Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Diagnosis of chronic pancreatitis using semi-quantitative MRI features of the pancreatic parenchyma: results from the multi-institutional MINIMAP study.

PURPOSE: To determine the diagnostic performance of parenchymal MRI features differentiating CP from controls. METHODS: This prospective study performed abdominal MRI scans at seven institutions, using 1.5 T Siemens and GE scanners, in 50 control and 51 definite CP participants, from February 2019 to May 2021. MRI parameters included the T1-weighted signal intensity ratio of the pancreas (T1 score), arterial-to-venous enhancement ratio (AVR) during venous and delayed phases, pancreas volume, and diameter. We evaluated the diagnostic performance of these parameters individually and two semi-quantitative MRI scores derived using logistic regression: SQ-MRI Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume). RESULTS: When compared to controls, CP participants showed a significantly lower mean T1 score (1.11 vs. 1.29), AVR venous (0.86 vs. 1.45), AVR delayed (1.07 vs. 1.57), volume (54.97 vs. 80.00 ml), and diameter of the head (2.05 vs. 2.39 cm), body (2.25 vs. 2.58 cm), and tail (1.98 vs. 2.51 cm) (p < 0.05 for all). AUCs for these individual MR parameters ranged from 0.66 to 0.79, while AUCs for the SQ-MRI scores were 0.82 and 0.81 for Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume), respectively. After propensity-matching adjustments for covariates, AUCs for Models A and B of the SQ-MRI scores increased to 0.92 and 0.93, respectively. CONCLUSION: Semi-quantitative parameters of the pancreatic parenchyma, including T1 score, enhancement ratio, pancreas volume, diameter and multi-parametric models combining these parameters are helpful in diagnosis of CP. Longitudinal analyses including more extensive population are warranted to develop new diagnostic criteria for CP.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium.

ABSTRACT: This core component of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study will examine the hypothesis that advanced magnetic resonance imaging (MRI) techniques can reflect underlying pathophysiologic changes and provide imaging biomarkers that predict diabetes mellitus (DM) after acute pancreatitis (AP). A subset of participants in the DREAM study will enroll and undergo serial MRI examinations using a specific research protocol. The aim of the study is to differentiate at-risk individuals from those who remain euglycemic by identifying parenchymal features after AP. Performing longitudinal MRI will enable us to observe and understand the natural history of post-AP DM. We will compare MRI parameters obtained by interrogating tissue properties in euglycemic, prediabetic, and incident diabetes subjects and correlate them with metabolic, genetic, and immunological phenotypes. Differentiating imaging parameters will be combined to develop a quantitative composite risk score. This composite risk score will potentially have the ability to monitor the risk of DM in clinical practice or trials. We will use artificial intelligence, specifically deep learning, algorithms to optimize the predictive ability of MRI. In addition to the research MRI, the DREAM study will also correlate clinical computed tomography and MRI scans with DM development.

Quantitative MRI of chronic pancreatitis: results from a multi-institutional prospective study, magnetic resonance imaging as a non-invasive method for assessment of pancreatic fibrosis (MINIMAP).

PURPOSE: To determine if quantitative MRI techniques can be helpful to evaluate chronic pancreatitis (CP) in a setting of multi-institutional study. METHODS: This study included a subgroup of participants (n = 101) enrolled in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED) study (NCT03099850) from February 2019 to May 2021. MRI was performed on 1.5 T using Siemens and GE scanners at seven clinical centers across the USA. Quantitative MRI parameters of the pancreas included T1 relaxation time, extracellular volume (ECV) fraction, apparent diffusion coefficient (ADC), and fat signal fraction. We report the diagnostic performance and mean values within the control (n = 50) and CP (n = 51) groups. The T1, ECV and fat signal fraction were combined to generate the quantitative MRI score (Q-MRI). RESULTS: There was significantly higher T1 relaxation time; mean 669 ms (± 171) vs. 593 ms (± 82) (p = 0.006), ECV fraction; 40.2% (± 14.7) vs. 30.3% (± 11.9) (p < 0.001), and pancreatic fat signal fraction; 12.2% (± 5.5) vs. 8.2% (± 4.4) (p < 0.001) in the CP group compared to controls. The ADC was similar between groups (p = 0.45). The AUCs for the T1, ECV, and pancreatic fat signal fraction were 0.62, 0.72, and 0.73, respectively. The composite Q-MRI score improved the diagnostic performance (cross-validated AUC: 0.76). CONCLUSION: Quantitative MR parameters evaluating the pancreatic parenchyma (T1, ECV fraction, and fat signal fraction) are helpful in the diagnosis of CP. A Q-MRI score that combines these three MR parameters improves diagnostic performance. Further studies are warranted with larger study populations including patients with acute and recurrent acute pancreatitis and longitudinal follow-ups.

