Long-Duration Neoadjuvant Therapy with FOLFIRINOX Yields Favorable Outcomes for Patients Who Undergo Surgery for Pancreatic Cancer.

BACKGROUND: In 2023 alone, it's estimated that over 64,000 patients will be diagnosed with PDAC and more than 50,000 patients will die of the disease. Current guidelines recommend neoadjuvant therapy for patients with borderline resectable and locally advanced PDAC, and data is emerging on its role in resectable disease. Neoadjuvant chemotherapy may increase the number of patients able to receive complete chemotherapy regimens, increase the rate of microscopically tumor-free resection (R0) margin, and aide in identifying unfavorable tumor biology. To date, this is the largest study to examine surgical outcomes after long-duration neoadjuvant chemotherapy for PDAC. METHODS: Retrospective analysis of single-institution data. RESULTS: The routine use of long-duration therapy in our study (median cycles: FOLFIRINOX = 10; gemcitabine-based = 7) is unique. The majority (85%) of patients received FOLFIRINOX without radiation therapy; the R0 resection rate was 76%. Median OS was 41 months and did not differ significantly among patients with resectable, borderline-resectable, or locally advanced disease. CONCLUSIONS: This study demonstrates that in patients who undergo surgical resection after receipt of long-duration neoadjuvant FOLFIRINOX therapy alone, survival outcomes are similar regardless of pretreatment resectability status and that favorable surgical outcomes can be attained.

A Novel Machine Learning Approach to Predict Textbook Outcome in Colectomy.

BACKGROUND: Several calculators exist to predict risk of postoperative complications. However, in low-risk procedures such as colectomy, a tool to determine the probability of achieving the ideal outcome could better aid clinical decision-making, especially for high-risk patients. A textbook outcome is a composite measure that serves as a surrogate for the ideal surgical outcome. OBJECTIVE: To identify the most important factors for predicting textbook outcomes in patients with nonmetastatic colon cancer undergoing colectomy and to create a textbook outcome decision support tool using machine learning algorithms. DESIGN: This was a retrospective analysis study. SETTINGS: Data were collected from the American College of Surgeons National Surgical Quality Improvement Program database. PATIENTS: Adult patients undergoing elective colectomy for nonmetastatic colon cancer (2014-2020) were included. MAIN OUTCOME MEASURES: Textbook outcome was the main outcome, defined as no mortality, no 30-day readmission, no postoperative complications, no 30-day reinterventions, and a hospital length of stay of ≤5 days. Four models (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting) were trained and validated. Ultimately, a web-based calculator was developed as proof of concept for clinical application. RESULTS: A total of 20,498 patients who underwent colectomy for nonmetastatic colon cancer were included. Overall, textbook outcome was achieved in 66% of patients. Textbook outcome was more frequently achieved after robotic colectomy (77%), followed by laparoscopic colectomy (68%) and open colectomy (39%, p < 0.001). eXtreme Gradient Boosting was the best performing model (area under the curve = 0.72). The top 5 preoperative variables to predict textbook outcome were surgical approach, patient age, preoperative hematocrit, preoperative oral antibiotic bowel preparation, and patient sex. LIMITATIONS: This study was limited by its retrospective nature of the analysis. CONCLUSIONS: Using textbook outcome as the preferred outcome may be a useful tool in relatively low-risk procedures such as colectomy, and the proposed web-based calculator may aid surgeons in preoperative evaluation and counseling, especially for high-risk patients. See Video Abstract . UN NUEVO ENFOQUE DE APRENDIZAJE AUTOMTICO PARA PREDECIR EL RESULTADO DE LOS LIBROS DE TEXTO EN COLECTOMA: ANTECEDENTES:Existen varias calculadoras para predecir el riesgo de complicaciones posoperatorias. Sin embargo, en procedimientos de bajo riesgo como la colectomía, una herramienta para determinar la probabilidad de lograr el resultado ideal podría ayudar mejor a la toma de decisiones clínicas, especialmente para pacientes de alto riesgo. Un resultado de libro de texto es una medida compuesta que sirve como sustituto del resultado quirúrgico ideal.OBJETIVO:Identificar los factores más importantes para predecir el resultado de los libros de texto en pacientes con cáncer de colon no metastásico sometidos a colectomía y crear una herramienta de apoyo a la toma de decisiones sobre los resultados de los libros de texto utilizando algoritmos de aprendizaje automático.DISEÑO:Este fue un estudio de análisis retrospectivo.AJUSTES:Los datos se obtuvieron de la base de datos del Programa Nacional de Mejora de la Calidad del Colegio Americano de Cirujanos.PACIENTES:Se incluyeron pacientes adultos sometidos a colectomía electiva por cáncer de colon no metastásico (2014-2020).MEDIDAS PRINCIPALES DE RESULTADO:El resultado de los libros de texto fue el resultado principal, definido como ausencia de mortalidad, reingreso a los 30 días, complicaciones posoperatorias, reintervenciones a los 30 días y una estancia hospitalaria ≤5 días. Se entrenaron y validaron cuatro modelos (regresión logística, árbol de decisión, bosque aleatorio y XGBoost). Finalmente, se desarrolló una calculadora basada en la web como prueba de concepto para su aplicación clínica.RESULTADOS:Se incluyeron un total de 20.498 pacientes sometidos a colectomía por cáncer de colon no metastásico. En general, el resultado de los libros de texto se logró en el 66% de los pacientes. Los resultados de los libros de texto se lograron con mayor frecuencia después de la colectomía robótica (77%), seguida de la colectomía laparoscópica (68%) y la colectomía abierta (39%) (p<0,001). XGBoost fue el modelo con mejor rendimiento (AUC=0,72). Los cinco principales variables preoperatorias para predecir el resultado en los libros de texto fueron el abordaje quirúrgico, la edad del paciente, el hematocrito preoperatorio, la preparación intestinal con antibióticos orales preoperatorios y el sexo femenino.LIMITACIONES:Este estudio estuvo limitado por la naturaleza retrospectiva del análisis.CONCLUSIONES:El uso de los resultados de los libros de texto como resultado preferido puede ser una herramienta útil en procedimientos de riesgo relativamente bajo, como la colectomía, y la calculadora basada en la web propuesta puede ayudar a los cirujanos en la evaluación y el asesoramiento preoperatorios, especialmente para pacientes de alto riesgo. (Traducción-Yesenia Rojas-Khalil ).

