Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9.

OBJECTIVES: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Use of individualized 3D-printed models of pancreatic cancer to improve surgeons' anatomic understanding and surgical planning.

OBJECTIVES: Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery. METHODS: Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents. RESULTS: After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT. CONCLUSION: The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning. CLINICAL RELEVANCE STATEMENT: The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students. KEY POINTS: • A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

High-resolution pancreatic computed tomography for assessing pancreatic ductal adenocarcinoma resectability: a multicenter prospective study.

OBJECTIVE: This prospective multicenter study aimed to evaluate the diagnostic performance of 80-kVp thin-section pancreatic CT in determining pancreatic ductal adenocarcinoma (PDAC) resectability according to the recent National Comprehensive Cancer Network (NCCN) guidelines. METHODS: We prospectively enrolled surgical resection candidates for PDAC from six tertiary referral hospitals (study identifier: NCT03895177). All participants underwent pancreatic CT using 80 kVp tube voltage with 1-mm reconstruction interval. The local resectability was prospectively evaluated using NCCN guidelines at each center and classified into three categories: resectable, borderline resectable, and unresectable. RESULTS: A total of 138 patients were enrolled; among them, 60 patients underwent neoadjuvant therapy. R0 resection was achieved in 103 patients (74.6%). The R0 resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable disease, respectively, in 78 patients who underwent upfront surgery. Meanwhile, the rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively, in patients who received neoadjuvant therapy. The area under curve of high-resolution CT in predicting R0 resection was 0.784, with sensitivity, specificity, and accuracy of 87.4% (90/103), 48.6% (17/35), and 77.5% (107/138), respectively. Tumor response was significantly associated with the R0 resection after neoadjuvant therapy (odds ratio [OR] = 38.99, p = 0.016). CONCLUSION: An 80-kVp thin-section pancreatic CT has excellent diagnostic performance in assessing PDAC resectability, enabling R0 resection rates of 88.7% and 90.9% for patients with resectable PDAC who underwent upfront surgery and patients with resectable PDAC after neoadjuvant therapy, respectively. KEY POINTS: • The margin-negative (R0) resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable pancreatic ductal adenocarcinoma (PDAC), respectively, on 80-kVp thin-section pancreatic CT in the 78 patients who underwent upfront surgery. • Among the 60 patients who underwent neoadjuvant therapy, the R0 rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively. • Tumor response, along with the resectability status on pancreatic CT, was significantly associated with the R0 resection rate after neoadjuvant therapy.

MRI vs. CT for pancreatic adenocarcinoma vascular invasion: comparative diagnostic test accuracy systematic review and meta-analysis.

OBJECTIVES: To perform a systematic review comparing the diagnostic accuracy of MRI vs. CT for assessing pancreatic ductal adenocarcinoma (PDAC) vascular invasion. METHODS: MEDLINE, EMBASE, Cochrane Central, and Scopus were searched until December 2021 for diagnostic accuracy studies comparing MRI vs. CT to evaluate vascular invasion of pathologically confirmed PDAC in the same patients. Findings on resection or exploratory laparotomy were the preferred reference standard. Data extraction, risk of bias, and applicability assessment were performed by two authors using the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Bivariate random-effects meta-analysis and meta-regression were performed with 95% confidence intervals (95% CI). RESULTS: Three studies were included assessing 474 vessels without vascular invasion and 65 with vascular invasion in 107 patients. All patients were imaged using MRI at ≥ 1.5 T and a pancreatic protocol CT. No difference was shown between MRI and CT for diagnosing PDAC vascular invasion: MRI/CT sensitivity (95% CI) were 71% (47-87%)/74% (56-86%), and specificity were 97% (94-99%)/97% (94-98%). Sources of bias included selection bias from only a subset of CT patients undergoing MRI and verification bias from patients with unresectable disease not confirmed on surgery. No patients received neoadjuvant therapy prior to staging. CONCLUSIONS: Based on limited data, no difference was observed between MRI and pancreatic protocol CT for PDAC vascular invasion assessment. MRI may be an adequate substitute for pancreatic protocol CT in some patients, particularly those who have already had a single-phase CT. Larger and more recent cohort studies at low risk of bias, including patients who have received neoadjuvant therapy, are needed. CLINICAL RELEVANCE STATEMENT: Abdominal MRI performed similarly to pancreatic protocol CT at assessing pancreatic ductal adenocarcinoma vascular invasion, suggesting local staging is adequate in some patients using MRI. More data are needed using larger, more recent cohorts including patients with neoadjuvant treatment. KEY POINTS: • Based on limited data, no difference was found between MRI and pancreatic protocol CT sensitivity and specificity for diagnosing PDAC vascular invasion (p = 0.81, 0.73 respectively). • Risk of bias could be reduced in future PDAC MRI vs CT comparative diagnostic test accuracy research by ensuring all enrolled patients undergo both imaging modalities being compared in random order and regardless of the findings on either modality. • More studies are needed that directly compare the diagnostic performance of MRI and CT for PDAC staging after neoadjuvant therapy.

