Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

Preoperative Prediction of Long-Term Survival After Surgery in Patients with Resectable Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Although some clinical trials have demonstrated the benefits of neoadjuvant therapy for resectable pancreatic ductal adenocarcinoma (PDAC), its optimal candidate has not been clarified. This study aimed to detect predictive prognostic factors for resectable PDAC patients who underwent upfront surgery and identify patient cohorts with long-term survival without neoadjuvant therapy. PATIENTS AND METHODS: A total of 232 patients with resectable PDAC who underwent upfront surgery between January 2008 and December 2019 were evaluated. RESULTS: The median overall survival (OS) time and 5-year OS rate of resectable PDAC with upfront surgery was 31.5 months and 33.3%, respectively. Multivariate analyses identified tumor diameter in computed tomography (CT) ≤ 19 mm [hazard ratio (HR) 0.40, p < 0.001], span-1 within the normal range (HR 0.54, p = 0.023), prognostic nutritional index (PNI) ≥ 44.31 (HR 0.51, p < 0.001), and lymphocyte-to-monocyte ratio (LMR) ≥ 3.79 (HR 0.51, p < 0.001) as prognostic factors that influence favorable prognoses after upfront surgery. According to the prognostic prediction model based on these four factors, patients with four favorable prognostic factors had a better prognosis with a 5-year OS rate of 82.4% compared to others (p < 0.001). These patients had a high R0 resection rate and a low frequency of tumor recurrence after upfront surgery. CONCLUSIONS: We identified patients with long-term survival after upfront surgery by prognostic prediction model consisting of tumor diameter in CT, span-1, PNI, and LMR. Evaluation of anatomical, biological, nutritional, and inflammatory factors may be valuable to introduce an optimal treatment strategy for resectable PDAC.

Predictors for Long-Term Survival After Resection of Pancreatic Ductal Adenocarcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Improved systemic therapy has made long term (≥ 5 years) overall survival (LTS) after resection of pancreatic ductal adenocarcinoma (PDAC) increasingly common. However, a systematic review on predictors of LTS following resection of PDAC is lacking. METHODS: The PubMed, Embase, Scopus, and Cochrane CENTRAL databases were systematically searched from inception until March 2023. Studies reporting actual survival data (based on follow-up and not survival analysis estimates) on factors associated with LTS were included. Meta-analyses were conducted by using a random effects model, and study quality was gauged by using the Newcastle-Ottawa Scale (NOS). RESULTS: Twenty-five studies with 27,091 patients (LTS: 2,132, non-LTS: 24,959) who underwent surgical resection for PDAC were meta-analyzed. The median proportion of LTS patients was 18.32% (IQR 12.97-21.18%) based on 20 studies. Predictors for LTS included sex, body mass index (BMI), preoperative levels of CA19-9, CEA, and albumin, neutrophil-lymphocyte ratio, tumor grade, AJCC stage, lymphovascular and perineural invasion, pathologic T-stage, nodal disease, metastatic disease, margin status, adjuvant therapy, vascular resection, operative time, operative blood loss, and perioperative blood transfusion. Most articles received a "good" NOS assessment, indicating an acceptable risk of bias. CONCLUSIONS: Our meta-analysis pools all true follow up data in the literature to quantify associations between prognostic factors and LTS after resection of PDAC. While there appears to be evidence of a complex interplay between risk, tumor biology, patient characteristics, and management related factors, no single parameter can predict LTS after the resection of PDAC.