T1 signal intensity ratio of the pancreas as an imaging biomarker for the staging of chronic pancreatitis.

PURPOSE: Our purpose was to validate the T1 SIR (T1 score) as an imaging biomarker for the staging of CP in a large, multi-institutional, prospective study. METHODS: The prospective study population included 820 participants enrolled in the PROCEED study from nine clinical centers between June 2017 and December 2021. A radiologist at each institution used a standardized method to measure the T1 signal intensity of the pancreas and the reference organs (spleen, paraspinal muscle, liver), which was used to derive respective T1 scores. Participants were stratified according to the seven mechanistic stages of chronic pancreatitis (MSCP 0-6) based on their clinical history, MRCP, and CT findings. RESULTS: The mean pancreas-to-spleen T1 score was 1.30 in participants with chronic abdominal pain, 1.22 in those with acute or recurrent acute pancreatitis, and 1.03 in definite CP. After adjusting for covariates, we observed a linear, progressive decline in the pancreas-to-spleen T1 score with increasing MSCP from 0 to 6. The mean pancreas-to-spleen T1 scores were 1.34 (MSCP 0), 1.27 (MSCP 1), 1.21 (MSCP 2), 1.16 (MSCP 3), 1.18 (MSCP 4), 1.12 (MSCP 5), and 1.05 (MSCP 6) (p < 0.0001). The pancreas-to-liver and pancreas-to-muscle T1 scores showed less linear trends and wider confidence intervals. CONCLUSION: The T1 score calculated by SIR of the pancreas-to-spleen shows a negative linear correlation with the progression of chronic pancreatitis. It holds promise as a practical imaging biomarker in evaluating disease severity in clinical research and practice.

Chemical Exchange Saturation Transfer for Pancreatic Ductal Adenocarcinoma Evaluation.

OBJECTIVES: The aims of the study are to evaluate the feasibility of using pH-sensitive magnetic resonance imaging, chemical exchange saturation transfer (CEST) in pancreatic imaging and to differentiate pancreatic ductal adenocarcinoma (PDAC) with the nontumor pancreas (upstream and downstream) and normal control pancreas. METHODS: Sixteen CEST images with PDAC and 12 CEST images with normal volunteers were acquired and magnetization transfer ratio with asymmetric analysis were measured in areas of PDAC, upstream, downstream, and normal control pancreas. One-way analysis of variance and receiver operating characteristic curve were used to differentiate tumor from nontumor pancreas. RESULTS: Areas with PDAC showed higher signal intensity than upstream and downstream on CEST images. The mean (standard deviation) values of magnetization transfer ratio with asymmetric analysis were 0.015 (0.034), -0.044 (0.030), -0.019 (0.027), and -0.037 (0.031), respectively, in PDAC area, upstream, downstream, and nontumor area in patient group and -0.008 (0.024) in normal pancreas. Significant differences were found between PDAC and upstream ( P < 0.001), between upstream and normal pancreas ( P = 0.04). Area under curve is 0.857 in differentiating PDAC with nontumor pancreas. CONCLUSIONS: pH-sensitive CEST MRI is feasible in pancreatic imaging and can be used to differentiate PDAC from nontumor pancreas. This provides a novel metabolic imaging method in PDAC.

Segmentation of Pancreatic Subregions in Computed Tomography Images.

The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation-one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.