A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.

INTRODUCTION: Peritoneal carcinomatosis is considered a late-stage manifestation of neoplastic diseases. Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can be an effective treatment for these patients. However, the procedure is associated with significant morbidity. Our aim was to develop a machine learning model to predict the probability of achieving textbook outcome (TO) after CRS-HIPEC using only preoperatively known variables. METHODS: Adult patients with peritoneal carcinomatosis and who underwent CRS-HIPEC were included from a large, single-center, prospectively maintained dataset (2001-2020). TO was defined as a hospital length of stay ≤14 days and no postoperative adverse events including any complications, reoperation, readmission, and mortality within 90 days. Four models (logistic regression, neural network, random forest, and XGBoost) were trained, validated, and a user-friendly risk calculator was then developed. RESULTS: A total of 1954 CRS-HIPEC procedures for peritoneal carcinomatosis were included. Overall, 13% (n = 258) achieved TO following CRS-HIPEC procedure. XGBoost and logistic regression had the highest area under the curve (AUC) (0.76) after model optimization, followed by random forest (AUC 0.75) and neural network (AUC 0.74). The top preoperative variables associated with achieving a TO were lower peritoneal cancer index scores, not undergoing proctectomy, splenectomy, or partial colectomy and being asymptomatic from peritoneal metastases prior to surgery. CONCLUSION: This is a data-driven study to predict the probability of achieving TO after CRS-HIPEC. The proposed pipeline has the potential to not only identify patients for whom surgery is not associated with prohibitive risk, but also aid surgeons in communicating this risk to patients.

Development and Validation of Prediction Models and Risk Calculators for Posthepatectomy Liver Failure and Postoperative Complications Using a Diverse International Cohort of Major Hepatectomies.

OBJECTIVE: The study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables. BACKGROUND: PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function. METHODS: The cohort included adult patients who underwent major hepatectomies at 12 international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation data set. RESULTS: Among 2192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build 2 risk calculators with the option to include or exclude intraoperative variables ( PHLF Risk Calculator; CCI>40 Risk Calculator ). CONCLUSIONS: Using an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI>40 with good discrimination and calibration.

A Machine Learning Approach to Predict Postoperative Pancreatic Fistula After Pancreaticoduodenectomy Using Only Preoperatively Known Data.