MR Elastography of the Pancreas: Bowel Preparation and Repeatability Assessment in Pancreatic Cancer Patients and Healthy Controls.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) stromal viscoelasticity can be measured using MR elastography (MRE). Bowel preparation regimens could affect MRE quality and knowledge on repeatability is crucial for clinical implementation. PURPOSE: To assess effects of four bowel preparation regimens on MRE quality and to evaluate repeatability and differentiate patients from healthy controls. STUDY TYPE: Prospective. POPULATION: 15 controls (41 ± 16 years; 47% female), 16 PDAC patients (one excluded, 66 ± 12 years; 40% female) with 15 age-/sex-matched controls (65 ± 11 years; 40% female). Final sample size was 25 controls and 15 PDAC. FIELD STRENGTH/SEQUENCE: 3-T, spin-echo echo-planar-imaging, turbo spin-echo, and fast field echo gradient-echo. ASSESSMENT: Four different regimens were used: fasting; scopolaminebutyl; drinking 0.5 L water; combination of 0.5 L water and scopolaminebutyl. MRE signal-to-noise ratio (SNR) was compared between all regimens. MRE repeatability (test-retest) and differences in shear wave speed (SWS) and phase angle (ϕ) were assessed in PDAC and controls. Regions-of-interest were defined for tumor, nontumorous (n = 8) tissue in PDAC, and whole pancreas in controls. Two radiologists delineated tumors twice for evaluation of intraobserver and interobserver variability. STATISTICAL TESTS: Repeated measures analysis of variance, coefficients of variation (CoVs), Bland-Altman analysis, (un)paired t-test, Mann-Whitney U-test, and Wilcoxon signed-rank test. P-value<0.05 was considered statistically significant. RESULTS: Preparation regimens did not significantly influence MRE-SNR. Therefore, the least burdensome preparation (fasting only) was continued. CoVs for tumor SWS were: intrasession (12.8%) and intersession (21.7%), and intraobserver (7.9%) and interobserver (10.3%) comparisons. For controls, CoVs were intrasession (4.6%) and intersession (6.4%). Average SWS for tumor, nontumor, and healthy tissue were: 1.74 ± 0.58, 1.38 ± 0.27, and 1.18 ± 0.16 m/sec (ϕ: 1.02 ± 0.17, 0.91 ± 0.07, and 0.85 ± 0.08 rad), respectively. Significant differences were found between all groups, except for ϕ between healthy-nontumor (P = 0.094). DATA CONCLUSION: The proposed bowel preparation regimens may not influence MRE quality. MRE may be able to differentiate between healthy tissue-tumor and tumor-nontumor. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Pancreatic juice cytology for diagnosing invasive pancreatic carcinoma/high-grade pancreatic intraepithelial neoplasia without visible tumors on endoscopic ultrasound.

OBJECTIVES: Pancreatic ductal adenocarcinoma (PDAC) with a diameter ≤10 mm and high-grade pancreatic intraepithelial neoplasia (HG-PanIN) require pre-operative diagnosis. Most cases present only indirect imaging findings without visible tumors on endoscopic ultrasound (EUS). Therefore, EUS-guided fine-needle aspiration/biopsy is not applicable. An alternative diagnostic method is pancreatic juice cytology (PJC) via endoscopic naso-pancreatic drainage (ENPD-PJC), which is not the standard practice. This study aimed to investigate ENPD-PJC for diagnosing suspected PDAC/HG-PanIN cases without visible tumors on EUS. METHODS: Data of patients with suspected PDAC/HG-PanIN without visible tumors who underwent PJC were retrospectively evaluated. One PJC sample was collected during endoscopic retrograde pancreatography (ERP-PJC), and 12 samples were collected during ENPD-PJC, 3-hourly for cytological analysis. ERP-PJC, ERP/ENPD-PJC, and ENPD-PJC positivity indicated cytologically positive samples. Patients with positive/negative PJC with follow-up for <4-years were excluded as undiagnosed cases. A non-malignant diagnosis was based on histopathological absence/stable imaging findings for ≥4-years. The primary endpoint was to demonstrate that ERP/ENPD-PJC has a higher diagnostic ability than ERP-PJC. RESULTS: Twenty-two patients with histopathologically diagnosed PDAC/HG-PanIN and 31 with a non-malignant diagnosis were enrolled. ERP-PJC, ERP/ENPD-PJC, and ENPD-PJC showed sensitivities of 36.4 %, 86.4 %, and 77.3 %, specificities of 93.5 %, 87.1 %, and 93.5 %, and accuracies of 69.8 %, 86.7 %, and 86.7 %, respectively. ERP/ENPD-PJC and ENPD-PJC demonstrated superior sensitivity and accuracy compared to ERP-PJC. A greater occurrence of positive outcomes markedly distinguished true positives from false positives. CONCLUSIONS: ERP/ENPD-PJC and ENPD-PJC had higher diagnostic accuracies for PDAC/HG-PanIN without visible tumors on EUS. ENPD-PJC is recommended for the diagnosis of these lesions.

