A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

[A rare case of intraductal tumor of the pancreas in which an intraductal tubulopapillary neoplasm was mixed with a widely spreading gastric-type intraductal papillary-mucinous neoplasm].

A 70-year-old man receiving treatment for diabetes mellitus presented with a cystic mass in the border area of the pancreatic body and tail on plain computed tomography (CT) due to impaired glucose intolerance. Contrast-enhanced CT showed a faint hyperattenuated nodular mass extending from the dilated main pancreatic duct (MPD) to the branch duct. Endoscopic retrograde cholangiopancreatography revealed a mildly dilated orifice of the papilla of Vater and MPD stenosis with entire upstream and immediate downstream dilatations. The patient underwent distal pancreatectomy due to the suspicion of mixed-type intraductal papillary-mucinous carcinoma. A pathological examination showed an intraductal solid-nodular mass measuring 25mm in length, consisting of two types of neoplasms. One showed tubulopapillary growth with entirely high-grade (HG) atypical cuboidal epithelium, in which immunohistochemical examinations were positive for MUC6 but negative for human gastric mucin (HGM), MUC1, MUC2, and MUC5AC, fitting the concept of intraductal tubulopapillary neoplasm (ITPN). The other showed the same growth of low-grade (LG) atypical columnar cells positive for HGM and MUC5AC and negative for MUC1 and MUC2, which corresponded to gastric-type intraductal papillary-mucinous neoplasm (IPMN) -LG. The tumor had not invaded the duct walls, and no metastatic lymph nodes were observed. The ITPN was adjacent to the IPMN mainly composed of tubular glands mimicking pyloric glands with LG dysplasia that corresponded to the so-called IPMN-pyloric gland variant. Moreover, the proliferation of low-papillary gastric-type IPMN spread around the intraductal tumors. Consequently, the patient was diagnosed with an intraductal tubular neoplasm comprising a noninvasive ITPN and gastric-type IPMN-LG. ITPN is a recently identified intraductal neoplasm of the pancreas proposed by Yamaguchi et al. and is distinguished by intraductal tubulopapillary growth with HG cellular atypia without overt mucin production, in contrast to IPMN. To date, no cases of intraductal nodular tumors comprising ITPN and IPMN have been reported. We report this original case with imaging and pathological observations and discuss potential processes via which ITPN and IPMN may arise adjacent to each other in the same pancreatic duct.

NIR Fluorescent/Photoacoustic Bimodal Imaging of Ferroptosis in Pancreatic Cancer Using Biothiols-Activable Probes.

Ferroptosis modulation is a powerful therapeutic option for pancreatic ductal adenocarcinoma (PDAC) with a low 5-year survival rate and lack of effective treatment methods. However, due to the dual role of ferroptosis in promoting and inhibiting pancreatic tumorigenesis, regulating the degree of ferroptosis is very important to obtain the best therapeutic effect of PDAC. Biothiols are suitable as biomarkers of imaging ferroptosis due to the dramatic decreases of biothiol levels in ferroptosis caused by the inhibited synthesis pathway of glutathione (GSH) and the depletion of biothiol by reactive oxygen species. Moreover, a very recent study reported that cysteine (Cys) depletion can lead to pancreatic tumor ferroptosis in mice and may be employed as an effective therapeutic strategy for PDAC. Therefore, visualization of biothiols in ferroptosis of PDAC will be helpful for regulating the degree of ferroptosis, understanding the mechanism of Cys depletion-induced pancreatic tumor ferroptosis, and further promoting the study and treatment of PDAC. Herein, two biothiol-activable near-infrared (NIR) fluorescent/photoacoustic bimodal imaging probes (HYD-BX and HYD-DX) for imaging of pancreatic tumor ferroptosis were reported. These two probes show excellent bimodal response performances for biothiols in solution, cells, and tumors. Subsequently, they have been employed successfully for real-time visualization of changes in concentration levels of biothiols during the ferroptosis process in PDAC cells and HepG2 cells. Most importantly, they have been further applied for bimodal imaging of ferroptosis in pancreatic cancer in mice, with satisfactory results. The development of these two probes provides new tools for monitoring changes in concentration levels of biothiols in ferroptosis and will have a positive impact on understanding the mechanism of Cys depletion-induced pancreatic tumor ferroptosis and further promoting the study and treatment of PDAC.

Short- and long-term outcomes after distal pancreatectomy with radiologic infiltration of splenic vessels for pancreatic ductal adenocarcinoma.