Outcomes Following Percutaneous Cholecystostomy Tube Placement for Acalculous Versus Calculous Cholecystitis.

BACKGROUND: Acute acalculous cholecystitis (AAC) is often diagnosed in critically ill patients. Percutaneous cholecystostomy tube (PCT) placement facilitates less invasive gallbladder decompression in patients who are poor surgical candidates. Specific guidelines for optimal management of AAC patients following PCT placement remain to be defined. We hypothesize that AAC patients are at lower risk of recurrent cholecystitis than acute calculous cholecystitis (ACC) patients and do not require cholecystectomy after PCT placement. METHODS: A retrospective review of patients who underwent PCT placement for AAC or ACC between 6/1/2007 and 5/31/2019 was performed. Primary outcome was recurrent cholecystitis and interval cholecystectomy for patients surviving 30 days after PCT placement. Secondary outcome was 30 day mortality. A cox regression model calculated the adjusted hazard ratio (AHR) for the outcomes. RESULTS: Eighty-four AAC and 85 ACC patients underwent PCT placement. Compared to ACC patients, more AAC patients were male (72.6 vs. 48.2%; p < 0.01), younger (median age 62 vs. 73 years; p < 0.01), and required intensive care (69.0 vs. 52.9%; p = 0.04), with lower median Charlson Comorbidity Index (4.0 vs. 6.0; p < 0.01). 30 day mortality was higher among AAC patients than ACC patients (45.2 vs. 21.2%; p < 0.01). 2/24 (8.3%) AAC patients and 5/31 (16.1%) ACC patients developed recurrent cholecystitis at a median 208.0 days (IQR:64.0-417.0) after PCT placement and 115.0 days (IQR:7.0-403.0) following PCT removal. Cox regression analysis demonstrated that AAC patients had lower likelihood of interval cholecystectomy compared to ACC patients (AHR 2.35; 95% CI:1.11,4.96). CONCLUSION: Recurrent cholecystitis is rare in patients surviving 30 days following PCT placement. When compared with ACC patients, fewer AAC patients require cholecystectomy.

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

Percutaneous Cholecystostomy Tube for Acute Cholecystitis: Quantifying Outcomes and Prognosis.

BACKGROUND: Percutaneous cholecystostomy tubes (PCT) are utilized in the management of acute cholecystitis in patients deemed unsuitable for surgery. However, the drive for these decisions and the outcomes remain understudied. We sought to characterize the practices and utilization of PCT and evaluate associated outcomes at an urban medical center. METHODS: Patients undergoing PCT placement over a 12-y study period ending May 2019 were reviewed. Demographics, clinical presentation, labs, imaging studies, and outcomes were abstracted. The primary and secondary outcomes were 30-d mortality and interval cholecystectomy, respectively. RESULTS: Two hundred and four patients met inclusion criteria: 59.3% were male with a median age of 67.5 y and a National Surgical Quality Improvement Program (NSQIP) risk of serious complication of 8.0%. Overall, 57.8% of patients were located in an intensive care unit setting. The majority (80.9%) had an ultrasound and 48.5% had a hepatobiliary iminodiacetic acid scan. The overall 30-d mortality was 31.9%: 41.5% for intensive care unit and 18.6% for ward patients (P < 0.01). Of patients surviving beyond 30 d (n = 139), the PCT was removed from 106 (76.3%), and a cholecystectomy was performed in 55 (39.6%) at a median interval of 58.0 d. A forward logistic regression identified total bilirubin (Adjusted Odds Ratio: 1.12, adjusted P < 0.01) and NSQIP risk of serious complication (Adjusted Odds Ratio: 1.16, adjusted P < 0.01) as the only predictors for 30-d mortality. CONCLUSIONS: Patients selected for PCT placement have a high mortality risk. Despite subsequent removal of the PCT, the majority of surviving patients did not undergo an interval cholecystectomy. Total bilirubin and NSQIP risk of serious complication are useful adjuncts in predicting 30-d mortality in these patients.

Venous bullet embolism to the right ventricle: Case report and review of management.

Intravascular missile emboli to the right heart should be retrieved surgically if the risk of surgical complication due to sternotomy and cardiotomy is low. Endovascular retrieval is another possible method of extraction to be considered.

