial Registration: ClinicalTrials.gov Identifier: NCT05476978.

Extraskeletal osteosarcoma infiltrating pancreas, spleen, gastric, and left kidney: a case report.

BACKGROUND: Extraskeletal osteosarcoma is an extremely rare malignancy that accounts for 1% of soft tissue sarcoma and 4.3% of all osteosarcoma. Extraskeletal osteosarcoma can develop in a patient between the ages of 48 and 60 years. The incidence of extraskeletal osteosarcoma is slightly higher in male patients than in females. CASE PRESENTATION: A 50-year-old Caucasian male patient presented with a 6-month history of intermittent lower-left back pain that limits his activity. Prior ultrasonography and abdominal computed tomography scan showed a diagnosis of kidney stone and tumor in the lower-left abdomen. The computed tomography urography with contrast revealed a mass suspected as a left retroperitoneal malignant tumor. Hence, the tumor was resected through laparotomy and the patient continued with histopathological and immunohistochemistry examination with the result of extraskeletal osteosarcoma. CONCLUSION: Extraskeletal osteosarcoma presents diagnostic challenges requiring multimodal examination, including histological and immunohistochemistry analyses. This case underscores the aggressive nature and poor prognosis despite undergoing the current suggested treatment.

68Ga-NODAGA-Exendin-4 PET/CT Findings in a Case of Metastatic Insulinoma With Refractory Hypoglycemia Controlled by 90Y Transarterial Radioembolization.

ABSTRACT: Metastatic insulinomas can cause recurrent hypoglycemia requiring continuous IV glucose infusion. Various medical and chemotherapeutic treatment options are used to reduce the patient's risk of death due to hypoglycemia. Treatment-resistant hepatic metastatic insulinomas may benefit clinically from 90Y transarterial radioembolization therapy. In this case, we present a case of liver metastatic insulinoma that achieved clinical improvement after 2 cycles of 90Y microspheres transarterial radioembolization, and the presence of active metastases was demonstrated with 68Ga-NODAGA-exendin-4 PET/CT imaging.

Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer.

Background Structured radiology reports for pancreatic ductal adenocarcinoma (PDAC) improve surgical decision-making over free-text reports, but radiologist adoption is variable. Resectability criteria are applied inconsistently. Purpose To evaluate the performance of large language models (LLMs) in automatically creating PDAC synoptic reports from original reports and to explore performance in categorizing tumor resectability. Materials and Methods In this institutional review board-approved retrospective study, 180 consecutive PDAC staging CT reports on patients referred to the authors' European Society for Medical Oncology-designated cancer center from January to December 2018 were included. Reports were reviewed by two radiologists to establish the reference standard for 14 key findings and National Comprehensive Cancer Network (NCCN) resectability category. GPT-3.5 and GPT-4 (accessed September 18-29, 2023) were prompted to create synoptic reports from original reports with the same 14 features, and their performance was evaluated (recall, precision, F1 score). To categorize resectability, three prompting strategies (default knowledge, in-context knowledge, chain-of-thought) were used for both LLMs. Hepatopancreaticobiliary surgeons reviewed original and artificial intelligence (AI)-generated reports to determine resectability, with accuracy and review time compared. The McNemar test, t test, Wilcoxon signed-rank test, and mixed effects logistic regression models were used where appropriate. Results GPT-4 outperformed GPT-3.5 in the creation of synoptic reports (F1 score: 0.997 vs 0.967, respectively). Compared with GPT-3.5, GPT-4 achieved equal or higher F1 scores for all 14 extracted features. GPT-4 had higher precision than GPT-3.5 for extracting superior mesenteric artery involvement (100% vs 88.8%, respectively). For categorizing resectability, GPT-4 outperformed GPT-3.5 for each prompting strategy. For GPT-4, chain-of-thought prompting was most accurate, outperforming in-context knowledge prompting (92% vs 83%, respectively; P = .002), which outperformed the default knowledge strategy (83% vs 67%, P < .001). Surgeons were more accurate in categorizing resectability using AI-generated reports than original reports (83% vs 76%, respectively; P = .03), while spending less time on each report (58%; 95% CI: 0.53, 0.62). Conclusion GPT-4 created near-perfect PDAC synoptic reports from original reports. GPT-4 with chain-of-thought achieved high accuracy in categorizing resectability. Surgeons were more accurate and efficient using AI-generated reports. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Optimization of Endoscopic Ultrasound Characteristics in the Diagnosis of Malignant Intraductal Papillary Mucinous Neoplasm.

