Introduction

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas are the most frequently identified precursor lesions of pancreatic cancer (PDAC). Only a minority progress towards malignancy, making their identification a critical point in cancer prevention. The available guidelines are imprecise, commonly resulting in over treatment of indolent lesions.

Aims & Methods

The aim of this study (EMI-IPMN, NCT06706700) is to discover novel biological and radiological biomarkers that can distinguish malignant from benign IPMNs. We focused on Endocrine, Metabolic and Inflammatory (EMI) factors, some pathways that are characterized in PDAC but poorly investigated in IPMN progression.
Patients with a presumed diagnosis of IPMN with indication for surgical resection who underwent high-quality magnetic resonance imaging (MRI) and/or computed tomography (CT), were prospectively enrolled. Peripheral blood (PB) and cyst fluid (CF) samples were collected during surgery, and lesions were histologically classified as IPMN with low-grade dysplasia (LGD), high-grade dysplasia (HGD), or invasive cancer (IC). Malignant IPMNs were defined as those with HGD and IC. Cytokine and hormone levels were measured in both fluids using multiplex assays and compared across the diagnostic groups. Continuous variables were converted into categorical variables based on optimal cut-off values, and both univariate and multivariate logistic regression analyses were conducted.

Results

Among the 205 patients enrolled, 133 (58 females, 75 males, median age 69.2) had a histological diagnosis of IPMN: 49 (36.8%) had LGD, 44 (33.1%) HGD and 40 (30.1%) had IC. Analysis of the EMI profile revealed higher concentrations of several markers (IL-8, VEGF, GLP-1, glucagon, and C-peptide) in CF compared to PB. Specific cytokines such as MCP-1 IP-10, TNFα, and IL-8 were significantly elevated in the CF of HGD/IC cases compared to LGD (p=0.05, 0.041, 0.001 and 0.025 respectively). Similarly, IP-10, IL-8, and MDC levels in PB were higher in HGD/IC patients (p=0.006, 0.008 and 0.004 respectively).
Pancreatic fat content, assessed with mDixon Quant Fat Fraction MRI sequences, was notably higher in HGD/IC patients (13.89%) compared to LGD patients (8.38%, p<0.05), and positively correlated with MCP-1 levels in CF.
Among these biological and radiological markers TNFα in the CF demonstrated a substantial diagnostic accuracy for identifying malignant IPMN, with an area under the ROC curve (AUC) of 0.72. In the final multivariable analysis we demonstrate that high TNFα levels in CF and HbA1c were the two independent risk factors for the identification of malignant IPMN (OR 10.8 and 1.11 respectively).

Conclusion

The progression of IPMN to PDAC is associated with a proinflammatory shift in the tumor microenvironment, reflected by local (CF) and systemic (PB) elevations in key cytokines (TNFα, MCP-1, and IL-8) whose involvement in PDAC progression is well established. Elevated hormone levels in CF, along with the strong association between malignant IPMN and HbA1c, underscore the critical role of endocrine alterations in the early stages of pancreatic tumorigenesis. These findings may be harnessed to enable earlier detection of malignant transformation in IPMN. Moreover, the correlation between imaging markers and biological profiles suggests that MRI could serve as non-invasive proxies for underlying pathological changes. Together, these results offer new insights into the pathophysiology of IPMN progression and may imporve diagnostic strategies

Introduction

Early diagnosis of pancreatic cancer remains difficult due to the lack of overt findings on imaging. Our prior analysis revealed that indirect signs such as pancreatic atrophy and ductal stenosis or dilation often precede tumor visibility. In collaboration with Fujifilm Corporation, we developed an AI-based system capable of detecting pancreatic masses, atrophy, and ductal abnormalities on contrast-enhanced CT(CE-CT) and non-contrast CT(NC-CT). This study evaluated its diagnostic performance.