BACKGROUND: Clinically-relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD) is a major postoperative complication and the primary determinant of surgical outcomes. However, the majority of current risk calculators utilize intraoperative and postoperative variables, limiting their utility in the preoperative setting. Therefore, we aimed to develop a user-friendly risk calculator to predict CR-POPF following PD using state-of-the-art machine learning (ML) algorithms and only preoperatively known variables. METHODS: Adult patients undergoing elective PD for non-metastatic pancreatic cancer were identified from the ACS-NSQIP targeted pancreatectomy dataset (2014-2019). The primary endpoint was development of CR-POPF (grade B or C). Secondary endpoints included discharge to facility, 30-day mortality, and a composite of overall and significant complications. Four models (logistic regression, neural network, random forest, and XGBoost) were trained, validated and a user-friendly risk calculator was then developed. RESULTS: Of the 8666 patients who underwent elective PD, 13% (n = 1160) developed CR-POPF. XGBoost was the best performing model (AUC = 0.72), and the top five preoperative variables associated with CR-POPF were non-adenocarcinoma histology, lack of neoadjuvant chemotherapy, pancreatic duct size less than 3 mm, higher BMI, and higher preoperative serum creatinine. Model performance for 30-day mortality, discharge to a facility, and overall and significant complications ranged from AUC 0.62-0.78. CONCLUSIONS: In this study, we developed and validated an ML model using only preoperatively known variables to predict CR-POPF following PD. The risk calculator can be used in the preoperative setting to inform clinical decision-making and patient counseling.

National Practice Patterns in Malignant Peritoneal Mesothelioma: Updates in Management and Survival.

BACKGROUND: Malignant peritoneal mesothelioma (MPM) is a rare malignancy with a historically poor prognosis. Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) has emerged as an effective therapy for patients with peritoneal malignancies. A contemporary analysis of trends in management of and survival from MPM is warranted. METHODS: Patients with MPM were identified from the National Cancer Database (2004-2018). Patients were categorized by treatment (CRS-HIPEC, CRS-chemotherapy, CRS only, chemotherapy only, no treatment), and joinpoint regression was employed to compute the annual percent change (APC) in treatment over time. Multivariable Cox proportional hazards models were used to analyze factors associated with survival. RESULTS: Of 2683 patients with MPM, 19.1% underwent CRS-HIPEC, and 21.1% received no treatment. Joinpoint regression revealed a statistically significant increase in the proportion of patients undergoing CRS-HIPEC over time (APC 3.21, p = 0.01), and a concurrent decrease in the proportion of patients who underwent no treatment (APC - 2.21, p = 0.02). Median overall survival was 19.5 months. Factors independently associated with survival included CRS-HIPEC, CRS, histology, sex, age, race, Charlson Comorbidity Index, insurance, and hospital type. Although there was a strong association between year of diagnosis and survival on univariate analysis (2016-2018 HR 0.67, p < 0.001), this association was attenuated after adjustment for treatment. CONCLUSIONS: CRS-HIPEC is increasingly employed as a treatment for MPM. In parallel, there has been a decrease in patients receiving no treatment with an increase in overall survival. These findings suggest that patients with MPM may be receiving more appropriate therapy; however, a substantial proportion of patients may remain undertreated.

Hand-Assisted Versus Pure Minimally-Invasive Distal Pancreatectomy: Is There a Downside to Lending a Hand?

BACKGROUND: Hand-assisted laparoscopic distal pancreatectomy (HALDP) is suggested to offer similar outcomes to pure laparoscopic distal pancreatectomy (LDP). However, given the longer midline incision, it is unclear whether HALDP increases the risk of postoperative hernia. Our aim was to determine the risk of postoperative incisional hernia development after HALDP. METHODS: We retrospectively collected data from patients undergoing HALDP or LDP at a single center (2012-2020). Primary endpoints were postoperative incisional hernia and operative time. All patients had at minimum six months of follow-up. Outcomes were compared using unadjusted and multivariable regression analyses. RESULTS: Ninety-five patients who underwent laparoscopic distal pancreatectomy were retrospectively identified. Forty-one patients (43%) underwent HALDP. Patients with HALDP were older (median, 67 vs. 61 years, p = 0.02). Sex, race, Body Mass Index (median, 27 vs. 26), receipt of neoadjuvant chemotherapy, gland texture, wound infection rates, postoperative pancreatic fistula, overall complications, and hospital length-of-stay were similar between HALDP and LDP (all p > 0.05). In unadjusted analysis, operative times were shorter for HALDP (164 vs. 276 min, p < 0.001), but after adjustment, did not differ significantly (MR 0.73; 0.49-1.07, p = 0.1). Unadjusted incidence of hernia was higher in HALDP versus LDP (60% vs. 24%, p = 0.004). After adjustment, HALDP was associated with an increased odds of developing hernia (OR 7.52; 95% CI 1.54-36.8, p = 0.014). After propensity score matching, odds of hernia development remained higher for HALDP (OR 4.62; 95% CI 1.28-16.65, p = 0.031) p = 0.03). CONCLUSIONS: Compared with LDP, HALDP was associated with increased likelihood of postoperative hernia with insufficient evidence that HALDP shortens operative times. Our results suggest that HALDP may not be equivalent to LDP.

