Background/Objectives: Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. Methods: Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (Ktrans), reverse reflux rate constant (Kep), volume fraction of extravascular extracellular space (Ve), plasma volume fraction (Vp), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). Results: All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82-0.94). Multivariable analysis identified Kep (OR = 2620.172, p = 0.001), maximal slope (OR = 1.715, p = 0.024), and tumor location (OR = 5.561, p = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; p = 0.012). Conclusions: A qDCE-MRI-based model incorporating Kep and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC.

To evaluate the prognostic significance of tumour mutation burden (TMB) in pancreatic ductal adenocarcinoma (PDAC) and explore the performance of dual-layer spectral CT (DLCT) for noninvasive TMB evaluation. This retrospective analysis enroled patients with histopathologically confirmed PDAC who underwent DLCT between June 2019 and December 2023. Clinical, qualitative radiological, and quantitative conventional CT and DLCT parameters were evaluated. Survival analysis evaluated TMB’s association with progression-free survival (PFS) and identified an optimal TMB cutoff. Independent TMB predictors were identified through univariable and LASSO regression. Predictive performance was quantified via receiver operating characteristic and precision-recall curve assessments. Among 75 patients (mean age 60.4 ± 11.2 years; 41 males, 34 females), median TMB was 2.13 mut/Mb (interquartile range: 1.00–4.26). A 5 mut/Mb cutoff revealed distinct prognostic groups, with high-TMB cases exhibiting better PFS (median PFS: 7 vs 5 months, p = 0.02). Normalised iodine concentration in the pancreatic phase (nICa) was the sole independent TMB predictor (area under the curve [AUC] = 0.901; cutoff = 0.089; accuracy = 89.3

Perineural invasion (PNI) is an independent predictive factor for pancreatic ductal adenocarcinoma (PDAC); however, preoperative prediction is difficult. This study aimed to evaluate the value of dual-layer spectral CT (DLCT) parameters in PNI in PDAC. This retrospective study included patients with pathologically confirmed PDAC who underwent DLCT between August 2019 and March 2024. Risk factors were identified using univariable and least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC), precision-recall (PR) curves, calibration curves and decision curve analysis (DCA) accessed diagnostic performance. We included 100 patients (mean age: 60.88 ± 11.39 years old, 59 males). There were 71 patients with PNI and 29 without PNI. Relevant factors for PNI included tumour diameter, normalised CT attenuation during the pancreatic parenchymal phase in conventional CT (nCTa), normalised CT attenuation during the pancreatic parenchymal phase in DLCT at 40 keV, and normalised iodine concentration during the pancreatic parenchymal phase (nDIa) (odds ratio [OR], 1.34; 95

Background: Postoperative pancreatic fistula (POPF) is a prevalent and severe complication of pancreaticoenteric anastomosis; however, its accurate preoperative prediction is challenging. Purpose: To investigate the utility of pancreatic stiffness and fluidity derived from tomoelastography and stratify the risk of POPF. Materials and methods: This prospective study included participants who underwent preoperative tomoelastography and pancreaticoenteric anastomosis between November 2021 and July 2024. Participants were divided into training and test sets in a ratio of 2:1. Stiffness and fluidity were quantified using maps of shear-wave speed ( c ) and phase angle (φ). A nomogram was constructed using independent predictive factors of POPF, which were determined using logistic regression analysis of the training set. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA) of both sets. Results: The POPF rate was 20.19% (21/104) and 24.52% (13/53) in the training and test sets, respectively. A moderate correlation was observed between c and fibrosis ( r = 0.66; P < 0.001) and between fat fraction and lipomatosis ( r = 0.55; P < 0.001) in the total set. Pancreatic c (odds ratio, [OR]: 0.27; P < 0.001), φ (OR: 0.17; P < 0.001), main pancreatic duct (MPD) (OR: 0.49; P = 0.002), and fat fraction (OR: 1.05; P = 0.028) in the resection margin were independent predictive factors for POPF in training set. The AUCs of the nomogram were higher than those of the conventional MRI model (fat fraction and MPD) in both the training (0.941 vs. 0.812, P = 0.002) and test sets (0.900 vs. 0.808, P = 0.046). The nomogram had a good calibration. DCA curves showed that the nomogram had better clinical applicability than the conventional MRI model. Conclusion: A nomogram constructed with pancreatic mechanical properties (stiffness and fluidity) quantified using tomoelastography can improve the predictive performance of conventional MRI for POPF risk stratification.

