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Background Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. Methods Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. Results A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 (P < .001), whereas the hybrid model had an AUC of .785 (P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 (P = .096). Conclusions A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.

The aim of this study was to investigate outcomes associated with neoadjuvant chemotherapy in patients undergoing pancreatoduodenectomy for early stage pancreatic adenocarcinoma in the era of modern chemotherapy. The National Cancer Database (2010–2016) was queried for patients with clinical stage 0–2 pancreatic adenocarcinoma who underwent pancreatoduodenectomy. Patients who underwent up-front pancreatoduodenectomy were propensity matched to patients who received neoadjuvant chemotherapy. Postoperative outcomes, pathologic outcomes, and overall survival were compared. A total of 2036 patients were in each group. Neoadjuvant chemotherapy was associated with shorter length of stay, lower 30-day readmission rate, and lower 30 and 90-day mortality rates (all p < 0.05). Neoadjuvant chemotherapy was associated with lower rates of positives nodes and positive resection margins (all p < 0.0001). Neoadjuvant chemotherapy was associated with longer survival (26.8 vs. 22.1months, p < 0.0001). Patients who received neoadjuvant chemotherapy followed by surgery and adjuvant therapy had the longest OS, followed by neoadjuvant + surgery, surgery + adjuvant therapy, and surgery alone (29.8 vs. 25.6 vs. 23.9 vs. 13.1 months; p < 0.0001). Neoadjuvant chemotherapy is associated with improved postoperative outcomes, oncologic outcomes, and overall survival in patients with early stage pancreatic adenocarcinoma. Neoadjuvant chemotherapy should be considered in all patients with early stage pancreatic adenocarcinoma. •Rate of use of neoadjuvant therapy is increasing in early stage pancreatic adenocarcinoma.•Neoadjuvant chemotherapy is associated with improved postoperative and pathologic outcomes.•Neoadjuvant chemotherapy is associated with longer overall survival (27 vs 22 months).•One-third of patients who undergo up-front resection do not receive adjuvant chemotherapy.•Those who receive both pre- and postoperative chemotherapy have the longest survival (30 months).

IntroductionWhile minimally invasive left pancreatectomy has become more widespread and generally accepted over the last decade, opinions on modality of minimally invasive approach (robotic or laparoscopic) remain mixed with few institutions performing a significant portion of both operative approaches simultaneously.Methods247 minimally invasive left pancreatectomies were retrospectively identified in a prospectively maintained institutional REDCap™ database, 135 laparoscopic left pancreatectomy (LLP) and 108 robotic-assisted left pancreatectomy (RLP). Demographics, intraoperative variables, postoperative outcomes, and OR costs were compared between LLP and RLP with an additional subgroup analysis for procedures performed specifically for pancreatic adenocarcinoma (35 LLP and 23 RLP) focusing on pathologic outcomes and 2-year actuarial survival.ResultsThere were no significant differences in preoperative demographics or indications between LLP and RLP with 34% performed for chronic pancreatitis and 23% performed for pancreatic adenocarcinoma. While laparoscopic cases were faster (p < 0.001) robotic cases had a higher rate of splenic preservation (p < 0.001). Median length of stay was 5 days for RLP and LLP, and rate of clinically significant grade B/C pancreatic fistula was approximately 20% for both groups. Conversion rates to laparotomy were 4.3% and 1.8% for LLP and RLP approaches respectively. RLP had a higher rate of readmission (p = 0.035). Pathologic outcomes and 2-year actuarial survival were similar between LLP and RLP. LLP on average saved $206.67 in OR costs over RLP.ConclusionsThis study demonstrates that at a high-volume center with significant minimally invasive experience, both LLP and RLP can be equally effective when used at the discretion of the operating surgeon. We view the laparoscopic and robotic platforms as tools for the modern surgeon, and at our institution, given the technical success of both operative approaches, we will continue to encourage our surgeons to approach a difficult operation with their tool of choice.Graphical abstract