Imaging classification of pancreatic ductal adenocarcinoma with histological large duct pattern.

OBJECTIVES: To investigate the imaging features of pancreatic ductal adenocarcinoma (PDAC) with histological large duct pattern. METHODS: Our study included 37 patients (mean age, 66.5 years; 22 women) with surgically proven PDAC with histological large duct pattern, whose imaging features were classified into four types: Type I, solid mass; Type II, predominantly cystic mass with intracystic solid components; Type III, predominantly solid mass with intratumoral cysts; and Type IV, solid mass with peritumoral retention cysts or pseudocysts. Two radiologists independently analyzed both CT and MRI images for the morphological type, presence of abrupt main pancreatic duct (MPD) cutoff, adjacent vascular invasion, diffusion restriction, and reached consensus. RESULTS: On CT, 26 patients (70.3%) had Type I tumors, five (13.5%) had Type II, three (8.1%) had Type III, and three (8.1%) had Type IV. Among the 26 patients with Type I tumors on CT, 16 had tumors with multiple intratumoral cysts within the solid mass on MRI and were subsequently classified as Type III. Accordingly, 10 patients (27.0%) were classified as Type I, five (13.5%) as Type II, 19 (51.7%) as Type III, and three (8.1%) as Type IV on MRI. Of the 37 patients, 27 (73.0%) had an abrupt MPD cutoff, 15 (40.5%) had adjacent vascular invasion, and 25 (67.6%) had diffusion restriction on MRI. CONCLUSIONS: Predominantly solid pancreatic masses with small intratumoral cysts visualized on MRI may be a characteristic imaging finding of PDAC with histological large duct pattern, and differentiate it from conventional PDAC or other cystic pancreatic tumors. CLINICAL RELEVANCE STATEMENT: Radiologists should be familiar with the various imaging features of PDAC with histological large duct pattern and should be aware that it may mimic other solid or cystic tumors of the pancreas. KEY POINTS: Imaging features of pancreatic ductal adenocarcinoma with histological large duct pattern can be classified into four types. This pathology more frequently appears as a predominantly solid mass with intratumoral cysts on MRI than on CT. Adding MRI to CT may help identify pancreatic ductal adenocarcinoma with histological large duct pattern.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas.

PURPOSE: 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). METHODS: 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. RESULTS: The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076). CONCLUSIONS: Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

Combination of modified FOLFIRINOX with stereotactic body radiotherapy as an induction therapy for locally advanced pancreatic adenocarcinoma - a prospective single-arm study.

Introduction: Radical resection is the only potentially curative treatment for pancreatic adenocarcinoma; however, only a minor fraction of patients are eligible for resection. Induction therapy may be offered to patients, but the response rate in cases with significant vascular involvement is limited. This study aimed to evaluate the efficacy and safety of modified of FOLFIRINOX chemotherapy (mFFX) + stereotactic body radiotherapy (SBRT) in combination as induction therapy for locally advanced pancreatic carcinoma. The primary endpoints were the resection rate and one-year overall survival (OS). The secondary endpoints were progression-free survival (PFS), toxicity, and quality of live (QoL). Material and methods: Thirty patients with locally advanced pancreatic adenocarcinoma were treated with 6 cycles of mFFX, followed by SBRT and additional 3 cycles of mFFX. The response was measured prior to SBRT and after regimen completion. In the absence of disease progression, the patients were referred for surgery. The patients were requested to complete quality of life questionnaires (QLQ)-C30 and QLQ-PAN26 questionnaires biweekly. Results: On the first evaluation, disease control was noted in 26 (86.7%) patients. Stereotactic body radiotherapy was performed in 20 patients. Twelve patients underwent laparotomy, with radical resection possible in 3 cases. The one-year OS rate was 63.3%. Overall, 11 grade ≥ 3 adverse events were noted. No deterioration in the overall QoL was observed. The median PFS was 7.53 months. Conclusions: The expected resection rate of ≥ 30% was not achieved. However, the combination was associated with good local control, low adverse event rate, and good QoL, which advocate its further investigation in this clinical situation.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

OBJECTIVES: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.