Aims & Methods

The AI algorithm extracts the pancreatic parenchyma, ducts, and tumor regions from CT images and classifies the presence of four imaging findings (pancreatic mass, atrophy, ductal dilation, and ductal stenosis). The model was trained and validated using approximately 2,000 images. For testing, we used an external dataset comprising 90 pancreatic cancer cases (Tis/T1: T2: T3–T4 = 1:1:1) and 100 controls collected from affiliated institutions. Reference standard for each imaging findings was established by two expert radiologists. Diagnosis of pancreas cancer was also performed using the logistic regression model with the results of each imaging finding. Diagnostic performances were performed on CE-CT and NC-CT, and AI diagnostic accuracy and reading time were compared with those of four radiologists and two gastroenterologists.

Results

For the diagnosis of pancreas cancer, the AI showed a sensitivity of 98.9% and specificity of 93.0% on CE-CT, and sensitivity of 83.9% and specificity of 92.0% on NC-CT. The system successfully detected early-stage cancers, including Tis and T1, by identifying indirect imaging findings. Detection metrics for CE-CT vs. NC-CT were as follows: mass (sensitivity 97.2% vs. 87.5%; specificity 74.6% vs. 71.4%), atrophy (74.0% vs. 59.6%; 90.7% vs. 92.9%), ductal dilation (78.8% vs. 90.5%; 96.4% vs. 88.3%), and ductal stenosis (87.7% vs. 93.9%; 90.4% vs. 85.1%).
Physicians showed mean sensitivities/specificities of 88.9%/99.3% on CE-CT and 53.4%/99.5% on NC-CT. Mean reading time per case was 22 seconds (AI) vs. 63 seconds (physicians) for CE-CT and 20 seconds (AI) vs. 78 seconds (physicians) for NC-CT.

Conclusion

The AI system demonstrated diagnostic performance comparable to that of experienced radiologists and significantly reduced reading time. While its specificity was somewhat lower than that of physicians, the system effectively identified early-stage pancreatic cancers using secondary findings. At present, it may be best applied as a second-reader support tool in radiological interpretation. Further refinement may expand its role in early detection and possibly future screening.

Disclosure

N.H., A.O., and M.T. are employees of Fujifilm Corporation.
A.M., K.S., T.M., and Y.K. declare financial conflicts of interest with Fujifilm Corporation.

Introduction

Computer-aided diagnosis (CAD) systems are widely implemented in the medical field for the analysis of medical images. PolyDeep is a CAD system that has demonstrated its efficacy in lesion detection.

Aims & Methods

The aim of this study is to determine whether PolyDeep increases the adenoma detection rate (ADR) in the context of expert endoscopists participating in a regional, population-based colorectal cancer screening program in Spain. PolyDeep Advance 3 (NCT05513261) is a multicentric, randomized controlled clinical trial. For the primary endpoint, we aimed to assess whether the ADR is superior in PolyDeep-assisted colonoscopy compared to conventional colonoscopy. As secondary endpoints, we aimed to determine whether PolyDeep-assisted colonoscopy improves the polyp detection rate (PDR), serrated lesion detection rate (SLDR), advanced serrated lesion detection rate (ASLDR), and advanced adenoma detection rate (AADR). We included participants who underwent colonoscopy for screening (i.e. those with a positive faecal immunochemical occult blood test – FIT+) or for surveillance after resection of advanced adenomas. We compared both groups using the chi-square and t-Student test.

Results

Between November 2023 and May 2024, we recruited 857 patients, of whom 30 were excluded. The baseline characteristics of the 827 patients included in the analysis were similar (male: 59.3%, screening colonoscopy: 66.7%, mean age: 61.7 ± 6.5 years, withdrawal time: 10:49 ± 04:32 minutes, colorectal cancer incidence: 2.8%). However, there were significant differences between both groups in the Boston Bowel Preparation Scale (conventional colonoscopy: 7.7 ± 1.3 vs. PolyDeep-assisted colonoscopy: 7.4 ± 1.3, p = 0.01). Regarding ADR, we did not find statistically significant differences between conventional and PolyDeep-assisted colonoscopy (57.7% vs. 60.8%, p = 0.4). Similarly, for the secondary endpoints, there were no significant differences in PDR (68.7% vs. 69.6%, p = 0.8), SLDR (32.0% vs. 29.4%, p = 0.5), ASLDR (5.3% vs. 5.1%, p = 1.0), or AADR (24.5% vs. 23.6%, p = 0.8). In Table 1, we described lesion detection rates between both groups by indication (i.e., FIT+ or surveillance).