Pancreatic ductal adenocarcinoma (PDAC) regional nodal disease at standard lymphadenectomy: is MRI accurate for identifying node-positive patients?

OBJECTIVE: To determine the accuracy of qualitative and quantitative MRI features for the diagnosis of pathologic regional lymph nodes at standard lymphadenectomy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: All adult patients with pancreatic MRI performed from 2011 to 2021 within 3 months of a pancreaticoduodenectomy were eligible for inclusion in this single-center retrospective cohort study. Regional nodes at standard lymphadenectomy were independently reviewed by two fellowship-trained abdominal radiologists for the following qualitative features: heterogeneous T2 signal, round shape, indistinct margin, peri-nodal fat stranding, and restricted diffusion greater than the spleen. Quantitative characteristics including primary tumor size, largest node short- and long-axes length, number of regional nodes, absolute apparent diffusion coefficient (ADC) values, and ADC node-to-spleen signal index were assessed. Analysis was at the patient-level with surgical pathology as the reference standard. RESULTS: Of 75 patients, 85% (64/75) were positive for regional nodal disease on histopathology. None of the qualitative variables evaluated on MRI was associated with pathologic nodes. Median primary tumor maximum diameter was slightly larger for patients with pathologic nodes compared to those without (18 mm (10-42 mm) vs 16 mm (9-22 mm), p = 0.027). None of the other quantitative features was associated with pathologic nodes. Radiologist opinion was not associated with pathologic nodes (p = 0.520). Interobserver agreement was fair (kappa = 0.257). CONCLUSIONS: Lymph node morphologic features and radiologist opinion using MRI are of limited value for diagnosing PDAC regional nodal disease. Improved diagnostic techniques are needed given the prognostic implications of pathologic lymph nodes in these patients. KEY POINTS: • Multiple lymph node morphologic features routinely assessed on MRI for malignancies elsewhere in the body are likely not applicable when assessing for pancreatic ductal adenocarcinoma nodal disease. • Interobserver agreement for the presence or absence of pancreatic ductal adenocarcinoma lymph node morphologic features on MRI is fair (kappa = 0.257). • Many more lymph nodes are resected at PDAC standard lymphadenectomy than are detectable on MRI, median 25 vs 5 (p < 0.001), suggesting improved diagnostic techniques are needed to identify PDAC nodal disease.

Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis.

OBJECTIVES: To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP). METHODS: In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility. RESULTS: The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively. CONCLUSIONS: The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients. KEY POINTS: • A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

Identification of intratumoral fluid-containing area by magnetic resonance imaging to predict prognosis in patients with pancreatic ductal adenocarcinoma after curative resection.