Macroscopic qualitative evaluation of solid pancreatic lesion specimens from endoscopic ultrasound-guided fine needle aspiration/biopsies.

OBJECTIVES: To pathologically clarify the macroscopic features of endoscopic ultrasound-guided fine needle aspiration/biopsy (EUS-FNA/B) specimens in representative pancreatic diseases and establish tissue-handling standards based on the macroscopic findings. METHODS: We gathered EUS-FNA/B specimens of cases at our institution with the final diagnoses of pancreatic ductal adenocarcinoma (PDAC, n = 172), neuroendocrine tumor (NET, n = 19), and chronic inflammatory lesion (CIL, n = 24) including autoimmune pancreatitis. We classified the specimens' macroscopic features in five categories (red strings, mixed-red-and-white strings, white cores, gray tissues, gelatinous tissues) and compared the specimens' features on cytological and histological slides. RESULTS: All five macroscopic categories were observed in variable combinations in the PDACs; red strings and white cores predominated in the NETs and CILs. White cores represented neoplastic (PDAC, NET) or lesion (CIL) tissues. Mixed-red-and-white strings were unique to PDACs and contained cancerous cells. Neoplastic cells were numerous in red strings in NETs but not the other groups. Gray and gelatinous tissues represented necrosis and mucin, respectively, and the former were almost exclusively observed in PDACs. Red strings, mixed-red-and-white strings, and white cores were suitable for histological examination, whereas gray and gelatinous tissues were suitable for cytological examination. The white cores, mixed-red-and-white strings, and gelatinous tissues may be composed of non-neoplastic tissues such as contaminated gastrointestinal epithelium. In seven PDACs, although white cores were obtained, a histological diagnosis was not established. CONCLUSIONS: Macroscopic evaluations of EUS-FNA/B can enable the identification of specimen components and a possible diagnosis. They also contribute to the selection of the optimal tissue-handling methods.

Can we rely on contrast-enhanced CT to identify pancreatic ductal adenocarcinoma? A population-based study in sensitivity and factors associated with false negatives.

OBJECTIVES: To determine the sensitivity of contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC) and identify factors associated with false negatives (FNs). METHODS: Patients diagnosed with PDAC in 2014-2015 were retrospectively identified by a cancer registry. CECTs performed during the diagnostic interval were retrospectively classified as true positive (TP), indeterminate, or FN. Sensitivity TP/(TP+FN) was calculated for all CECTs and the following subgroups: protocol (uniphasic vs. biphasic); tumor size (≤ 2 cm vs. > 2 cm); and resectability (potentially resectable vs. unresectable). Multivariate logistic regression was performed to assess which of the following factors were associated with FN: clinical suspicion of PDAC; size >2 cm; presence of metastases; protocol; isoattenuating tumor; and potentially resectable disease on imaging. RESULTS: In total, 176 CECTs (127 uniphasic; 49 biphasic) in 154 patients (90 men, mean age 72 ± 11 years) were included. Sensitivity was 125/149 (83.9%) overall and 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. Sensitivity was decreased for tumors ≤ 2 cm (45.4% vs. 90.6%), no liver metastases (78.0% vs. 95.9%), and potentially resectable disease (65.3% vs. 93.0%). Factors significantly associated with FN were clinical suspicion (OR, 0.24, 95% CI: 0.07-0.75), size>2 cm (OR, 0.10, 95% CI: 0.02-0.44), absence of liver metastases (OR, 4.94, 95% CI: 1.29-22.99), and potentially resectable disease (OR, 4.13, 95% CI: 1.07-16.65). CONCLUSIONS: In our population, the overall sensitivity of CECT to detect PDAC is 83.9%; however, this is substantially lower in several scenarios, including patients with potentially resectable disease. This finding has important implications for patient outcomes and efforts to maximize CECT sensitivity should be sought. CLINICAL RELEVANCE STATEMENT: The sensitivity of CECT to detect PDAC is significantly decreased in the setting of sub-2 cm tumors and potentially resectable disease. A dedicated biphasic pancreatic CECT protocol has higher sensitivity and should be applied in patients with suspected pancreatic disease. KEY POINTS: • The sensitivities of contrast-enhanced CT for the detection of PDAC were 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. • The sensitivity of contrast-enhanced CT was decreased for small tumors ≤ 2 cm (45.4% vs. 90.6%), if there were no liver metastases (78.0% vs. 95.9%), and with potentially resectable disease (65.3% vs. 93.0%). • Absence of liver metastases (OR, 4.94, 95% CI: 1.29-22.99) and potentially resectable disease (OR, 4.13, 95% CI: 1.07-16.65) were associated with a false--negative (FN) CT result; suspicion of malignancy on the imaging requisition (OR, 0.24, 95% CI: 0.07-0.75) and size > 2 cm (OR, 0.10, 95% CI: 0.02-0.44) were negatively associated with FN.