Background As Enhanced Recovery After Surgery (ERAS®) programs expand across numerous subspecialties, growth and sustainability on a system level becomes increasingly important and may benefit from reporting multidisciplinary and financial data. However, the literature on multidisciplinary outcome analysis in ERAS is sparse. This study aims to demonstrate the impact of multidisciplinary ERAS auditing in a hospital system. Additionally, we describe developing a financial metric for use in gaining support for system-wide ERAS adoption and sustainability. Methods Data from HPB, colorectal and urology ERAS programs at a single institution were analyzed from a prospective ERAS Interactive Audit System (EIAS) database from September 2015 to June 2019. Clinical 30-day outcomes for the ERAS cohort ( n  = 1374) were compared to the EIAS pre-ERAS control ( n  = 311). Association between improved ERAS compliance and improved outcomes were also assessed for the ERAS cohort. The potential multidisciplinary financial impact was estimated from hospital bed charges. Results Multidisciplinary auditing demonstrated a significant reduction in postoperative length of stay (LOS) (1.5 days, p  < 0.001) for ERAS patients in aggregate and improved ERAS compliance was associated with reduced LOS (coefficient − 0.04, p  = 0.004). Improved ERAS compliance in aggregate also significantly associated with improved 30-day survival (odds ratio 1.04, p  = 0.001). Multidisciplinary analysis also demonstrated a potential financial impact of 44% savings ( p  < 0.001) by reducing hospital bed charges across all specialties. Conclusions Multidisciplinary auditing of ERAS programs may improve ERAS program support and expansion. Analysis across subspecialties demonstrated associations between improved ERAS compliance and postoperative LOS as well as 30-day survival, and further suggested a substantial combined financial impact.

BackgroundCurrent evaluation methods for robotic-assisted surgery (ARCS or GEARS) are limited to 5-point Likert scales which are inherently time-consuming and require a degree of subjective scoring. In this study, we demonstrate a method to break down complex robotic surgical procedures using a combination of an objective cumulative sum (CUSUM) analysis and kinematics data obtained from the da Vinci® Surgical System to evaluate the performance of novice robotic surgeons.MethodsTwo HPB fellows performed 40 robotic-assisted hepaticojejunostomy reconstructions to model a portion of a Whipple procedure. Kinematics data from the da Vinci® system was recorded using the dV Logger® while CUSUM analyses were performed for each procedural step. Each kinematic variable was modeled using machine learning to reflect the fellows’ learning curves for each task. Statistically significant kinematics variables were then combined into a single formula to create the operative robotic index (ORI).ResultsThe inflection points of our overall CUSUM analysis showed improvement in technical performance beginning at trial 16. The derived ORI model showed a strong fit to our observed kinematics data (R2 = 0.796) with an ability to distinguish between novice and intermediate robotic performance with 89.3% overall accuracy.ConclusionsIn this study, we demonstrate a novel approach to objectively break down novice performance on the da Vinci® Surgical System. We identified kinematics variables associated with improved overall technical performance to create an objective ORI. This approach to robotic operative evaluation demonstrates a valuable method to break down complex surgical procedures in an objective, stepwise fashion. Continued research into objective methods of evaluation for robotic surgery will be invaluable for future training and clinical implementation of the robotic platform.

BackgroundMinimally invasive approach represents the gold standard for the resection of the left lateral section of the liver. Recently, the American Minimally Invasive Liver Resection (AMILES) registry has become available to track outcomes of laparoscopic and robotic liver resection in the Americas. The aim of the present study is to determine the benchmark performance of MILLS throughout the AMILES database.MethodsThe AMILES registry was interrogated for cases of minimally invasive left lateral sectionectomies (MILLS). Centers with best practices according to the achievement of textbook outcomes (TOs) were identified and were used to define benchmark performances.ResultsSeven institutions from US and Canada entered 1665 minimally invasive liver resections, encompassing 203 MILLS. Overall, 49% of cases of MILLS satisfied contemporarily all textbook outcomes. While all centers obtained TOs with different rates of success, the outcomes of the top-ranking centers were used for benchmarking. Benchmark performance metrics of MILLS across North America are: conversion rate ≤ 3.7%, blood loss ≤ 200 ml, OR time ≤ 199 min, transfusion rate ≤ 4.5%, complication rate ≤ 7.9%, LOS ≤ 4 days.ConclusionBenchmark performances of MILLS have been defined on a large multi-institutional database in North America. As more institutions join the collaboration and more prospective cases accrue, benchmark for additional procedures and approaches will be defined.