OBJECTIVES: Endoscopic ultrasound (EUS) is an excellent diagnostic tool that provides high-resolution images of pancreatic cystic lesions. However, its role in the diagnosis of malignant intraductal papillary mucinous neoplasm (IPMN) remains limited and unclear. We aimed to determine the usefulness of this modality for such diagnosis. METHODS: Overall, 246 patients who underwent EUS for IPMN after computed tomography (CT)/magnetic resonance imaging (MRI) from April 2018 to June 2021 were followed up until March 2022. We assessed the added value of performing EUS after CT or MRI for diagnosing malignant IPMN, using receiver operating characteristic curve analysis. Walls as thick as 2 mm were considered thickened in this study if they were highly uneven. RESULTS: EUS clearly enhanced accuracy in identifying enhancing nodules and thickened walls. The areas under the receiver operating characteristic curve and corresponding 95% confidence intervals were 0.655 (0.549-0.760) and 0.566 (0.478-0.654) upon CT/MRI but 0.853 (0.763-0.942) and 0.725 (0.634-0.817) when observed using EUS. The combination of nodule size, thickened wall, and main duct size yielded the highest area under the receiver operating characteristic curve (0.944 [0.915-0.973]). CONCLUSIONS: EUS more accurately detects malignant IPMN, as uneven wall thickening and certain nodules cannot be identified with CT/MRI.

Dilated common bile duct is commonly associated with main duct Intraductal Papillary Mucinous Neoplasm of the pancreas.

BACKGROUND: Dilatation of common bile duct (CBD) is mostly pathological and mainly occurs secondary to mechanical causes. We aimed to explore the prevalence of CBD dilatation in Intraductal Papillary Mucinous Neoplasms of the pancreas (IPMN) among patients referred to EUS. METHODS: A retrospective study of all patients who had an EUS diagnosis of IPMN from 2011 to 2019 at Galilee Medical Center were extracted. Control group including patients with other types of pancreatic cysts. RESULTS: Overall, 2400 patients were included in the study, of them 158 patients were diagnosed with pancreatic cysts, 117 patients (74%) diagnosed with IPMN (group A), and 41 patients (26%) diagnosed with other pancreatic cysts (group B). Univariate analysis showed significant association of IPMN (OR 3.8, 95% CI 1.3-11.5), resected gallbladder (GB) (OR 7.75, 95% CI 3.19-18.84), and age (OR 1, 95% CI 1.01-1.08) with CBD dilatation. Classifying IPMN to sub-groups using adjusted multivariate logistic regression analysis, only main duct-IPMN (MD-IPMN) significantly correlated with CBD dilatation compared to branch duct (BD)-IPMN and mixed type-IPMN (OR 19.6, 95% CI 4.57-83.33, OR 16.3, 95% CI 3.02-88.08). CONCLUSION: MD-IPMN was significantly correlated with dilated CBD. Assessment of the pancreas is warranted in encountered cases of dilated CBD without obvious mechanical cause.

SCANPatient: study protocol for a multi-centre, batched, stepped wedge, comparative effectiveness, randomised clinical trial of synoptic reporting of computerised tomography (CT) scans assessing cancers of the pancreas.

BACKGROUND: Complete surgical removal of pancreatic ductal adenocarcinoma (PDAC) is central to all curative treatment approaches for this aggressive disease, yet this is only possible in patients technically amenable to resection. Hence, an accurate assessment of whether patients are suitable for surgery is of paramount importance. The SCANPatient trial aims to test whether implementing a structured synoptic radiological report results in increased institutional accuracy in defining surgical resectability of non-metastatic PDAC. METHODS: SCANPatient is a batched, stepped wedge, comparative effectiveness, cluster randomised clinical trial. The trial will be conducted at 33 Australian hospitals all of which hold regular multi-disciplinary team meetings (MDMs) to discuss newly diagnosed patients with PDAC. Each site is required to manage a minimum of 20 patients per year (across all stages). Hospitals will be randomised to begin synoptic reporting within a batched, stepped wedge design. Initially all hospitals will continue to use their current reporting method; within each batch, after each 6-month period, a randomly selected group of hospitals will commence using the synoptic reports, until all hospitals are using synoptic reporting. Each hospital will provide data from patients who (i) are aged 18 or older; (ii) have suspected PDAC and have an abdominal CT scan, and (iii) are presented at a participating MDM. Non-metastatic patients will be documented as one of the following categories: (1) locally advanced and surgically unresectable; (2) borderline resectable; or (3) anatomically clearly resectable (Note: Metastatic disease is treated as a separate category). Data collection will last for 36 months in each batch, and a total of 2400 patients will be included. DISCUSSION: Better classifying patients with non-metastatic PDAC as having tumours that are either clearly resectable, borderline or locally advanced and unresectable may improve patient outcomes by optimising care and treatment planning. The borderline resectable group are a small but important cohort in whom surgery with curative intent may be considered; however, inconsistencies with definitions and an understanding of resectability status means these patients are often incorrectly classified and hence overlooked for curative options. TRIAL REGISTRATION: The SCANPatient trial was registered on 17th May 2023 in the Australian New Zealand Clinical Trials Registry (ANZCTR) (ACTRN12623000508673).