Clinical benefits of pulmonary resection for lung metastases from pancreatic cancer.

PURPOSE: Systemic chemotherapy is generally used for metastatic pancreatic cancer; however, pulmonary resection may be a treatment option for lung oligometastases from pancreatic cancer. The current study aimed to clarify the oncological outcomes and clinical benefits of pulmonary resection for lung metastases. METHODS: Of 510 patients who underwent pancreatic resection for pancreatic cancer, 44 patients with recurrence of isolated lung metastases and one patient with simultaneous lung metastases were evaluated. RESULTS: Of the 45 patients, 20 patients were selected as candidates for pulmonary resection based on clinical factors such as recurrence-free interval (RFI) from pancreatectomy to lung metastases, number of lung metastases, and serum CA19-9 level. The post-recurrent survival of patients with pulmonary resection was significantly better than that of patients without pulmonary resection. Fourteen of the 20 patients with pulmonary resection developed tumor recurrence with a median disease-free survival (DFS) of 15 months. Univariate analyses revealed that an RFI from pancreatectomy to lung metastases of ≥28 months was associated with better DFS after pulmonary resection. Of the 14 patients with an RFI of ≥28 months, pulmonary resection resulted in prolonged chemotherapy-free interval in 12 patients. Furthermore, repeat pulmonary resection for recurrent tumors after pulmonary resection led to further cancer-free interval in some cases. CONCLUSIONS: Although many patients had tumor recurrence after pulmonary resection, pulmonary resection for lung metastases from pancreatic cancer may provide prolonged cancer-free interval without the need for chemotherapy. Pulmonary resection should be performed for the patients with a long RFI from pancreatectomy to lung metastases.

Iron-Dependent Cell Death: A New Treatment Approach against Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating tumor type where a very high proportion of people diagnosed end up dying from cancer. Surgical resection is an option for only about 20% of patients, where the 5-year survival increase ranges from 10 to 25%. In addition to surgical resection, there are adjuvant chemotherapy schemes, such as FOLFIRINOX (a mix of Irinotecan, oxaliplatin, 5-Fluorouraci and leucovorin) or gemcitabine-based treatment. These last two drugs have been compared in the NAPOLI-3 clinical trial, and the NALIRIFOX arm was found to have a higher overall survival (OS) (11.1 months vs. 9.2 months). Despite these exciting improvements, PDAC still has no effective treatment. An interesting approach would be to drive ferroptosis in PDAC cells. A non-apoptotic reactive oxygen species (ROS)-dependent cell death, ferroptosis was first described by Dixon et al. in 2012. ROS are constantly produced in the tumor cell due to high cell metabolism, which is even higher when exposed to chemotherapy. Tumor cells have detoxifying mechanisms, such as Mn-SOD or the GSH-GPX system. However, when a threshold of ROS is exceeded in the tumor cell, the cell's antioxidant systems are overwhelmed, resulting in lipid peroxidation and, ultimately, ferroptosis. In this review, we point out ferroptosis as an approach to consider in PDAC and propose that altering the cellular ROS balance by combining oxidizing agents or with inhibitors of the main cellular detoxifiers triggers ferroptosis in PDAC.

Interobserver Variability and Accuracy of Preoperative CT and MRI in Pancreatic Ductal Adenocarcinoma Size Estimation: A Retrospective Cohort Study.