Feasibility and safety study of 22-gauge endoscopic ultrasound (EUS) needles for portal vein sampling in a swine model.

Background and study aims Endoscopic ultrasound (EUS) has been used for portal vein sampling in patients with pancreaticobiliary cancers for enumerating circulating tumor cells but is not yet a standard procedure. Further evaluation is needed to refine the methodology. Therefore, we evaluated the feasibility and safety of 19-gauge (19G) versus a 22-gauge (22 G) EUS fine-needle aspiration needles for portal vein sampling in a swine model. Methods Celiotomy was performed on two farm pigs. Portal vein sampling occurred transhepatically. We compared 19 G and 22 G needles coated interiorly with saline, heparin or ethylenediaminetetraacetic acid (EDTA). Small- (10 mL) and large- (25 mL) volume blood collections were evaluated. Two different collection methods were tested: direct-to-vial and suction syringe. A bleeding risk trial for saline-coated 19 G and 22 G needles was performed by puncturing the portal vein 20 times. Persistent bleeding after 3 minutes was considered significant. Results All small-volume collection trials were successful except for 22 G saline-coated needles with direct-to-vial method. All large-volume collection trials were successful when using suction syringe; direct-to-vial method for both 19 G and 22 G needles were unsuccessful. Collection times were shorter for 19 G vs. 22 G needles for both small and large-volume collections ( P  < 0.05). Collection times for saline-coated 22 G needles were longer compared to heparin/EDTA-coated ( P  < 0.05). Bleeding occurred in 10 % punctures with 19 G needles compared to 0 % with 22 G needles. Conclusion The results of this animal study demonstrate the feasibility and the safety of using 22 G needles for portal vein sampling and can form the basis for a pilot study in patients.

Inter-observer variability of radiologists for Cambridge classification of chronic pancreatitis using CT and MRCP: results from a large multi-center study.

PURPOSE: Determine inter-observer variability among radiologists in assigning Cambridge Classification (CC) of chronic pancreatitis (CP) based on magnetic resonance imaging (MRI)/magnetic resonance cholangiopancreatography (MRCP) and contrast-enhanced CT (CECT). METHODS: Among 422 eligible subjects enrolled into the PROCEED study between 6/2017 and 8/2018, 39 were selected randomly for this study (chronic abdominal pain (n = 8; CC of 0), suspected CP (n = 22; CC of 0, 1 or 2) or definite CP (n = 9; CC of 3 or 4). Each imaging was scored by the local radiologist (LRs) and three of five central radiologists (CRs) at other consortium sites. The CRs were blinded to clinical data and site information of the participants. We compared the CC score assigned by the LR with the consensus CC score assigned by the CRs. The weighted kappa statistic (K) was used to estimate the inter-observer agreement. RESULTS: For the majority of subjects (34/39), the group assignment by LR agreed with the consensus composite CT/MRCP score by the CRs (concordance ranging from 75 to 89% depending on cohort group). There was moderate agreement (63% and 67% agreed, respectively) between CRs and LRs in both the CT score (weighted Kappa [95% CI] = 0.56 [0.34, 0.78]; p-value = 0.57) and the MR score (weighted Kappa [95% CI] = 0.68 [0.49, 0.86]; p-value = 0.72). The composite CT/MR score showed moderate agreement (weighted Kappa [95% CI] = 0.62 [0.43, 0.81]; p-value = 0.80). CONCLUSION: There is a high degree of concordance among radiologists for assignment of CC using MRI and CT.

Multiparametric Mapping Magnetic Resonance Imaging of Pancreatic Disease.

BACKGROUND: Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. PURPOSE: To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. MATERIALS AND METHODS: Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. RESULTS: T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). CONCLUSION: Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

A phase 1 study of veliparib, a PARP-1/2 inhibitor, with gemcitabine and radiotherapy in locally advanced pancreatic cancer.