OBJECTIVES: To compare the prognosis of pancreatic ductal adenocarcinoma (PDAC) after curative resection according to the type of intratumoral fluid-containing area identified on MRI. METHODS: This retrospective study included 112 consecutive patients who underwent upfront surgery with margin-negative resection between 2012 and 2019. All patients underwent MRI within 1 month before surgery. Three radiologists independently assessed the MRI findings, determined whether intratumoral fluid-containing areas were present, and classified all intratumoral fluid-containing areas by type (i.e., imaging necrosis or neoplastic mucin cysts). Recurrence-free survival (RFS) and overall survival (OS) were evaluated by the Kaplan-Meier method and the Cox proportional hazards model. Histopathological differences according to the type of intratumoral fluid-containing area were assessed. RESULTS: Of the 112 PDAC patients, intratumoral fluid-containing areas were identified on MRI in 33 (29.5%), among which 18 were classified as imaging necrosis and 15 as neoplastic mucin cysts. PDAC patients with imaging necrosis demonstrated significantly shorter RFS (mean 6.1 months versus 47.3 months; p < .001) and OS (18.4 months versus 55.0 months, p = .001) than those with neoplastic mucin cysts. Multivariable analysis showed that only the type of intratumoral fluid-containing area was significantly associated with RFS (hazard ratio, 2.25 and 0.38; p = .009 and p = .046 for imaging necrosis and neoplastic mucin cysts, respectively). PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts (p ≤ .02). CONCLUSION: The detection and discrimination of intratumoral fluid-containing areas on preoperative MRI may be useful in predicting the prognosis of PDAC patients after curative resection. KEY POINTS: • Pancreatic ductal adenocarcinoma (PDAC) patients with imaging necrosis demonstrated significantly shorter survival than those with neoplastic mucin cysts after curative resection. • Multivariable analysis showed that only the type of intratumoral fluid-containing area identified on MRI was significantly associated with recurrence-free survival. • PDAC with imaging necrosis had more frequent histological necrosis, more aggressive tumor differentiation, and higher tumor cellularity than PDAC with neoplastic mucin cysts.

Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma.

OBJECTIVES: Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score). METHODS: We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint. RESULTS: The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively. CONCLUSIONS: In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor). KEY POINTS: • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI.

OBJECTIVES: Tumour size measurement is pivotal for staging and stratifying patients with pancreatic ductal adenocarcinoma (PDA). However, computed tomography (CT) frequently underestimates tumour size due to insufficient depiction of the tumour rim. CT-derived fractal dimension (FD) maps might help to visualise perfusion chaos, thus allowing more realistic size measurement. METHODS: In 46 patients with histology-proven PDA, we compared tumour size measurements in routine multiphasic CT scans, CT-derived FD maps, multi-parametric magnetic resonance imaging (mpMRI), and, where available, gross pathology of resected specimens. Gross pathology was available as reference for diameter measurement in a discovery cohort of 10 patients. The remaining 36 patients constituted a separate validation cohort with mpMRI as reference for diameter and volume. RESULTS: Median RECIST diameter of all included tumours was 40 mm (range: 18-82 mm). In the discovery cohort, we found significant (p = 0.03) underestimation of tumour diameter on CT compared with gross pathology (Δdiameter3D = -5.7 mm), while realistic diameter measurements were obtained from FD maps (Δdiameter3D = 0.6 mm) and mpMRI (Δdiameter3D = -0.9 mm), with excellent correlation between the two (R2 = 0.88). In the validation cohort, CT also systematically underestimated tumour size in comparison to mpMRI (Δdiameter3D = -10.6 mm, Δvolume = -10.2 mL), especially in larger tumours. In contrast, FD map measurements agreed excellently with mpMRI (Δdiameter3D = +1.5 mm, Δvolume = -0.6 mL). Quantitative perfusion chaos was significantly (p = 0.001) higher in the tumour rim (FDrim = 4.43) compared to the core (FDcore = 4.37) and remote pancreas (FDpancreas = 4.28). CONCLUSIONS: In PDA, fractal analysis visualises perfusion chaos in the tumour rim and improves size measurement on CT in comparison to gross pathology and mpMRI, thus compensating for size underestimation from routine CT. KEY POINTS: • CT-based measurement of tumour size in pancreatic adenocarcinoma systematically underestimates both tumour diameter (Δdiameter = -10.6 mm) and volume (Δvolume = -10.2 mL), especially in larger tumours. • Fractal analysis provides maps of the fractal dimension (FD), which enable a more reliable and size-independent measurement using gross pathology or multi-parametric MRI as reference standards. • FD quantifies perfusion chaos-the underlying pathophysiological principle-and can separate the more chaotic tumour rim from the tumour core and adjacent non-tumourous pancreas tissue.

[The role of the microbiome in diseases of the pancreas]. / Rolle des Mikrobioms bei Erkrankungen des Pankreas.