Background The mechanical properties of pancreatic diseases provide new insight for diagnosis and differentiation. Tomoelastography, a multifrequency MR elastography technique, provides high-resolution maps of shear wave speed (c in m/s) and phase angle (φ in rad) for evaluating pancreatic stiffness and fluidity. We explore the diagnostic performances of stiffness and fluidity quantified using tomoelastography for differentiating between non-functional pancreatic neuroendocrine neoplasms (NF-pNENs) and solid pseudopapillary neoplasms (SPNs). Methods This prospective study consecutive enrolled 92 untreated patients with pathologically confirmed NF-pNENs and SPNs who underwent tomoelastography examinations from September 2021 to September 2023. Two radiologists independently measured the stiffness and fluidity, and intra/interobserver agreements were assessed. Multivariate logistic regression analysis was performed to identify independent relevant clinical and radiological features to differentiate NF-pNENs and SPNs. The diagnostic performances of stiffness and fluidity and clinical features for tumor differentiation were evaluated using receiver operating characteristic curves. Results Thirty patients with SPNs; 62 with NF-pNENs. The radiologists showed substantial or near-perfect interobserver agreement in evaluating clinical and radiological features. SPNs had lower stiffness (1.87 vs 2.40 m/s, P < 0.001) and fluidity (0.97 vs 1.10 rad, P < 0.001) than NF-pNENs. In the multivariate analysis, the independent relevant factors for tumor differentiation were age (P = 0.002) and stiffness (P < 0.001). The areas under the curves (AUCs) of age and stiffness for tumor differentiation were 0.780 (cutoff, 47.5 years) and 0.876 (cutoff, 2.07 m/s), respectively. The differentiation performance of the combined model (c + age) was better than that of convention model (age + enhancement pattern) (AUC = 0.921 vs 0.813; P = 0.009), as well as compared to the age (AUC = 0.921 vs 0.780; P < 0.001), enhancement pattern (AUC = 0.921 vs 0.675; P < 0.001), and fluidity (φ) (AUC = 0.921 vs 0.788; P = 0.011) metrics, but the stiffness (c) metric alone had comparable differentiation performance (AUC = 0.921 vs 0.876; P = 0.115). Conclusions Tomoelastography quantified lesion stiffness values combined with clinical age metrics were effective in identifying NF-pNENs and SPNs, establishing the value of tomoelastography in the non-invasive preoperative quantitative identification of pancreas-associated neoplasms.

To evaluate the value of dual-layer spectral detector CT (DLCT) for predicting Ki-67 proliferation status and p53 mutations in pancreatic ductal adenocarcinoma (PDAC). This retrospective study included untreated patients with pathologically confirmed PDAC who underwent DLCT between June 2019 and September 2023. Independent relevant clinical-radiological features and quantitative parameters for predicting Ki-67 proliferation status and p53 mutations were identified using multivariate logistic regression analysis. The diagnostic performances of independent variables were evaluated using receiver operating characteristic curves. We included 92 patients (60.19 ± 11.22 years old, 61 males). There were 40 patients with high Ki-67 expression (Ki-67 ≥ 25

Dual-layer spectral detector CT (DLCT) represents an advanced and emerging modality in CT imaging, offering multiparametric images that enhance the quantitative assessment of pancreatic diseases. Non-hypervascular non-functional pancreatic neuroendocrine neoplasms (NF-pNENs) and solid pseudopapillary neoplasms (SPNs) frequently exhibit overlapping clinical and imaging features, complicating their differentiation. This study aimed to investigate the valuable quantitative parameters of DLCT in preoperative differentiation between non-hypervascular NF-pNEN and SPN, as well as to analyze their diagnostic performance. This retrospective study included 52 patients with pathologically confirmed non-hypervascular NF-pNENs and SPNs who underwent DLCT examination before surgery between June 2019 and September 2025. To differentiate between non-hypervascular NF-pNENs and SPNs, independent relevant clinical-radiological features and quantitative parameters were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multivariate logistic regression analysis. The diagnostic performances of independent variables were assessed through receiver operating characteristic curves. There were 34 patients with non-hypervascular NF-pNENs (46.7 ± 10.2 years, 19 females) and 18 patients with SPNs (32.8 ± 7.9 years, 14 females). Clinical, radiological features and parameters were evaluated with near-perfect agreements among two radiologists. Age and normalised iodine concentration of lesion in the arterial phase (nICa) were the independent factors for differentiating between non-hypervascular NF-pNENs and SPNs in multivariate logistic regression analysis. The areas under the receiver operating characteristic curves (AUCs) for age, nCTa and nICa in tumour differentiation were 0.855 (95