Purpose: To assess interobserver variability and accuracy of preoperative computed tomography (CT) and magnetic resonance imaging (MRI) in pancreatic ductal adenocarcinoma (PDAC) size estimation using surgical specimens as standard of reference. Methods: Patients with PDAC who underwent preoperative CT and MRI examinations before surgery were included. PDAC largest axial dimension was measured by 2 readers on 8 MRI sequence and 2 CT imaging phases (pancreatic parenchymal and portal venous). Measurements were compared to actual tumour size at pathologic examination. Interobserver variability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plots. Differences in tumour size (Δdiameter) between imaging and actual tumour size were searched using Wilcoxon rank sum test. Results: Twenty-nine patients (16 men; median age, 70 years) with surgically resected PDAC were included. Interobserver reproducibility was good to excellent for all MRI sequences and the 2 CT imaging phases with ICCs between .862 (95%CI: .692-.942) for fat-saturated in-phase T1-weighted sequence and .955 (95%CI: .898-.980) for portal venous phase CT images. Best accuracy in PDAC size measurement was obtained with pancreatic parenchymal phase CT images with median Δdiameters of -2 mm for both readers, mean relative differences of -9% and -6% and no significant differences with dimensions at histopathological analysis (P = .051). All MRI sequences led to significant underestimation of PDAC size (median Δdiameters, -6 to -1 mm; mean relative differences, -21% to -11%). Conclusions: Most accurate measurement of PDAC size is obtained with CT images obtained during the pancreatic parenchymal phase. MRI results in significant underestimation of PDAC size.

Optimal lymph-node dissection for pancreatic tail cancer.

PURPOSE: The optimal range of lymph-node dissection for pancreatic tail cancer remains unclear. We investigated the location and frequency of lymph-node metastases to identify the correct range of lymph-node dissection for pancreatic tail cancer. METHODS: We analyzed clinical data retrospectively, on patients who underwent distal pancreatectomy for resectable left-sided pancreatic cancer, between February, 2006 and March, 2021. Eligible patients were divided into two groups according to the tumor location: those with pancreatic tail cancer (Pt group) and those with pancreatic body or body and tail cancer (non-Pt group). RESULTS: Of the 96 patients analyzed, 61 (64%) were assigned to the Pt group and 35 (36%) were assigned to the non-Pt group. Metastases to stations 7, 8, 9, 10, 11, 14, and 18 were found in 0 (0%), 0 (0%), 0 (0%), 4 (7%), 18 (30%), 2 (4%), and 10 (17%) patients in the Pt group, and in 1 (3%), 4 (12%), 2 (6%), 1 (3%), 18 (51%), 3 (9%), and 6 (17%) patients in the non-Pt group, respectively. CONCLUSION: Lymph-node dissection at stations 7, 8, and 9 might not be necessary in patients with resectable pancreatic cancer confined to the pancreatic tail.

Ex-vivo human pancreatic specimen evaluation by 7 Tesla MRI: a prospective radiological-pathological correlation study.

PURPOSE: To compare the characteristics detected by 7Tesla (7 T) MR and the histological composition of ex-vivo specimens from lesions diagnosed at preoperative CT scan as Pancreatic Ductal Adenocarcinoma (PDAC). MATERIALS AND METHODS: Ten pancreatic specimens were examined. The 7 T imaging protocol included both morphologic and quantitative sequences; the latter was acquired by conventional methods and a novel multiparametric method, the magnetic resonance fingerprinting (MRF) sequence. Two radiologists reviewed the images to: (1) evaluate the quality of the morphological and quantitative sequences by assigning an "image consistency score" on a 4-point scale; (2) identify the lesion, recording its characteristics; (3) perform the quantitative analysis on "target lesion" and "non target tissue". Finally, the specimen was analysed by two pathologists. RESULTS: Seven out of 10 lesions were PDAC, 2/10 were biliary carcinomas, whereas one lesion was an ampullary adenocarcinoma. The quality of the morphological sequences was judged "excellent". The "image consistency score" for the conventional quantitative sequences and MRF were 2.8 ± 0.42 and 2.9 ± 0.57; the "overall MR examination score" was 3.5 ± 0.53. A statistical correlation was found between the relaxation time values of conventional and MRF T1-weighted sequences (p < 0.0001), as well as between conventional and MRF fat- and water-fraction maps (p < 0.05). The "target lesion" and "non target tissue" relaxation time values were statistically different according to conventional T1-, T2-weighted, and MRF T1-weighted sequences. CONCLUSIONS: Conventional T1-, T2-weighted sequences and MRF derived relaxometries may be useful in differentiating between tumour and non-target pancreatic tissue. Moreover, the MRF sequence can be used to obtain reliable relaxation time data.