BACKGROUND: The effect of radiologic splenic vessels involvement (RSVI) on the survival of patients with pancreatic adenocarcinoma (PAC) located in the body and tail of the pancreas is controversial, and its influence on postoperative morbidity after distal pancreatectomy (DP) is unknown. This study aimed to determine the influence of RSVI on postoperative complications, overall survival (OS), and disease-free survival (DFS) in patients undergoing DP for PAC. METHODS: A multicenter retrospective study of DP was conducted at 7 hepatopancreatobiliary units between January 2008 and December 2018. Patients were classified according to the presence of RSVI. A Clavien-Dindo grade of >II was considered to represent a major complication. RESULTS: A total of 95 patients were included in the analysis. Moreover, 47 patients had vascular infiltration: 4 had arterial involvement, 10 had venous involvement, and 33 had both arterial and venous involvements. The rates of major complications were 20.8% in patients without RSVI, 40.0% in those with venous RSVI, 25.0% in those with arterial RSVI, and 30.3% in those with both arterial and venous RSVIs (P = .024). The DFS rates at 3 years were 56% in the group without RSVI, 50% in the group with arterial RSVI, and 16% in the group with both arterial and venous RSVIs (P = .003). The OS rates at 3 years were 66% in the group without RSVI, 50% in the group with arterial RSVI, and 29% in the group with both arterial and venous RSVIs (P < .0001). CONCLUSION: RSVI increased the major complication rates after DP and reduced the OS and DFS. Therefore, it may be a useful prognostic marker in patients with PAC scheduled to undergo DP and may help to select patients likely to benefit from neoadjuvant treatment.

CT findings and clinical effects of high grade pancreatic intraepithelial neoplasia in patients with intraductal papillary mucinous neoplasms.

PURPOSE: To investigate the common CT findings of high-grade (HG) PanIN and clinical effects in the remnant pancreas in patients with intraductal papillary mucinous neoplasm (IPMN) of the pancreas. MATERIALS AND METHODS: Two hundred fifty-one patients with surgically confirmed IPMNs (118 malignant [invasive carcinoma/high-grade dysplasia] and 133 benign [low-grade dysplasia]) were retrospectively enrolled. The grade of PanIN (233 absent/low-grade and 18 high-grade) was recorded, and all patients underwent serial CT follow-up before and after surgery. Two radiologists analyzed CT findings of high-risk stigmata or worrisome features according to 2017 international consensus guidelines. They also analyzed tumor recurrence on serial follow-up CT after surgery. Statistical analyses were performed to identify significant predictors and clinical impact on postoperative outcomes of HG PanIN. RESULTS: PanIN grade showed a significant association with IPMN grade (p = 0.012). Enhancing mural nodules ≥5 mm, abrupt main pancreatic duct (MPD) changes with distal pancreatic atrophy, increased mural nodule size and MPD diameter were common findings in HG PanIN (P<0.05). In multivariate analysis, abrupt MPD change with distal pancreatic atrophy (odds ratio (OR) 6.59, 95% CI: 2.32-18.72, <0.001) and mural nodule size (OR, 1.05; 95% CI, 1.02-1.08, 0.004) were important predictors for HG PanIN. During postoperative follow-up, HG PanIN (OR, 4.98; 95% CI, 1.22-20.33, 0.025) was significantly associated with cancer recurrence in the remnant pancreas. CONCLUSION: CT can be useful for predicting HG PanIN using common features, such as abrupt MPD changes and mural nodules. In HG PanIN, extra caution is needed to monitor postoperative recurrence during follow-up.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment. METHODS: This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts. RESULTS: Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model. CONCLUSIONS: The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma.

OBJECTIVE: To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016-October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter. RESULTS: Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915-0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967-0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant. CONCLUSION: Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model.

KRAS Mutation Detection with (2S,4R)-4-[18F]FGln for Noninvasive PDAC Diagnosis.

Pancreatic ductal adenocarcinoma (PDAC), which has a poor prognosis and nonspecific symptoms and progresses rapidly, is the most common pancreatic cancer type. Inhibitors targeting KRAS G12D and G12C mutations have been pivotal in PDAC treatment. Cancer cells with different KRAS mutations exhibit various degrees of glutamine dependency; in particular, cells with KRAS G12D mutations exhibit increased glutamine uptake. (2S,4R)-4-[18F]FGln has recently been developed for clinical cancer diagnosis and tumor cell metabolism analysis. Thus, we verified the heterogeneity of glutamine dependency in PDAC models with different KRAS mutations by a visual and noninvasive method with (2S,4R)-4-[18F]FGln. Two tumor-bearing mouse models (bearing the KRAS G12D or G12C mutation) were injected with (2S,4R)-4-[18F]FGln, and positron emission tomography (PET) imaging features and biodistribution were observed and analyzed. The SUVmax in the regions of interest (ROI) was significantly higher in PANC-1 (G12D) tumors than in MIA PaCa-2 (G12C) tumors. Biodistribution analysis revealed higher tumor accumulation of (2S,4R)-4-[18F]FGln and other metrics, such as T/M and T/B, in the PANC-1 mouse models compared to those in the MIAPaCa-2 mouse models. In conclusion, PDAC cells with the KRAS G12D and G12C mutations exhibit various degrees of (2S,4R)-4-[18F]FGln uptake, indicating that (2S,4R)-4-[18F]FGln might be applied to detect KRAS G12C and G12D mutations and provide treatment guidance.