Conclusion

In the context of expert endoscopists PolyDeep did not increase the ADR.

Introduction

The ongoing phase 3 ESSENCE trial (NCT04822181) reported positive results for histological and non-invasive test (NIT) endpoints in an interim analysis of the first 800 randomised participants with biopsy-defined metabolic dysfunction-associated steatohepatitis (MASH) and F2/F3 fibrosis receiving once-weekly subcutaneous semaglutide 2.4 mg vs placebo. Participants receiving semaglutide achieved a mean weight loss of 10.5% vs 2.0% with placebo. In this post hoc analysis, we assessed the weight dependency of the effects of semaglutide 2.4 mg on study endpoints (NITs and histology) after 72 weeks of treatment, using weight loss-independent and weight loss-dependent pathways as covariates.

Aims & Methods

NITs and biopsies were assessed at baseline and week 72. MASH-related NIT responder endpoints were change in alanine aminotransferase (ALT; ≥17-unit reduction) and FibroScan-aspartate aminotransferase (FAST) score (≥0.22 reduction). Fibrosis-related NIT responder endpoints were change in vibration-controlled transient elastography (VCTE) liver stiffness measurement (30% reduction) and Enhanced Liver Fibrosis (ELF) score (≥0.5-unit reduction). Histological endpoints included resolution of MASH and improvement in fibrosis. All endpoints were assessed using logistic regression at week 72 with treatment as exposure, percentage weight loss from baseline to week 72 as mediator, and baseline type 2 diabetes status, fibrosis stage and body weight as covariates. The total and weight loss-independent and weight loss-dependent effect sizes were calculated as odds ratios (ORs), and missing data were omitted. All data are based on the full analysis set from the on-treatment observation period.

Results

For MASH-related endpoints, the total effect (OR [95% confidence interval (CI)]) for ALT, FAST score and resolution of MASH without worsening of fibrosis was 4.7 (3.3, 6.6), 6.9 (4.3, 10.9) and 3.9 (2.8, 5.5), respectively. ORs (95% CI) for the weight loss-independent effect were 3.0 (2.0, 4.6), 2.8 (1.7, 4.7) and 2.0 (1.4, 3.0), respectively; for the weight loss-dependent effect, ORs (95% CI) were 1.5 (1.2, 2.0), 2.5 (1.8, 3.4) and 1.9 (1.6, 2.4), respectively. Overall, 71.9%, 53.3% and 51.9% of the total effect for ALT, FAST score and resolution of MASH, respectively, were not mediated by weight loss. For the fibrosis-related endpoints, the total effect (OR [95% CI]) for VCTE, ELF score and improvement in fibrosis without worsening of MASH were 3.0 (2.0, 4.4), 4.5 (3.1, 6.4) and 2.1 (1.5, 3.1), respectively. ORs (95% CI) for the weight loss-independent effect were 1.7 (1.1, 2.7), 2.4 (1.6, 3.7) and 1.5 (1.0, 2.4), respectively; for the weight loss-dependent effect, ORs (95% CI) were 1.7 (1.4, 2.2), 1.9 (1.5, 2.3) and 1.4 (1.1, 1.8), respectively. This shows that 48.9%, 58.5% and 55.5% of the total effect for VCTE, ELF score and fibrosis improvement, respectively, were not mediated by weight loss.

Conclusion

Semaglutide 2.4 mg improved MASH-related histological and NIT endpoints and fibrosis-related NIT endpoints through equal contributions of weight loss-independent and weight loss-dependent metabolic mechanisms, with effects beyond weight loss.