BACKGROUND: The human body is inhabited by diverse microorganisms. Together, this so-called microbiome exerts important metabolic functions and contributes to the maintenance of health. At the same time, shifts in the microbiome composition may lead to disease. OBJECTIVES: Review of the current literature about the role of the microbiome in diseases of the pancreas. MATERIALS AND METHODS: Literature search in PubMed and Embase. RESULTS: The exocrine pancreas is a major factor determining the composition and stability of the intestinal microbiome even in healthy people without pancreatic disease. Inflammatory diseases of the pancreas such as acute or chronic pancreatitis lead to reduced microbial diversity, loss of gut barrier stabilizing bacteria and an increase in facultative pathogens like Escherichia or Enterococcus. Even pancreatic cancer tissue harbours microbiota and mice models have shown that the growth of pancreatic cancer can be inhibited by microbiota ablation. CONCLUSIONS: Inflammatory diseases of the pancreas lead to gut microbiome dysbiosis and tumor microbiota probably play a role in the development of pancreatic cancer. Until now, however, there is no proof that therapeutic microbiota modulation in individuals with pancreatic disease can improve mortality or quality of life. At this point, the analysis of the microbiome in pancreatic disease should only be performed in scientific studies.

Prediction of tumour grade and survival outcome using pre-treatment PET- and MRI-derived imaging features in patients with resectable pancreatic ductal adenocarcinoma.

OBJECTIVES: To perform a correlation analysis between histopathology and imaging in patients with previously untreated pancreatic ductal adenocarcinoma (PDAC) and to determine the prognostic values of clinical, histological, and imaging parameters regarding overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). METHODS: This single-centre study prospectively included 61 patients (32 males; median age, 68.0 years [IQR, 63.0-75.0 years]) with histologically confirmed PDAC and following surgical resection who preoperatively underwent 18F-FDG PET/CT and DW-MRI. On whole lesions, we measured, using a 42% SUVmax threshold volume of interest (VOI), the following quantitative parameters: mean and maximum standardised uptake values (SUVmean and SUVmax), total lesion glycolysis (TLG), metabolic tumour volume (MTV), mean and minimum apparent diffusion coefficient (ADCmean and ADCmin), diffusion total volume (DTV), and MTV/ADCmin ratio. Spearman's correlation analysis was performed to assess relationships between these markers and histopathological findings from surgical specimens (stage; grade; resection quality; and vascular, perineural, and lymphatic invasion). Kaplan-Meier and Cox hazard ratio methods were used to evaluate the impacts of imaging parameters on OS (n = 41), DSS (n = 36), and PFS (n = 41). RESULTS: Inverse correlations between ADCmin and SUVmax (rho = - 0.34; p = 0.0071), and between SUVmean and ADCmean (rho = - 0.29; p = 0.026) were identified. ADCmin was inversely correlated with tumour grade (rho = - 0.40; p = 0.0015). MTV was an independent predictive factor for OS and DSS, while DTV was an independent predictive factor for PFS. CONCLUSION: In previously untreated PDAC, ADC and SUV values are correlated. Combining PET-MRI metrics may help predict PDAC grade and patients' survival. KEY POINTS: • Minimum apparent diffusion coefficient derived from DW-MRI inversely correlates with tumour grade in pancreatic ductal adenocarcinoma. • In pancreatic ductal adenocarcinoma, metabolic tumour volume has been confirmed as a predictive factor for patients' overall survival and disease-specific survival. • Combining PET and MRI metrics may help predict grade and patients' survival in pancreatic ductal adenocarcinoma.

Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis.