Predicting Recurrence-Free Survival After Upfront Surgery in Resectable Pancreatic Ductal Adenocarcinoma: A Preoperative Risk Score Based on CA 19-9, CT, and 18F-FDG PET/CT.

OBJECTIVE: To develop and validate a preoperative risk score incorporating carbohydrate antigen (CA) 19-9, CT, and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT variables to predict recurrence-free survival (RFS) after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Patients with resectable PDAC who underwent upfront surgery between 2014 and 2017 (development set) or between 2018 and 2019 (test set) were retrospectively evaluated. In the development set, a risk-scoring system was developed using the multivariable Cox proportional hazards model, including variables associated with RFS. In the test set, the performance of the risk score was evaluated using the Harrell C-index and compared with that of the postoperative pathological tumor stage. RESULTS: A total of 529 patients, including 335 (198 male; mean age ± standard deviation, 64 ± 9 years) and 194 (103 male; mean age, 66 ± 9 years) patients in the development and test sets, respectively, were evaluated. The risk score included five variables predicting RFS: tumor size (hazard ratio [HR], 1.29 per 1 cm increment; P < 0.001), maximal standardized uptake values of tumor ≥ 5.2 (HR, 1.29; P = 0.06), suspicious regional lymph nodes (HR, 1.43; P = 0.02), possible distant metastasis on 18F-FDG PET/CT (HR, 2.32; P = 0.03), and CA 19-9 (HR, 1.02 per 100 U/mL increment; P = 0.002). In the test set, the risk score showed good performance in predicting RFS (C-index, 0.61), similar to that of the pathologic tumor stage (C-index, 0.64; P = 0.17). CONCLUSION: The proposed risk score based on preoperative CA 19-9, CT, and 18F-FDG PET/CT variables may have clinical utility in selecting high-risk patients with resectable PDAC.

Diagnostic Assessment of Endoscopic Ultrasonography-Fine Needle Aspiration Cytology in the Pancreas: A Comparison between Liquid-Based Preparation and Conventional Smear.

Background and Objectives: This study aimed to elucidate the cytologic characteristics and diagnostic usefulness of endoscopic ultrasonography-fine needle aspiration cytology (EUS-FNAC) by comparing it with liquid-based preparation (LBP) and conventional smear (CS) in pancreas. Methods: The diagnostic categories (I through VII) were classified according to the World Health Organization Reporting System for Pancreaticobiliary Cytopathology. Ten cytologic features, including nuclear and additional features, were evaluated in 53 cases subjected to EUS-FNAC. Nuclear features comprised irregular nuclear contours, nuclear enlargement, hypochromatic nuclei with parachromatin clearing, and nucleoli. Additional cellular features included isolated atypical cells, mucinous cytoplasm, drunken honeycomb architecture, mitosis, necrotic background, and cellularity. A decision tree analysis was conducted to assess diagnostic efficacy. Results: The diagnostic concordance rate between LBP and CS was 49.1% (26 out of 53 cases). No significant differences in nuclear features were observed between categories III (atypical), VI (suspicious for malignancy), and VII (malignant). The decision tree analysis of LBP indicated that cases with moderate or high cellularity and mitosis could be considered diagnostic for those exhibiting nuclear atypia. Furthermore, in CS, mitosis, isolated atypical cells, and necrotic background exerted a more significant impact on the diagnosis of EUS-FNAC. Conclusions: Significant parameters for interpreting EUS-FNAC may differ between LBP and CS. While nuclear atypia did not influence the diagnosis of categories III, VI, and VII, other cytopathologic features, such as cellularity, mitosis, and necrotic background, may present challenges in diagnosing EUS-FNAC.

Extranodal diffuse large B-cell lymphoma presenting with extensive organ involvement.

Extranodal involvement in diffuse large B-cell lymphoma (DLBCL) is defined as disease outside of the lymph nodes and occurs in up to one-third of patients, though multiorgan extranodal involvement is rare. Here, we describe a case of a patient presenting with widely metastatic lesions, including involvement of the lung, parotid gland, breast, pancreas, femur and multiple soft tissue masses, with initial concern for primary breast malignancy. Breast pathology and imaging were consistent with triple-expressor, double-hit stage IV high-grade B-cell lymphoma with extensive extranodal involvement. Extranodal involvement is a poor prognostic factor associated with high rates of treatment failure, and novel therapies targeting CD19 are currently being studied for relapsed and refractory DLBCL. Extranodal disease is a complex entity that can involve virtually any organ system and should be considered for new presentations of malignancy.

Diagnostics and Imaging for Pancreatic Neuroendocrine Tumors.

Pancreatic neuroendocrine tumors originate from hormone-producing islet cells and have a propensity to metastasize to the liver once they reach 2 cm in size. Their diagnosis relies upon a combination of computed tomography, MRI, DOTATATE PET, and endoscopic ultrasound with or without tissue biopsy. Biochemical work-up is driven by patient symptoms of hormone excess.