Sofosbuvir plus glecaprevir/pibrentasvir as salvage therapy after liver transplantation in NS5A inhibitor-experienced patients. A case series.

BACKGROUND: Treatment of chronic hepatitis C virus (HCV) infection with direct-acting antivirals achieves a sustained virologic response rate higher than 95%. However, virologic failure remains a clinical challenge, and data on retreatment are limited, especially in special populations such as liver transplant (LT) recipients. OBJECTIVES: This study evaluated the sofosbuvir plus glecaprevir-pibrentasvir (GLE/PIB) regimen in LT recipients who had failed to a nonstructural protein 5A (NS5A) inhibitor-based regimen. MATERIAL AND METHODS: Retrospective study of 111 liver transplant recipients between January 2018 and December 2020; 18 patients presented with HCV recurrent infection after LT, out of whom three had a history of at least one NS5A inhibitor-based regimen. Salvage therapy with sofosbuvir plus GLE/PIB was started for 12 weeks; baseline characteristics and outcomes were recorded. RESULTS: All three patients (100%) achieved an undetectable HCV viral load 12 weeks after treatment completion. No serious adverse events were observed. CONCLUSION: In our series, sofosbuvir plus GLE/PIB for 12 weeks is an effective and safe salvage therapy after LT in patients previously treated with NS5A inhibitors.

ANTECEDENTES: El tratamiento del virus de la hepatitis C (VHC) crónica con antivirales de acción directa logra tasas de respuesta virológica sostenida superiores a 95 %. Sin embargo, el manejo del fracaso virológico sigue siendo un desafío clínico y la evidencia sobre el retratamiento es limitada, especialmente en poblaciones como los receptores de trasplante hepático (TH). OBJETIVO: Este estudio evaluó el régimen de sofosbuvir más glecaprevir/pibrentasvir (GLE/PIB) en receptores de TH en quienes falló el régimen basado en inhibidores de la proteína no estructural 5A (NS5A). MATERIAL Y MÉTODOS: Estudio retrospectivo de 111 pacientes trasplantados entre enero de 2018 y diciembre de 2020; 18 pacientes presentaron infección recurrente por VHC posterior al TH, tres de ellos tuvieron antecedentes de al menos un régimen basado en inhibidores de NS5A. Se inició terapia de rescate con sofosbuvir más GLE/PIB durante 12 semanas posterior al TH; se registraron las características basales de los pacientes y sus desenlaces. RESULTADOS: En los tres pacientes se logró obtener una carga viral indetectable de VHC a las 12 semanas de finalizar el tratamiento. No se observaron eventos adversos graves. CONCLUSIÓN: En nuestra serie, sofosbuvir más GLE/PIB durante 12 semanas demostró ser una terapia de rescate efectiva y segura posterior al TH en pacientes previamente tratados con inhibidores de NS5A.

AHPBA graduated fellows' assessments of strengths, weaknesses, opportunities and threats facing fellowship training in HPB surgery: "HPB training excellence means volume, knowledge, and mentorship".

BACKGROUND: Many fellowship programs in North America prepare surgeons for a career in Hepato-Pancreato-Biliary (HPB) surgery. Recent fellowship graduates were surveyed as part of a strengths, weaknesses, opportunities, and threats (SWOT) analysis commissioned by Americas Hepato-Pancreato-Biliary Association (AHPBA). METHODS: This was a cross-sectional study surveying AHPBA-certified fellowship graduates conducted August-December 2021. Survey data were analyzed using descriptive statistics. Free-text answers were analyzed using both grounded theory principles and thematic network analyses. RESULTS: Four main themes were identified: (i) concerns regarding the lack of standardization between HPB fellowship curricula (ii) concern for job market oversaturation, (iii) need to emphasize the value in HPB fellowship training and (iv) importance of diversity, inclusion, and equity in HPB training. DISCUSSION: Based on themes identified, the strengths of AHPBA-certified HPB programs include superior case volume and technical training. Areas of weakness and growth opportunities include standardizing training experiences. According to AHPBA-certificate awardees, optimizing future HPB fellowships would include strong sponsorship for job placement after graduation, and more intentional investments in diversity, equity, and inclusion.