To analyze abdominal manifestations in hereditary hemorrhagic telangiectasia (HHT). This retrospective study included 7 patients who were definitively diagnosed with HHT via the Curaçao criteria and underwent contrast-enhanced abdominal CT or MRI at our hospital from January 2016 to January 2024. We reviewed the literature and included patients with HHT who had abdominal CT and/or MRI data. A total of 7 patients were included in the study, 5 males and 2 females, ranging in age from 1 month to 79 years. All patients had at least 2 organs involved. Abdominal manifestations included liver involvement (telangiectasias, hepatic artery-hepatic vein fistula, hepatic artery-portal vein fistula, portal vein-hepatic vein fistula, or diffuse hyperplastic nodules) in 6 cases, splenic involvement (splenomegaly, dilated splenic capillaries, or aneurysm) in 4 cases, gastrointestinal involvement (collateral circulation establishment) in 5 cases, pancreatic involvement (increased and thickened pancreaticoduodenal artery) in 4 cases, and renal and perirenal involvement (no corticomedullary demarcation in the arterial phase, perinephric aneurysm) in 2 cases. Additionally, 139 cases of HHT with abdominal involvement were identified in the literature. Vascular fistula, aneurysm, capillary dilatation and the establishment of collateral circulation in abdominal organs are characteristic manifestations of HHT. MRI’s ability to visualize focal nodular hyperplasia-like nodules in the hepatobiliary phase and multiple arterial phases, together with CT-Angiography’s capacity to detect arteriovenous malformations across multiple organs, underscores the complementary strengths of these imaging modalities in the diagnosis of HHT.

Background: Tumor fibrosis plays an important role in chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC); however, there remains a contradiction in the prognostic value of fibrosis. The authors aimed to investigate the relationship between tumor fibrosis and survival in patients with PDAC, classify patients into high- and low-fibrosis groups, and develop and validate a CT-based radiomics model to non-invasively predict fibrosis before treatment. Materials and methods: This retrospective, bicentric study included 295 patients with PDAC without any treatments before surgery. Tumor fibrosis was assessed using the collagen fraction (CF). Cox regression analysis was used to evaluate the associations of CF with overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) analyses were used to determine the rounded threshold of CF. An integrated model (IM) was developed by incorporating selected radiomic features and clinical-radiological characteristics. The predictive performance was validated in the test cohort (Center 2). Results: The CFs were 38.22±6.89% and 38.44±8.66% in center 1 (131 patients, 83 males) and center 2 (164 patients, 100 males), respectively (P=0.814). Multivariable Cox regression revealed that CF was an independent risk factor in the OS and DFS analyses at both centers. ROCs revealed that 40% was the rounded cut-off value of CF. IM predicted CF with areas under the curves (AUCs) of 0.829 (95% CI: 0.753-0.889) and 0.751 (95% CI: 0.677-0.815) in the training and test cohorts, respectively. Decision curve analyses revealed that IM outperformed radiomics model and clinical-radiological model for CF prediction in both cohorts. Conclusions: Tumor fibrosis was an independent risk factor for survival of patients with PDAC, and a rounded cut-off value of 40% provided a good differentiation of patient prognosis. The model combining CT-based radiomics and clinical-radiological features can satisfactorily predict survival-grade fibrosis in patients with PDAC.

To prospectively investigate the pancreatic stiffness (c) and fluidity (φ) of pancreatic neuroendocrine neoplasms (pNENs), measured using multifrequency magnetic resonance elastography (MRE), and evaluate their performance in predicting pNENs pathological grade. This study included 96 untreated patients with pathologically confirmed pNENs who underwent multifrequency MRE within 2 weeks before surgery between September 2021 and November 2023. Independent predictors of pathological grade were identified using multivariate regression analysis, and predictive performance was assessed using receiver operating characteristic curves. The study included 76 patients with low-grade pNENs (45 men; mean age: 48.7 ± 14.0 years; Grade 1: 34 patients, Grade 2: 42 patients) and 20 patients with high-grade pNENs (10 men; mean age: 54.4 ± 13.8 years; Grade 3: 15 patients, neuroendocrine carcinoma: 5 patients). The two radiologists showed substantial or near-perfect interobserver agreement in evaluating the quantitative parameters. The multivariate regression analysis identified c and relative enhancement in the portal venous phase (V) as independent predictors of pathological grade. The combined model (V + c) had the best predictive performance (area under the curve (AUC) = 0.930; sensitivity: 95.0

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.