Disclosure

Philip N. Newsome reports grants from Novo Nordisk, and has received consulting fees from Boehringer Ingelheim, Madrigal and Novo Nordisk. PNN also reports honoraria as a speaker from AiCME, Echosens and Novo Nordisk; support for attending meetings for Novo Nordisk; and participation on an advisory board for Boehringer Ingelheim, GSK, Madrigal, Novo Nordisk and Sagimet.
Elisabetta Bugianesi served as a consultant or advisory board member for Boehringer Ingelheim, Gilead, Intercept, Merck, Novo Nordisk, Pfizer and ProSciento, and as a speaker for Gilead, Intercept, Merck, Novo Nordisk and Pfizer. EB has also received a research grant from Gilead for fatty liver research.
Vlad Ratziu received consulting fees from Boehringer Ingelheim, GSK, Madrigal, Novo Nordisk, ProSciento and Sagimet, and research grants (to institution) from MSD.
Mary E. Rinella consults for 89bio, Akero, Boehringer Ingelheim, CytoDyn, GSK, HistoIndex, Intercept, Madrigal, NGM Bio, Novo Nordisk, Sagimet and Sonic Incytes. MER has received fees for consulting or participation in advisory boards for Boehringer Ingelheim, Echosens, Eli Lilly and Novo Nordisk. MER also reports honoraria as a speaker for CME events sponsored by Boehringer Ingelheim, Madrigal and Novo Nordisk.
Michael Roden received lecture fees or served on advisory boards for AstraZeneca, Echosens, Eli Lilly, Madrigal, Merck-MSD, Novo Nordisk and Target RWE, and performed investigator-initiated research with support from Boehringer Ingelheim, Novo Nordisk and Nutricia/Danone to the German Diabetes Center.
Arun J. Sanyal consults for and advises AstraZeneca and Avant Santé. AJS also consults for and has received grants from Akero, BMS, Eli Lilly, Intercept, Madrigal and Novo Nordisk. AJS consults for and owns stock in Rivus, and consults for 89bio, AGED Diagnostics, Albireo, Alnylam, Altimmune, Boehringer Ingelheim, Echosens, Genentech, Gilead, GSK, HistoIndex, Mallinckrodt, Merck, NGM Bio, Novartis, PathAI, Pfizer, Poxel, Regeneron Pharmaceuticals, Inc., Salix, Siemens, Surrozen, Takeda, Terns and Zydus. AJS owns stock in Durect, Exalenz, Genfit, Indalo, Inversago and Tiziana, and has received royalties from Elsevier and Wolters Kluwer.
Adel Belloum, Niels Krarup, Anna Cali, Mohamed Tawfik and Thea Vestergaard are employees and stockholders of Novo Nordisk A/S.
Jörn M. Schattenberg declares consultant honoraria from 89bio, Alentis, Alexion, Altimmune, AstraZeneca, Bionorica, Boehringer Ingelheim, Gilead Sciences, GSK, Inventiva Pharma, Ipsen, Eli Lilly, Madrigal Pharmaceuticals, MSD, Northsea Therapeutics, Novartis, Novo Nordisk, Pfizer, Roche, Sanofi and Siemens Healthineers; speaker honoraria from AbbVie, Academic Medical Education, Boehringer Ingelheim, Echosens, Forum für Medizinische Fortbildung, Gilead Sciences, Madrigal Pharmaceuticals, MedicalTribune, MedPublico GmbH, MedScape and Novo Nordisk; and stockholder options in AGED Diagnostics and Hepta Bio.
Matthew J. Armstrong reports investigator fees from Novo Nordisk.
Anja Geerts reports no conflicts of interest.
Jacob George is supported by the Robert W. Storr Bequest to the Sydney Medical Foundation, University of Sydney; a National Health and Medical Research Council of Australia Program Grant (APP1053206), Project, Ideas and Investigator grants (APP2001692, APP1107178, APP1108422, APP1196492) and a Cancer Institute, NSW grant (2021/ATRG2028). He serves on advisory boards for AbbVie, AstraZeneca, Boehringer Ingelheim, Novo Nordisk and Roche.
Chun-Jen Liu reports no conflicts of interest.
Ewa Janczewska was involved in clinical trials and lectures with Novo Nordisk, was involved in clinical trials with Inventiva Pharma, MSD, Galectin, Dr Falk and Sanofi and reports travel grants from Novo Nordisk and Eli Lilly.
Håvard Midgard is involved in lectures with Novo Nordisk, AbbVie and Gilead, and reports ad boards for Boeringer Ingelheim, AbbVie and Gilead.
Igor Bakulin reports involvement in clinical trials for Novo Nordisk and Eli Lilly and lectures for AbbVie and Johnson & Johnson.