Gastric cancer in Sub-Saharan Africa - a systematic review of primary data.

Introduction: Gastric cancer (GC) is the third leading cause of global cancer-related mortality. Despite the shifting burden of GC to low-and middle-income countries, the data regarding incidence, treatment, and outcomes in these settings are sparse. The primary aim of this systematic review was to aggregate all available data on GC in sub-Saharan Africa (SSA) to describe the variability in incidence across the region. Methods: Studies reporting population-based primary data on GC in SSA were considered. The inclusion was limited to primary studies published between January 1995 and March 2022 which comprised of adult patients in SSA with GC. Studies without accessible full text in either French or English language were excluded. Unadjusted GC incidence rates with their standard errors for each study were recalculated from the crude numerators and denominators provided in individual studies. Results: A total of 5,626 articles were identified in the initial search, of which, 69 studies were retained. Reported incidence rates ranged from a high of 5.56 GC cases per 100,000 in Greater Meru Kenya to a low of 0.04 GC cases per 100,000 people in Benin City Nigeria. The overall crude pooled incidence was 1.20 GC cases per 100, 000 (95%CI 1.15-1.26) with a variability of 99.83% (I2 p < 0.001). From the 29 high-quality population-based registry studies the crude pooled incidence was 1.71 GC cases per 100,000 people (95%CI 1.56-21.88) with a variability of 99.60%. Conclusion: This systemic review demonstrates that GC incidence is highly variable across SSA. The limited data on GC treatment, mortality, and survival presents a significant challenge to providing a complete epidemiologic description of the burden of GC in SSA. There is a need for further robust data collection, exploration, and research studies on cancer care in SSA, with continued assessment of primary data availability.

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 ).

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.

Identifying Good Candidates for Active Surveillance of Ductal Carcinoma In Situ: Insights from a Large Neoadjuvant Endocrine Therapy Cohort.

Ductal carcinoma in situ (DCIS) is a biologically heterogenous entity with uncertain risk for invasive ductal carcinoma (IDC) development. Standard treatment is surgical resection often followed by radiation. New approaches are needed to reduce overtreatment. This was an observational study that enrolled patients with DCIS who chose not to pursue surgical resection from 2002 to 2019 at a single academic medical center. All patients underwent breast MRI exams at 3- to 6-month intervals. Patients with hormone receptor-positive disease received endocrine therapy. Surgical resection was strongly recommended if clinical or radiographic evidence of disease progression developed. A recursive partitioning (R-PART) algorithm incorporating breast MRI features and endocrine responsiveness was used retrospectively to stratify risk of IDC. A total of 71 patients were enrolled, 2 with bilateral DCIS (73 lesions). A total of 34 (46.6%) were premenopausal, 68 (93.2%) were hormone-receptor positive, and 60 (82.1%) were intermediate- or high-grade lesions. Mean follow-up time was 8.5 years. Over half (52.1%) remained on active surveillance without evidence of IDC with mean duration of 7.4 years. Twenty patients developed IDC, of which 6 were HER2 positive. DCIS and subsequent IDC had highly concordant tumor biology. Risk of IDC was characterized by MRI features after 6 months of endocrine therapy exposure; low-, intermediate-, and high-risk groups were identified with respective IDC rates of 8.7%, 20.0%, and 68.2%. Thus, active surveillance consisting of neoadjuvant endocrine therapy and serial breast MRI may be an effective tool to risk-stratify patients with DCIS and optimally select medical or surgical management. Significance: A retrospective analysis of 71 patients with DCIS who did not undergo upfront surgery demonstrated that breast MRI features after short-term exposure to endocrine therapy identify those at high (68.2%), intermediate (20.0%), and low risk (8.7%) of IDC. With 7.4 years mean follow-up, 52.1% of patients remain on active surveillance. A period of active surveillance offers the opportunity to risk-stratify DCIS lesions and guide decisions for operative management.