OBJECTIVES: Pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) are diseases with a highly analogous visual presentation that are difficult to distinguish by imaging. The purpose of this research was to create a radiomics-based prediction model using dual-time PET/CT imaging for the noninvasive classification of PDAC and AIP lesions. METHODS: This retrospective study was performed on 112 patients (48 patients with AIP and 64 patients with PDAC). All cases were confirmed by imaging and clinical follow-up, and/or pathology. A total of 502 radiomics features were extracted from the dual-time PET/CT images to develop a radiomics decision model. An additional 12 maximum intensity projection (MIP) features were also calculated to further improve the radiomics model. The optimal radiomics feature set was selected by support vector machine recursive feature elimination (SVM-RFE), and the final classifier was built using a linear SVM. The performance of the proposed dual-time model was evaluated using nested cross-validation for accuracy, sensitivity, specificity, and area under the curve (AUC). RESULTS: The final prediction model was developed from a combination of the SVM-RFE and linear SVM with the required quantitative features. The multimodal and multidimensional features performed well for classification (average AUC: 0.9668, accuracy: 89.91%, sensitivity: 85.31%, specificity: 96.04%). CONCLUSIONS: The radiomics model based on 2-[18F]fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET/CT dual-time images provided promising performance for discriminating between patients with benign AIP and malignant PDAC lesions, which shows its potential for use as a diagnostic tool for clinical decision-making. KEY POINTS: • The clinical symptoms and imaging visual presentations of PDAC and AIP are highly similar, and accurate differentiation of PDAC and AIP lesions is difficult. • Radiomics features provided a potential noninvasive method for differentiation of AIP from PDAC. • The diagnostic performance of the proposed radiomics model indicates its potential to assist doctors in making treatment decisions.

Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study.

BACKGROUND: Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment. METHODS: Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan-Meier method. RESULTS: Multivariable Cox regression analysis identified the probability of T1WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T1WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T1WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T1WI_NGTDM_Strength and high-T1WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively. CONCLUSIONS: Whole-tumour radiomics feature of T1WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.

Serum fibrinogen as a diagnostic and prognostic biomarker for pancreatic ductal adenocarcinoma.

BACKGROUND/OBJECTIVES: Early diagnosis of pancreatic ductal adenocarcinoma (PDAC) is important as PDAC can lead to mortality; however, no specific biomarker has been identified for its early diagnosis. We previously identified fibrinogen α chain as a promising biomarker for differentiating between patients with and without PDAC using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Here, we aimed to validate the clinical usefulness of serum fibrinogen as a biomarker for PDAC. METHODS: From 2009 to 2011, blood samples of 67 PDAC patients and 43 healthy adults (controls) were prospectively collected. Serum fibrinogen levels and their clinical significances were evaluated. RESULTS: Mean fibrinogen levels were significantly higher in the PDAC group than in the control group (3.08 ± 0.565 vs. 2.54 ± 0.249 log10 ng/mL, P < 0.001). In the receiver operating characteristic analysis, overall sensitivity, and specificity of serum fibrinogen levels for differentiating PDAC patients from control patients were 67.4% and 83.6%, respectively, with a 427-ng/mL cutoff value. Serum fibrinogen levels were significantly higher in PDAC patients with distant metastasis than in those without distant metastasis (3.38 ± 0.581 vs. 2.93 ± 0.499 log10 ng/mL, P = 0.002). Median overall survival was significantly longer in PDAC patients with low fibrinogen levels (<1000 ng/mL) than in those with high fibrinogen levels (≥1000 ng/mL) [489 days (95% confidence interval, 248.1-729.9) vs. 172 days (58.4-285.6) (P = 0.008)]. Although serum fibrinogen levels were poorly correlated with carbohydrate antigen 19-9 levels, these two biomarkers together predicted survival better. CONCLUSIONS: Serum fibrinogen levels may be a useful biomarker for diagnosing and predicting PDAC prognosis.

New Developments in the Treatment of Pancreatic Cancer: Highlights from the 44th ASCO Annual Virtual Meeting, May 29-31, 2020.

Pancreatic cancer has the worst survival of any solid tumor. Overall, pancreatic cancer accounts for about 3% of all cancers in the US and about 7% of all cancer deaths. The American Cancer Society's estimates that 57,600 people (30,400 men and 27,200 women) will be diagnosed with pancreatic cancer in the United States for 2020 and approximately 47,050 people (24,640 men and 22,410 women) will die of this disease. FOLFIRINOX, or the combination of gemcitabine with nab-paclitaxel remain to be the major treatment options for these patients for both local and metastatic disease. This slow progress is a result of partly the complex pathogenesis of this disease, and partly the fact that window of opportunity to treat these patients is short as majority of them are diagnosed at an advanced stage. This is a real challenge but also provides an opportunity for new ideas and novel approaches. In this paper, we will present few interesting studies presented at the American Society of Clinical Oncology (ASCO) 2020 virtual Annual Meeting.

Non-branched microcysts of the pancreas on MR imaging of patients with pancreatic tumors who had pancreatectomy may predict the presence of pancreatic intraepithelial neoplasia (PanIN): a preliminary study.