JS-K Combined with a Melanin-Based Theranostic Agent: A Novel Sequential Delivery Strategy to Enhance the Near-Infrared Fluorescence Imaging of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5 year survival rate less than 12%. This malignancy is closely related to the unique tumor microenvironment (TME), which is characterized by a hypovascular and hyperdense extracellular matrix, making it difficult for drugs to permeate the tumor center. Near-infrared fluorescence (NIRF) imaging, which has high sensitivity and resolution, may improve the survival rate of PDAC patients. In this study, we first used JS-K (O2-(2,4-dinitrophenyl) 1-[(4-ethoxycarbonyl) piperazine-1-yl] diazene-1-ium-1,2-diolate) to specifically dilate blood vessels within the TME of PDAC patients and subsequently injected IR820-PEG-MNPs (IPM NPs) to diagnose and treat orthotopic PDAC. We found that JS-K promoted the accumulation of IPM NPs in orthotopic Pan02 tumor-bearing mice and was able to increase the tumor signal-to-background ratio (SBR) in the orthotopic PDAC area by 41.5%. In addition, surgical navigation in orthotopic Pan02 tumor-bearing mice and complete tumor resection based on fluorescence imaging were achieved with a detection sensitivity of 81.0%. Moreover, we verified the feasibility of the combination of laparoscopy and photothermal ablation (PTA) for the treatment of PDAC. Finally, we demonstrated that IPM NPs had greater affinity for human PDAC tissues than for normal pancreatic tissues ex vivo, preliminarily highlighting the potential for clinical translation of these NPs. In conclusion, we developed and validated a novel sequential delivery strategy that promotes the accumulation of nanoagents in the tumor area and can be used for the diagnosis and treatment of PDAC.

Effect of X-ray tube on image quality and pancreatic ductal adenocarcinoma conspicuity in pancreatic protocol dual-energy CT.

AIM: To compare the radiation dose, image quality, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic protocol dual-energy computed tomography (CT) between two X-ray tubes mounted in the same CT machine. MATERIAL AND METHODS: This retrospective study comprised 80 patients (median age, 73 years; 45 men) who underwent pancreatic protocol dual-energy CT from January 2019 to March 2022 using either old (Group A, n=41) or new (Group B, n=39) X-ray tubes mounted in the same CT machine. The imaging parameters were completely matched between the two groups, and CT data were reconstructed at 70 and 40 keV. The CT dose-index volume (CTDIvol); CT attenuation of the abdominal aorta, pancreas, and PDAC; background noise; and qualitative scores for the image noise, overall image quality, and PDAC conspicuity were compared between the two groups. RESULTS: The CTDIvol was lower in Group B than Group A (7.9 versus 9.2 mGy; p<0.001). The CT attenuation of all anatomical structures at 70 and 40 keV was comparable between the two groups (p=0.06-0.78). The background noise was lower in Group B than Group A (12 versus 14 HU at 70 keV, p=0.046; and 26 versus 30 HU at 40 keV, p<0.001). Qualitative scores for image noise and overall image quality at 70 and 40 keV and PDAC conspicuity at 40 keV were higher in Group B than Group A (p<0.001-0.045). CONCLUSION: The latest X-ray tube could reduce the radiation dose and improve image quality in pancreatic protocol dual-energy CT.

Does CT overestimate extra-pancreatic perineural invasion in patients with pancreatic ductal adenocarcinoma following neoadjuvant chemoradiation therapy?