Gemcitabine Resistance in Pancreatic Ductal Carcinoma Cell Lines Stems from Reprogramming of Energy Metabolism.

Pancreatic ductal adenocarcinoma (PDAC) is associated with poor prognosis because it is often detected at an advanced stage, and drug resistance interferes with treatment. However, the mechanism underlying drug resistance in PDAC remains unclear. Here, we investigated metabolic changes between a parental PDAC cell line and a gemcitabine (GEM)-resistant PDAC cell line. We established a GEM-resistant cell line, MIA-G, from MIA-PaCa-2 parental (MIA-P) cells using continuous therapeutic-dose GEM treatment. MIA-G cells were also more resistant to 5-fluorouracil in comparison to MIA-P cells. Metabolic flux analysis showed a higher oxygen consumption rate (OCR) in MIA-G cells than in MIA-P cells. Notably, OCR was suppressed by GEM treatment only in MIA-G cells. GEM treatment increased mitochondrial membrane potential and mitochondrial reactive oxygen species (ROS) in MIA-P cells, but not in MIA-G cells. Glutamine uptake and peroxidase levels were elevated in MIA-G cells. The antioxidants N-acetyl-L-cysteine and vitamin C increased the sensitivity to GEM in both cell lines. In MIA-G cells, the expression of the mitochondrial transcription factor A also decreased. Furthermore, rotenone reduced the sensitivity of MIA-P cells to GEM. These findings suggest that the suppression of oxidative phosphorylation contributes to GEM resistance by reducing ROS production. Our study provides a new approach for reducing GEM resistance in PDAC.

The Impact of Epithelial-Mesenchymal Transition and Metformin on Pancreatic Cancer Chemoresistance: A Pathway towards Individualized Therapy.

Globally, pancreatic ductal adenocarcinoma remains among the most aggressive forms of neoplastic diseases, having a dismal prognostic outcome. Recent findings elucidated that epithelial-mesenchymal transition (EMT) can play an important role in pancreatic tumorigenic processes, as it contributes to the manifestation of malignant proliferative masses, which impede adequate drug delivery. An organized literature search with PubMed, Scopus, Microsoft Academic and the Cochrane library was performed for articles published in English from 2011 to 2021 to review and summarize the latest updates and knowledge on the current understanding of EMT and its implications for tumorigenesis and chemoresistance. Furthermore, in the present paper, we investigate the recent findings on metformin as a possible neoadjuvant chemotherapy agent, which affects EMT progression and potentially provides superior oncological outcomes for PDAC patients. Our main conclusions indicate that selectively suppressing EMT in pancreatic cancer cells has a promising therapeutic utility by selectively targeting the chemotherapy-resistant sub-population of cancer stem cells, inhibiting tumor growth via EMT pathways and thereby improving remission in PDAC patients. Moreover, given that TGF-ß1-driven EMT generates the migration of tumor-initiating cells by directly linking the acquisition of abnormal cellular motility with the maintenance of tumor initiating potency, the chemoprevention of TGF-ß1-induced EMT may have promising clinical applications in the therapeutic management of PDAC outcomes.

[Diagnostics and clinical management of premalignant diseases of the pancreas]. / Diagnostik und klinisches Management prämaligner Erkrankungen des Pankreas.

Pancreatic cancer is one of the most aggressive solid tumors and still has a poor prognosis. A delayed diagnosis at advanced stages and a poor response to systemic treatment frequently make a curative treatment impossible. Therefore, the identification of high-risk patients and screening them regularly is the most promising approach to improve the prognosis. Chronic pancreatitis as well as neoplastic pancreatic cysts can greatly increase the risk of developing pancreatic cancer. Furthermore, familial syndromes and germline mutations also confer an increased risk for development of pancreatic cancer. This article provides an overview of the various premalignant diseases of the pancreas. The value of the various imaging modalities, such as magnetic resonance imaging and endosonography are particularly discussed as well as the screening interval and the indications for surgical treatment are explained.