PURPOSE: To evaluate whether pancreatic parenchymal abnormalities on magnetic resonance imaging (MRI) are associated with pancreatic intraepithelial neoplasia (PanIN) on histology. MATERIALS AND METHODS: Retrospective study approved by institutional review board. One hundred patients (48 men, 52 women; mean age, 53.2 ± 16.29 [SD]) underwent MRI before pancreatectomy for pancreatic tumors analyzed by two independent observers blinded to histopathological results for the presence of non-communicating microcysts and pancreatic atrophy (global or focal) beside tumors. MRI findings were compared to histopathological findings of resected specimens. Interobserver agreement was calculated. The association between parenchymal abnormalities and presence of PanIN was assessed by uni- and multivariate analyses. RESULTS: PanIN was present in 65/100 patients (65%). The presence of microcysts on MRI had a sensitivity of 52.3% (34/65 [95%CI, 51.92-52.70%]), a specificity of 77.1% (27/35 [95%CI, 76.70-77.59]), and accuracy of 61% (61/100 95%CI [50.7-70.6]) for the diagnosis of PanIN while global atrophy had a sensitivity of 24.6% (16/6 [95%CI, 24.28-24.95]) and a specificity of 97.1% (34/35 [95%CI, 96.97-97.32%]). In multivariate analysis, the presence of microcysts (OR, 3.37 [95%CI, 1.3-8.76]) (p = 0.0127) and global atrophy (OR, 9.79 [95%CI, 1.21-79.129]) (p = 0.0324) were identified as independent predictors of the presence of PanIN. The combination of these two findings was observed in 10/65 PanIN patients and not in patients without PanIN (p = 0.013 with an OR of infinity [95%CI, 1.3-infinity]) and was not discriminant for PanIN-3 and lower grade (p = 0.22). Interobserver agreement for the presence of microcysts was excellent (kappa = 0.92), and for the presence of global atrophy, it was good (kappa = 0.73). CONCLUSION: The presence of non-communicating microcysts on pre-operative MRI can be a significant predictor of PanIN in patients with pancreatic tumors. KEY POINTS: • In patients with pancreatic tumors who had partial pancreatectomy, MR non-communicating pancreatic microcysts have a 52.3% sensitivity, a 77.1% specificity, and a 61% accuracy for the presence of PanIN with univariate and with an odds ratio of 3.37 with multivariate analyses. • The association of global atrophy and non-communicating microcysts increases the predictive risk of PanIN.

Differentiating focal autoimmune pancreatitis and pancreatic ductal adenocarcinoma: contrast-enhanced MRI with special emphasis on the arterial phase.

OBJECTIVES: To compare focal-type autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDA) using contrast-enhanced MR imaging (CE-MRI), and to assess diagnostic performance of the lesion contrast at arterial phase (AP) (ContrastAP) for differentiating between the two diseases. METHODS: Thirty-six patients with focal-type AIP and 72 patients with PDA were included. All included patients underwent CE-MRI with triple phases. The signal intensity (SI) of the mass and normal pancreas was measured at each phase, and the lesion contrast (SIpancreas/SImass) was compared between AIP and PDA groups. The sensitivity and specificity of ContrastAP using an optimal cutoff point were compared with those of key imaging features specific to AIP and PDA. RESULTS: The lesion contrast differed significantly between AIP and PDA groups at all phases of CE-MRI; the maximum difference was observed at AP. For AIP, the sensitivity (94.4%) and specificity (87.5%) of ContrastAP (cutoff ≤ 1.41) were comparable or significantly higher than those of all key imaging features (sensitivity, 38.9-88.9%; specificity, 48.6-95.8%), except for the halo sign. For PDA, the sensitivity (87.5%) and specificity (94.4%) of ContrastAP (cutoff > 1.41) were comparable or significantly higher than those of all key imaging features (sensitivity, 40.3-68.1%; specificity, 72.2-94.4%), except for the discrete mass. CONCLUSIONS: Quantitative analysis of the lesion contrast using CE-MRI, particularly at AP, was helpful to differentiate focal-type AIP from PDA. The diagnostic performance of ContrastAP was mostly comparable or higher than those of the key imaging features. KEY POINTS: • Diagnosis of focal-type AIP vs. PDA using imaging techniques is extremely challenging. • Lesion contrast in the arterial-phase MRI differs significantly between focal-type AIP and PDA. • Quantitative analysis of lesion contrast using CE-MRI, particularly at the arterial phase, is helpful to differentiate focal-type AIP from PDA.