Pancreatic neuroendocrine neoplasms (pNENs) are the second most common pancreatic malignancy. While most cases are sporadic, a small proportion is associated with genetic syndromes, such as Multiple Endocrine Neoplasia (MEN), Von Hippel-Lindau Syndrome (VHL), Neurofibromatosis Type 1 (NF1), and Tuberous Sclerosis Complex (TSC). This review aims to use pNENs as a clue to reveal the full spectrum of disease, providing a comprehensive understanding of diagnosis. It aids in promptly identifying abnormalities in other organs, recognizing familial genetic mutations, and achieving personalized treatment.

To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19–9 (CA 19-9). This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong’s test. The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95

Tomoelastography, a newly emerging imaging modality, is a multi-frequency magnetic resonance elastography technique using noise-robust data post-processing. The aim of our study was to investigate the assessment of stiffness and fluidity of pancreas in healthy volunteers with tomoelastography. Tomoelastography derived pancreatic stiffness and fluidity were near-perfect reproducible in healthy volunteers. There was no stiff or fluidic difference among different groups of sex, age, BMI or pancreatic anatomical region. Tomoelastography can provide stable and promising stiffness and fluidity measurements throughout the pancreas. Our results provide data that will enable pancreatic studies of tomoelastography as a potential clinical tool in future.

Table S1. Pathological characteristics of the patients in our study Table S2. Extracted imaging features Figure S1. Recruitment pathways for patients in this study

Purpose: To explore the optimal energy level of dual-layer spectral detector computed tomography (DLCT) im-ages of pancreatic neuroendocrine neoplasms (pNENs) and investigate the value in their detection. Methods: This retrospective analysis included 134 pNEN patients with 136 lesions; they underwent contrast-enhanced DLCT scanning with histopathological confirmation of pNENs. Virtual monoenergetic images (VMI) of 40-100 keV, iodine concentration map (IC map), Z-effective atomic number map (Zeff map), and conventional images were analysed. The optimal energy level was obtained by comparing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The lesion detection rates of DLCT and conventional images were compared. Subjective image analysis was performed by two readers who assessed the image quality and lesion conspicuity on a 5-point scale. Results: The SNR of VMIs from 40 to 80 keV (arterial phase, P < 0.001; venous phase, P < 0.05) and CNR from 40 to 60 keV (arterial and venous phases, each P < 0.05) were higher than that of conventional images; VMI40keV showed the highest SNR and CNR. There was a good inter-reader agreement between the two reviewers (Kappa values > 0.61); the scores of Zeff and IC maps were higher than those of conventional images and VMI40keV (P < 0.05). The detection performance of DLCT images was better than conventional images. Conclusions: The VMI40keV demonstrated the best CNR and SNR of pNENs compared to other VMIs. Zeff and IC maps improve objective image quality and reader preference compared to conventional images. These findings could possess important clinical implications in formulating treatment strategies.

BACKGROUND. The 2019 WHO classification of digestive system tumors separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. OBJECTIVE. The purpose of this study was to assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. METHODS. This retrospective study included 104 patients (mean age, 51 ± 13 [SD] years; 52 women, 52 men) with pathologically confirmed NEN (89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3; 15 NEC) who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases and assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilatation, vascular invasion, lymphadenopathy). Interobserver agreement was assessed. Mean and median values of both readers' measurements were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. RESULTS. Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.869 to 0.992 for quantitative metrics and, expressed as kappa coefficients, ranged from 0.723 to 0.816 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (p < .05) were IC in the portal venous phase (median, 1.3 mg/mL for NEC vs 2.7 mg/mL for NET), Zeff in the portal venous phase (median, 8.1 vs 8.6), and attenuation in the portal venous phase (median, 78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on a combination of IC and Zeff achieved sensitivity of 93.33% and specificity of 80.90% for predicting NEC. Significant independent predictors (p < .05) for differentiating grade 3 NET and NEC were IC (median, 2.0 vs 1.3 mg/mL; AUC = 0.789) and attenuation (mean, 90.3 vs 78.2 HU; AUC = 0.647), both measured in the portal venous phase. CONCLUSION. Incorporation of DLCT metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. CLINICAL IMPACT. DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.