Background Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). However, downsides of these guidelines include unnecessary surgery and missed malignancy. The aim of this study was to use computed tomography (CT)-guided deep learning techniques to predict malignancy of PCNs. Materials and Methods Patients with PCNs who underwent resection were retrospectively reviewed. Axial images of the mucinous cystic neoplasms were collected and based on final pathology were assigned a binary outcome of advanced neoplasia or benign. Advanced neoplasia was defined as adenocarcinoma or intraductal papillary mucinous neoplasm with high-grade dysplasia. A convolutional neural network (CNN) deep learning model was trained on 66% of images, and this trained model was used to test 33% of images. Predictions from the deep learning model were compared to Fukuoka guidelines. Results Twenty-seven patients met the inclusion criteria, with 18 used for training and 9 for model testing. The trained deep learning model correctly predicted 3 of 3 malignant lesions and 5 of 6 benign lesions. Fukuoka guidelines correctly classified 2 of 3 malignant lesions as high risk and 4 of 6 benign lesions as worrisome. Following deep learning model predictions would have avoided 1 missed malignancy and 1 unnecessary operation. Discussion In this pilot study, a deep learning model correctly classified 8 of 9 PCNs and performed better than consensus guidelines. Deep learning can be used to predict malignancy of PCNs; however, further model improvements are necessary before clinical use.

Patient-reported outcomes (PROs) are essential for patient-centered health care. This pilot study implemented a mobile application customized to an hepatopancreatobiliary Enhanced Recovery After Surgery (ERAS®) program—a novel environment—for real-time collection of PROs, including ERAS® pathway compliance. Patients undergoing hepatectomy, distal pancreatectomy, or pancreaticoduodenectomy through the ERAS® program were prospectively enrolled over 10 months. The application provided education and questionnaires before surgery through 30 days postdischarge. Thresholds were set for initial adoption of the application (75%), PRO response rate (50%), and patient satisfaction (75%). Daily postdischarge health checks integrated customized responses to guide out-of-hospital care. Of 165 enrolled patients, 122 met inclusion criteria. Application adoption was 93 per cent (114/122) and in-hospital engagement remained high at 88 per cent (107/122). Patients completed 62 per cent of PRO on quality of life, postoperative pain, nausea, opioid consumption, and compliance to ERAS® pathway items, including ambulation and breathing exercises. During postcharge tracking, 12 patients reported that the application prevented a phone call to the hospital and three patients reported prevention of an emergency room visit. PRO collection through this mobile device created an integrated platform for comprehensive perioperative care, patient-initiated outcome tracking with automatic reporting, and real-time feedback for process change. Improving proactive outpatient management of complex patients through mobile technology could help restructure health-care delivery and improve resource utilization for all patients.

BackgroundThrombocytopenia is a common finding in patients with chronic liver disease. It is associated with poor clinical outcomes due to increased risk of bleeding after even minor procedures. We sought to determine an algorithm for pre-operative platelet transfusion in patients with cirrhosis and hepatocellular carcinoma (HCC) undergoing laparoscopic microwave ablation (MIS-MWA).MethodsA retrospective review identified all patients with cirrhosis and HCC who underwent MIS-MWA at a single tertiary institution between 2007 and 2019. Demographics, pre-operative and post-operative laboratory values, transfusion requirements, and bleeding events were collected. The analyzed outcome of bleeding risk included any transfusion received intra-operatively or a transfusion or surgical intervention post-operatively. Logistic regression models were created to predict bleeding risk and identify patients who would benefit from pre-operative transfusion.ResultsThere were 433 patients with cirrhosis and HCC who underwent MIS-MWA identified; of these, 353 patients had complete laboratory values and were included. Bleeding risk was evaluated through bivariate analysis of statistically and clinically significant variables. The accuracy of both models was substantiated through bootstrap validation for 500 iterations (model 1: ROC 0.8684, Brier score 0.0238; model 2: ROC 0.8363, Brier score 0.0252). The first model captured patients with both thrombocytopenia and anemia: platelet count < 60 × 109 / L (OR 7.75, p 0.012, CI 1.58–38.06) and hemoglobin < 10 gm/dL (OR 5.76, p 0.032, CI 1.16–28.63). The second model captured patients with thrombocytopenia without anemia: platelet count < 30 × 109/L (OR 8.41, p 0.05, CI 0.96–73.50) and hemoglobin > 10 gm/dL (OR 0.16, p 0.026, CI 0.031–0.80).ConclusionThe prediction of patients with cirrhosis and HCC requiring pre-operative platelet transfusions may help to avoid bleeding complications after invasive procedures. This study needs to be prospectively validated and ultimately may be beneficial in assessment of novel therapies for platelet-based clinical treatment in liver disease.