AHPBA senior leaders' assessments of strengths, weaknesses, opportunities, and threats facing fellowship training in HPB surgery: "We need to standardize our training experiences".

BACKGROUND: Multiple fellowship programs in North America prepare surgeons for a career in Hepato-Pancreatico-Biliary (HPB) surgery. Inconsistent operative experiences and disease process exposures across programs and pathways produces variability in training product and therefore, lack of clarity around what trained HPB surgeons are prepared to do in early practice. Thus, a strengths, weaknesses, opportunities, and threats (SWOT) analysis of AHPBA fellowship training was conducted. METHODS: This was a mixed-methods, cross-sectional study. Eleven AHPBA-Founding Members (FM) and 24 current or former Program Directors (PD) of programs eligible for AHPBA certificates were surveyed and interviewed. Grounded theory principles and thematic network analysis were used to analyze interview transcripts. Descriptive statistics were used to analyze survey data. RESULTS: Three main themes were identified: (i) Concern for training rigor and consistency (ii) Desire to standardize curricula and broaden training requirements and, (iii) Need to validate both the value of training and job marketability via certification. DISCUSSION: Based on the themes identified, the strengths of AHPBA-certified HPB programs include superior technical training and case volumes. Areas of improvement included elevating baseline competencies by increasing required case volume and breadth to ensure minimally invasive experience, operative autonomy, and multidisciplinary care coordination.

The job market for HPB surgeons: leadership perspectives on surgeon skillsets, training pathways, and hiring.

BACKGROUND: Three tracks prepare Hepato-Pancreato-Biliary (HPB) surgeons: HPB, surgical oncology, and transplant fellowships. This study explored how surgical leaders thought about HPB surgery and evaluated potential candidates for HPB positions. METHODS: This descriptive qualitative study utilized interviews of healthcare leaders whose responsibilities included hiring HPB surgeons. We coded inductively then used thematic network analysis to organize the data. Individual codes formed basic themes, then larger secondary themes, then finally "primary" themes. RESULTS: Primary themes were: (1) What defines an HPB surgical practice?, (2) How do they assess candidates for HPB positions?, and (3) How will HPB practices continue to evolve? Leaders assessed applicants' training, behaviors and cultural fit, technical excellence, and more. Personal recommendations and professional networks significantly influenced the hiring process. HPB surgery needs were growing due to population changes, treatments advances, and changing market conditions. DISCUSSION: Surgical societies should focus on facilitating networking, promoting transparency, sharing quality data, providing evidence of technical skills and teamwork, mentorship, and providing guidance to general surgery residency program directors. There is great interest in unification and cooperation across the profession, protocol standardization enhancing quality, continued workforce diversification, and evaluation of the alignment between training and practice.

Autoimmune comorbidity in achalasia patients.

BACKGROUND AND AIM: Idiopathic achalasia is a rare esophageal motor disorder. The disease state manifests local and systemic inflammation, and it appears that an autoimmune component and specific autoantibodies participate in the pathogenesis. The study aims to determine the prevalence of autoimmune and chronic inflammatory diseases in patients with achalasia and compare the results with those from patients with gastroesophageal reflux disease (GERD). METHODS: It was a cross-sectional and included 114 patients with idiopathic achalasia and 114 age-matched and sex-matched control patients with GERD. Data on the presence of autoimmune and inflammatory diseases, the time of presentation, and any family history of autoimmune disease were obtained from the hospital's medical records. RESULTS: Seventy three (64%) were female patients (mean age: 42.3 ± 15.5; median disease duration: 12 months). We identified the presence of autoimmune disease in 19 patients with achalasia (16.7%), hypothyroidism was the main diagnosis, and it was present in 52.6% of patients compared with 4.2% in controls. Thirteen of the 19 achalasia patients (68.4%) with autoimmune disease had history of familial autoimmunity. We identified 11 achalasia (9.6%) and 5 GERD patients (4.16%) with an inflammatory condition. Compared with the GERD, the achalasia group was 3.8 times more likely to have an autoimmune disease (95% CI: 1.47-9.83), 3.0 times more likely to have thyroidopathies (95% CI: 1.00-9.03), and 3.02 times more likely to suffer from any chronic inflammatory disease (95% CI: 1.65-6.20). CONCLUSIONS: The non-negligible number of patients with autoimmune diseases identified among the patients with idiopathic achalasia supports the hypothesis that achalasia has an autoimmune component.