OBJECTIVES: To evaluate the diagnostic performance of CT in the assessment of extra-pancreatic perineural invasion (EPNI) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective study included 123 patients (66 men; median age, 66 years) with PDAC who underwent radical surgery and pancreatic protocol CT for assessing surgical resectability between September 2011 and March 2019. Among the 123 patients, 97 patients had received neoadjuvant chemoradiation therapy (CRT). Two radiologists reviewed the CT images for evidence of EPNI using a 5-point scale (5 = definitely present, 4 = probably present, 3 = equivocally present, 2 = probably absent, and 1 = definitely absent). Diagnostic performance for assessing EPNI was evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS: The sensitivity, specificity, and area under the ROC curve for assessing EPNI were 98%, 30%, and 0.62 in all patients; 97%, 22%, and 0.59 in patients with neoadjuvant CRT; and 100%, 100%, and 1.00 in patients without neoadjuvant CRT, respectively. False-positive assessment of EPNI occurred in 23% of patients (n = 28/123), and 100% of these (n = 28/28) had received neoadjuvant CRT. There was moderate to substantial agreement between the readers (ĸ = 0.49-0.62). CONCLUSION: Pancreatic protocol CT has better diagnostic performance for determination of EPNI in treatment naïve patients with PDAC and overestimation of EPNI is likely in patients who have received preoperative CRT. ADVANCES IN KNOWLEDGE: Pancreatic protocol CT has better diagnostic performance for the detection of EPNI in treatment naïve patients compared to patients receiving neoadjuvant CRT.

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma.

PURPOSE: To predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT). METHODS: Totally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade. RESULTS: Necrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13-7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98-17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07-1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%). CONCLUSION: Variables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

BACKGROUND: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION: The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer.

OBJECTIVE: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. METHODS: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). RESULTS: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. CONCLUSIONS: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. RELEVANCE STATEMENT: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. KEY POINTS: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments.

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

The Role of Intraoperative Pancreatoscopy in the Surgical Management of Intraductal Papillary Mucinous Neoplasms: A Scoping Review.

OBJECTIVES: Most patients with intraductal papillary mucinous neoplasms (IPMNs) are diagnosed with a solitary lesion; however, the presence of skip lesions, not appreciable on imaging, has been described. Postoperatively, these missed lesions can continue to grow and potentially become cancerous. Intraoperative pancreatoscopy (IOP) may facilitate detection of such skip lesions in the remnant gland. The aim of this scoping review was to appraise the evidence on the role of IOP in the surgical management of IPMNs. MATERIALS AND METHODS: Studies reporting on the use of IOP during IPMN surgery were identified through searches of the PubMed, Embase, and Scopus databases. Data extracted included IOP findings, surgical plan modifications, and patient outcomes. The primary outcome of interest was the utility of IOP in surgical decision making. RESULTS: Ten studies reporting on the use of IOP for IPMNs were identified, representing 147 patients. A total of 46 skip lesions were identified by IOP. Overall, surgical plans were altered in 37% of patients who underwent IOP. No IOP-related complications were reported. CONCLUSIONS: The current literature suggests a potential role of integration of IOP into the management of patients with IPMNs. This tool is safe and feasible and can result in changes in surgical decision making.

International evidence-based Kyoto guidelines for the management of intraductal papillary mucinous neoplasm of the pancreas.

This study group aimed to revise the 2017 international consensus guidelines for the management of intraductal papillary mucinous neoplasm (IPMN) of the pancreas, and mainly focused on five topics; the revision of high-risk stigmata (HRS) and worrisome features (WF), surveillance of non-resected IPMN, surveillance after resection of IPMN, revision of pathological aspects, and investigation of molecular markers in cyst fluid. A new development from the prior guidelines is that systematic reviews were performed for each one of these topics, and published separately to provide evidence-based recommendations. One of the highlights of these new "evidence-based guidelines" is to propose a new management algorithm, and one major revision is to include into the assessment of HRS and WF the imaging findings from endoscopic ultrasound (EUS) and the results of cytological analysis from EUS-guided fine needle aspiration technique, when this is performed. Another key element of the current guidelines is to clarify whether lifetime surveillance for small IPMNs is required, and recommends two options, "stop surveillance" or "continue surveillance for possible development of concomitant pancreatic ductal adenocarcinoma", for small unchanged BD-IPMN after 5 years surveillance. Several other points are also discussed, including identifying high-risk features for recurrence in patients who underwent resection of non-invasive IPMN with negative surgical margin, summaries of the recent observations in the pathology of IPMN. In addition, the emerging role of cyst fluid markers that can aid in distinguishing IPMN from other pancreatic cysts and identify those IPMNs that harbor high-grade dysplasia or invasive carcinoma is discussed.

Early Cancerous Lesions of the Pancreas and Ampulla: Current Concepts and Challenges.

Owing to the increased use of advanced imaging techniques, mass-forming (cystic/intraductal) preinvasive neoplasms are being detected much more frequently and they have rapidly become one of the main focuses of interests in medical field. These neoplasms have very distinctive clinical and radiographic findings, exhibit a spectrum of dysplastic transformation, from low-grade dysplasia to high-grade dysplasia, and may be associated with an invasive carcinoma. Accounting for about 5% to 10% of pancreatic ductal adenocarcinomas, they provide a curable target subset in an otherwise biologically dismal pancreas cancer category.