Comparison of diagnostic performance between CT and MRI in differentiating non-diffuse-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.

OBJECTIVES: To intraindividually compare the diagnostic performance of CT and MRI in differentiating non-diffuse-type autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDA). METHODS: Sixty-one patients with non-diffuse-type AIP and 122 patients with PDA, who underwent dynamic contrast-enhanced CT and MRI with MR pancreatography, were included. Two blinded radiologists independently rated their confidence in differentiating the two diseases on a 5-point scale, and the diagnostic performances of CT and MRI were compared. The presence of key imaging features to differentiate AIP and PDA were compared between CT and MRI. RESULTS: The area under the receiver operating characteristic curve was significantly greater on MRI (0.993-0.995) than on CT (0.953-0.976) for both raters (p≤0.035). The sensitivities of MRI were higher than those of CT for the diagnosis of AIP (88.5-90.2% vs. 77-80.3%, p≤0.07) and PDA (97.5-99.2% vs. 91.8-94.3%, p≤0.031) for both raters, although the difference for AIP was statistically marginal (p=0.07) for rater 1. In AIP, multiple pancreatic masses, delayed homogeneous enhancement of the pancreatic mass, and multiple main pancreatic duct (MPD) strictures were observed significantly more frequently using MRI than CT (p≤0.008). In PDA, discrete pancreatic mass and MPD stricture were observed significantly more frequently using MRI than CT (p≤0.012). CONCLUSIONS: The diagnostic performance of MRI is better for differentiating non-diffuse-type AIP from PDA, which is due to the superiority of MRI over CT in demonstrating the key distinguishing features of both diseases. KEY POINTS: • Imaging differential diagnosis of non-diffuse-type AIP and PDA is challenging. • MRI has better diagnostic performance than CT in differentiating non-diffuse-type AIP from PDA. • MRI is superior to CT in demonstrating key distinguishing features of non-diffuse-type AIP and PDA.

Real-world outcomes associated with afatinib use in patients with solid tumors harboring NRG1 gene fusions.

OBJECTIVES: Neuregulin-1 (NRG1) fusions may drive oncogenesis via constitutive activation of ErbB signaling. Hence, NRG1 fusion-driven tumors may be susceptible to ErbB-targeted therapy. Afatinib (irreversible pan-ErbB inhibitor) has demonstrated activity in individual patients with NRG1 fusion-positive solid tumors. This study collected real-world data on demographics, clinical characteristics, and clinical outcomes in this patient population. MATERIALS AND METHODS: In this retrospective, multicenter, non-comparative cohort study, physicians in the US-based Cardinal Health Oncology Provider Extended Network collected data from medical records of patients with NRG1 fusion-positive solid tumors who received afatinib (afatinib cohort) or other systemic therapies (non-afatinib cohort) in any therapy line. Objectives included demographics, clinical characteristics, and outcomes (overall response rate [ORR], progression-free survival [PFS], and overall survival [OS]). RESULTS: Patients (N = 110) with a variety of solid tumor types were included; 72 received afatinib, 38 other therapies. In the afatinib cohort, 70.8 % of patients received afatinib as second-line treatment and Eastern Cooperative Oncology Group performance status (ECOG PS) was 2-4 in 69.4 % at baseline. In the non-afatinib cohort, 94.7 % of patients received systemic therapy as first-line treatment and ECOG PS was 2-4 in 31.6 % at baseline. In the afatinib cohort, ORR was 37.5 % overall (43.8 % when received as first-line therapy); median PFS and OS were 5.5 and 7.2 months, respectively. In the non-afatinib cohort, ORR was 76.3 %; median PFS and OS were 12.9 and 22.6 months, respectively. CONCLUSION: This study provides real-world data on the characteristics of patients with NRG1 fusion-positive solid tumors treated with afatinib or other therapies; durable responses were observed in both groups. However, there were imbalances between the cohorts, and the study was not designed to compare outcomes. Further prospective/retrospective trials are required.

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.