Robotic-assisted endoluminal gastric leiomyoma resection: a novel surgical technique for benign gastroesophageal junction tumors.

Gastric leiomyomas are rare, benign smooth muscle tumors that arise from the muscularis propria and can be found in any part of the stomach. The American College of Gastroenterologists recommends resection only for symptomatic leiomyomas, which can often present with bleeding, abdominal pain, or dyspepsia. Notably, symptomatic leiomyomas that arise at the gastroesophageal (GE) junction, especially those that are large, pose unique challenges. Specifically, total gastrectomy with esophagojejunostomy is often necessary, which can be associated with a compromised quality of life and possible complications such as anastomotic stricture or reflux esophagitis. In this context, we present the case of a young, male patient with a large symptomatic leiomyoma at the GE junction who was offered a robotic-assisted endoluminal leiomyoma resection. By placing endoluminal trocars and utilizing the Da Vinci® robot, we were able to carefully excise the tumor without perforating the stomach or causing GE junction stenosis. This allowed the patient to preserve his stomach and avoid a high-risk anastomosis. Another notable highlight of the case included the use of the endoscope as both a bougie and a source of insufflation. The patient had an uncomplicated postoperative course and a rapid recovery, highlighting the feasibility of this approach for patients with benign GE junction tumors.

National practice patterns in the use of endoscopic ultrasound biopsy for resectable Pancreatic Neuroendocrine Tumors: Insights into the role of DOTATATE PET/CT in diagnosis.

INTRODUCTION: Pancreatic neuroendocrine tumors (PNETs) are typically diagnosed using endoscopic ultrasound-guided (EUS) biopsy, which can be associated with complications. Since 2016, DOTATATE PET/CT has emerged as an effective tool to localize and stage PNETs. METHODS: Patients with PNETs who underwent R0 resections were identified from the 2004-2019 National Cancer Database PUF. Joinpoint regression and multivariable logistic regression were used to analyze trends in the use of biopsy. RESULTS: Of 16,746 R0 resected PNET patients, 44 â€‹% underwent diagnostic biopsy. Joinpoint regression showed a significant increase in the use of biopsy from 2004 to 2019 (APC 1.80, p â€‹< â€‹0.001). A higher percentage of patients diagnosed after DOTATATE approval underwent biopsy compared to those diagnosed before (48 â€‹% vs. 42 â€‹%, p â€‹< â€‹0.001). Adjusted analysis showed diagnosis after 2016 was associated with increased odds of biopsy (OR â€‹= â€‹1.67, p â€‹< â€‹0.001). CONCLUSIONS: Despite technologic advancement with DOTATATE PET/CT, there has been a significant increase in the proportion of resectable PNETs undergoing preoperative biopsy.

Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery. METHODS: A systematic search of Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar was performed through December 2022. The primary outcome was the discriminatory performance of ML vs LR models as measured by the area under the receiver operating characteristic curve (AUC). A meta-analysis was then performed using a random effects model. RESULTS: A total of 62 LR models and 143 ML models were included across 38 studies. On average, the best-performing ML models had a significantly higher AUC than the LR models (ΔAUC, 0.07; 95% CI, 0.04-0.09; P < .001). Similarly, on average, the best-performing ML models had a significantly higher logit (AUC) than the LR models (Δlogit [AUC], 0.41; 95% CI, 0.23-0.58; P < .001). Approximately half of studies (44%) were found to have a low risk of bias. Upon a subset analysis of only low-risk studies, the difference in logit (AUC) remained significant (ML vs LR, Δlogit [AUC], 0.40; 95% CI, 0.14-0.66; P = .009). CONCLUSION: We found a significant improvement in discriminatory ability when using ML over LR algorithms in predicting postoperative outcomes for patients undergoing GI surgery. Subsequent efforts should establish standardized protocols for both developing and reporting studies using ML models and explore the practical implementation of these models.