BACKGROUND: Locally advanced pancreatic cancer (LAPC) has a dismal prognosis with current treatment modalities and one-third of patients die from local progression of disease. Preclinical studies with orthotopic PC demonstrated dramatic synergy between radiotherapy (RT) and the poly(ADP-ribose) polymerase-1/2 inhibitor (PARPi), veliparib. We conducted a phase I trial of gemcitabine, radiotherapy and dose-escalated veliparib in LAPC. METHODS: This was a single institution investigator-initiated open-label, single-arm phase 1 clinical trial (NCT01908478). Weekly gemcitabine with daily IMRT and veliparib dose escalated using a Bayesian adaptive design were administered in treatment naïve LA or borderline resectable PC. The primary end point was identification of the MTD. Secondary endpoints included efficacy, characterization of PAR levels using ELISA, DDR alterations with targeted next generation sequencing and transcriptome analysis, tumor mutation burden (TMB) and microsatellite instability (MSI) status. FINDINGS: Thirty patients were enrolled. The MTD of veliparib was 40 mg BID with gemcitabine 400 mg/m2 and RT (36 Gy/15 fractions). Sixteen DLTs were identified in 12 patients. Grade ≥ 3 adverse events included lymphopenia (96%) and anemia (36%). Median OS for all patients was 15 months. Median OS for DDR pathway gene altered and intact cases was 19 months (95% CI: 6.2-27.2) and 14 months (95% CI: 10.0-21.8), respectively. There were no significant associations between levels of PAR, TMB, or MSI with outcomes. The DDR transcripts PARP3 and RBX1 significantly correlated with OS. INTERPRETATION: This is the first report of a PARPi-chemoradiotherapy combination in PC. The regimen was safe, tolerable at the RP2D, and clinically active as an upfront treatment strategy in patients biologically unselected by upfront chemotherapy. Expression of the DDR transcripts, PARP3 and RBX1, were associated with OS suggesting validation in a follow up phase 2 study. FUND: Phase One Foundation; National Institutes of Health [1R01CA188480-01A1, P01 CA098912]. Veliparib was provided by Abbvie.

The role of pre-operative imaging and double balloon enteroscopy in the surgical management of small bowel neuroendocrine tumors: Is it necessary?

BACKGROUND AND OBJECTIVES: Pre-operative localization of small bowel neuroendocrine tumors (SBNET) is important for operative planning. The aim was to determine the effectiveness of pre-operative imaging and double-balloon enteroscopy (DBE) in identifying extent of disease. METHODS: Database review identified 85 patients with primary SBNET between 2006 and 2013. Analysis included patients who underwent imaging, endoscopy, and surgery at our institution. RESULTS: Average age was 60.7 years. Sixty-six (77.1%) patients had a primary NET in the ileum. Seventy-two patients (67.3%) underwent CT, 47 (46.7%) had MRI, 44 (46.7%) had somatostatin receptor imaging (SRI), and 41 (39.3%) underwent DBE. The sensitivity of each in identifying the NET was 59.7% for CT, 54% for MRI, 56% for SRI, and 88.1% for DBE. Eighteen (21.2%) patients had primary tumors not identified on imaging. Of these 18, 13 underwent DBE, and 12 of 13 (92.3%) DBEs identified the primary lesion. DBE was significantly better at identifying the primary NET than CT, MRI or SRI (P = 0.004, 0.007, and 0.012). CONCLUSIONS: Most SBNETs are identified with a combination of imaging modalities. In those with unidentified primary tumors after imaging, DBE should be considered as it may provide valuable information as to the location of the primary tumor.

Imaging of Neuroendocrine Tumors: Indications, Interpretations, Limits, and Pitfalls.

Imaging is critical in the diagnosis, prognosis, and management of neuroendocrine tumors (NETs). NETs share common imaging features, but each type exhibits unique features. Computed tomography scans or MRI of the abdomen is used to assess tumor burden routinely. Functional imaging with octreotide scan or gallium-68 somatostatin analog PET is used selectively to confirm diagnosis and guide therapy. Clinicians and radiologists should be familiar with the indications and interpretations of imaging modalities. Novel functional imaging modalities likely will be developed to detect small NETs, predict prognosis, guide therapeutic choices, and design novel therapies.