Objectives: To identify early and more accurate imaging response criteria for computed tomography evaluation to define 'responders' in advanced gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) patients treated with cisplatin/etoposide combined chemotherapy. Materials and methods: Thirty-seven patients with GEP-NEC treated with first-line cisplatin/etoposide (E/P) combined chemotherapy were enrolled in this study. Computed tomography scans of the chest, abdomen, and pelvis were performed at baseline, during the treatment course, and during follow-up. Tumour size was measured, and tumour response was evaluated by Response Evaluation Criteria in Solid Tumours (RECIST) 1.1. Receiver operating characteristic (ROC) analysis was carried out among the patients who progressed during follow-up. Thresholds from -55% to + 5% were tested by Kaplan-Meier analysis to define "responders" for significantly improved progression-free survival (PFS). The overall survival rate was compared between these two groups. Results: A reduction of 45% (vs. baseline) achieved the highest sensitivity (70%) and specificity (90%) by ROC analysis. This threshold divided patients into 15 responders and 22 nonresponders. Patients who were grouped as responders by the -45% threshold had a significantly longer PFS (11.06 months) than nonresponders (7.97 months, hazard ratio, 3.636; 95% confidence interval, 1.293-10.164). No significant difference was shown in overall survival between these two groups (29.1 vs. 21.4 months, P = 0.190). Conclusion: A 45% reduction in target lesions may be considered to be a more reliable predictor than the RECIST 1.1 criteria in evaluating the outcome of GEP-NEC patients treated with E/P chemotherapy.

To evaluate the prognostic value of fibrosis for patients with pancreatic adenocarcinoma (PDAC) and preoperatively predict fibrosis using clinicoradiological features. Tumor fibrosis plays an important role in the chemoresistance of PDAC. However, the prognostic value of tumor fibrosis remains contradiction and accurate prediction of tumor fibrosis is required. The study included 131 patients with PDAC who underwent first-line surgery. The prognostic value of fibrosis and rounded cutoff fibrosis points for median overall survival (OS) and disease-free survival (DFS) were determined using Cox regression and receiver operating characteristic (ROC) analyses. Then the whole cohort was randomly divided into training (n = 88) and validation (n = 43) sets. Binary logistic regression analysis was performed to select independent risk factors for fibrosis in the training set, and a nomogram was constructed. Nomogram performance was assessed using a calibration curve and decision curve analysis (DCA). Hazard ratios of fibrosis for OS and DFS were 1.121 (95% confidence interval [CI]: 1.082–1.161) and 1.110 (95% CI: 1.067–1.155). ROC analysis identified 40% as the rounded cutoff fibrosis point for median OS and DFS. Tumor diameter, carbohydrate antigen 19-9 level, and peripancreatic tumor infiltration were independent risk factors; areas under the nomogram curve were 0.810 and 0.804 in the training and validation sets, respectively. The calibration curve indicated good agreement of the nomogram, and DCA demonstrated good clinical usefulness. Tumor fibrosis was associated with poor OS and DFS in patients with PDAC. The nomogram incorporating clinicoradiological features was useful for preoperatively predicting tumor fibrosis. • Tumor fibrosis is correlated with poor prognosis in patients with pancreatic adenocarcinoma. • Tumor fibrosis can be categorized according to its association with overall survival and disease-free survival. • A nomogram incorporating carbohydrate antigen 19-9 level, tumor diameter, and peripancreatic tumor infiltration is useful for preoperatively predicting tumor fibrosis.

Background Patients with chronic pancreatitis often have irreversible pancreatic insufficiency before a clinical diagnosis. Pancreatic cancer is a fatal malignant tumor in the advanced stages. Patients having high risk of pancreatic diseases must be screened early to obtain better outcomes using new imaging modalities. Therefore, this study aimed to investigate the reproducibility of tomoelastography measurements for assessing pancreatic stiffness and fluidity and the variance among healthy volunteers. Methods Forty-seven healthy volunteers were prospectively enrolled and underwent two tomoelastography examinations at a mean interval of 7 days. Two radiologists blindly and independently measured the pancreatic stiffness and fluidity at the first examination to determine the reproducibility between readers. One radiologist measured the adjacent pancreatic slice at the first examination to determine the reproducibility among slices and measured the pancreas at the second examination to determine short-term repeatability. The stiffness and fluidity of the pancreatic head, body, and tail were compared to determine anatomical differences. The pancreatic stiffness and fluidity were compared based on sex, age, and body mass index (BMI). Results Bland-Altman analyses (all P > 0.05) and intraclass correlation coefficients (all >0.9) indicated near perfect reproducibility among readers, slices, and examinations at short intervals. Neither stiffness (P = 0.477) nor fluidity (P = 0.368) differed among the pancreatic anatomical regions. The mean pancreatic stiffness was 1.45 +/- 0.09 m/s; the mean pancreatic fluidity was 0.83 +/- 0.06 rad. Stiffness and fluidity did not differ by sex, age, or BMI. Conclusion Tomoelastography is a promising and reproducible tool for assessing pancreatic stiffness and fluidity in healthy volunteers.